Input: Claim: Every technological innovation is inherently beneficial to society. [SEP] Text: Similarly, a social media user of today might develop into a more informed and socially connected citizen or professional, but might also end up as a social outcast, or even be affected by mental illness. Companies know this phenomenon quite well and marketing effort often lever on the effect. 27. In the case of GMO crops and food, European markets have long resisted adoption out of distrust toward producers. Even if, in the end, they will end up accepting this technology, this delayed adoption might have had significant financial impact. 28. There are cases in which product liability can raise complex issues even for technology that is not particularly innovative. For instance, a pharmaceutical company might decide to withdraw a socially desirable product, such as a drug, if its profitability is not high enough to offset the potential liability that might emerge, because of rare but highly damaging consequences on particular customers. 29. Dramatic and well-known examples of such phenomena are tobacco smoke and asbestos, which have led to countless casualties and decades of debate between the first detection of the problem, legislative action restricting their usage and corporate lobbying trying to resist. 30. In order to cope with this tradeoff, the EU has introduced a rule termed ‘Clause’. states that “The producer shall not be liable […] if he proves that the state of scientific and technical knowledge at the time when he put the product into circulation was not such as to enable the existence of the defect to be discovered” (Directive 85/374/EEC). However, this solution is not without problems. Besides leaving consumers with residual risk, firms are effectively taken away incentives to work on product safety, since ignorance on side effects shields them from liability. This leaves the advancement of the state of the art on potential dangers of new technology to public authorities alone, who might not be able to develop or access enough knowledge on the subject. 1. Stanford University, Department 10. 1007/978-1-4471-7531-5_1Download citation. RIS. ENW. : 05 April 2023 Publisher Name: : 978-1-4471-7530-8 Online ISBN: 978-1-4471-7531-5eBook Packages: EngineeringEngineering (R0)Share this 34. 9910 units per month Download 29. 95 Price excludes VAT (USA) Available as PDF Read on any device Instant download Own it forever Buy 89.

Output: contrast


Input: Claim: Quantum algorithms can solve certain problems faster than classical ones. [SEP] Text: Simulations were created via a custom python script: genome_mis-asmbl_sim. py (available in the Struo2 GitHub repository). Three types of misassemblies were simulated: breakpoints (splitting a contig into 2 pieces), rearrangements (relocating a genomic fragment within one contig to another location on the same contig or other contig in the assembly), and chimerisms (relocating a genomic fragment from one contig in a donor genome to a contig in a recipient genome). Breakpoint locations were selected from a uniform distribution. Fragment sizes of rearrangements and chimerisms were selected from a uniform distribution, with size a range of 1e3-1e4. We used CheckM to assess assembly quality of each genome in each synthetic genome dataset (Parks et al. , 2015). Each resultant synthetic genome dataset was used to construct reference metagenome profiling databases via Struo2. We utilized the of (CAMI) “HMP Gut” metagenome dataset to assess how genome assembly quality affects Kraken2/Bracken taxonomic assignments (Sczyrba et al. , 2017). The dataset was downloaded from the CAMI challenge website: paired-end reads were subsampled to 1 million per sample. Beta diversity was calculated from the Bracken output via QIIME2 (Bokulich et al. , 2018). Beta diversity distance matrices were compared via Mantel tests with 999 permutations and the Pearson correlation method. Struo2 databases from GTDB releases 95 and 202 The genomes selected were as reported for the benchmarking of Struo and Struo2. The custom Kraken2, Bracken, genes, and HUMAnN3 databases, are available at: We will publish new versions of each database as new releases of the GTDB are published. Utility tools We have generated a set of utility tools for aiding in the construction of input for Struo2 and generally facilitating the integration of the GTDB taxonomy into existing bioinformatics pipelines. Some of these tools are described elsewhere in the Supplement Methods. We note 2 utility tools that can have a broad applicability: gtdb_to_taxdump. py and ncbi-gtdb_map. py.

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: (D–E) Using a laser microdissection (LMD) approach, it is possible to select non-colonized cells (D) and colonized cells (E) from root sections, and to collect them separately, thus avoiding the dilution effect caused by the heterogeneous situation of a mycorrhizal root. 4. 1. Proteomics on the early stages of the mycorrhizal symbiosisThe studies on the early stages of the symbiosis coincide with the earlier studies in symbiotic proteomics. Burgess and collaborators set up a complex experiment to identify proteins either induced, enhanced or inhibited during the early stages of ECM development [31]. They compared, over a time-course, the profiles of proteins expressed in roots inoculated with three different isolates of Pisolithus tinctorius showing different degrees of root colonization: isolate H2144 exhibited a very high infectivity, isolate 441 showed moderate infectivity, while isolate H506 was not able to induce ECM [31]. They used two-dimensional electrophoresis (2DE)-PAGE, a gel electrophoresis technique introduced in the middle 1970s by O'Farrell and Klose and able to separate with high-resolution proteins by two orthogonal properties: iso-electrical point and molecular weight [32]. With this approach, Burgess et al. (1995) found that the morphological changes observed in the inoculated plant roots were linked with massive changes in protein composition, and claimed that these changes commenced at the time of contact between the two partners [31]. It was later discovered [33,34] that these morphological changes during the establishment of ECM were not caused by the contact between plant and fungus, because molecular signals released in the rhizosphere were sufficient to trigger them. The work by Burgess et al. (1995) was nevertheless important because it revealed some plant and fungal symbiosis-related polypeptides and demonstrated that their upregulation was tightly correlated with fungal infectivity. Five years later, another paper on the early stage of the ECM symbiosis was published by Laurent et al. (1999). They used the same methodological approach, 2DE-PAGE separation, but they focused their attention on the cell wall polypeptides, in order to identify cell surface proteins involved in ECM symbiosis development.

Output: corroborate


Input: Claim: Social media can ensure complete transparency in government. [SEP] Text: Physicians and patients should be able to discern how a coverage policy was decided. Transparency in coverage policies should be conceived as a core value, an essential principle to help patients and physicians make informed choices and enable health insurance markets to function efficiently. Transparency helps ensure accountability and encourages rigor in drug coverage policy development. Lacking transparency, coverage policies may overemphasize cost control, compromising sound clinical and cost-effectiveness justification. Without more openness, plans may prioritize coverage of products for which they obtained the largest discounts, rather than those with the best safety and efficacy profiles for the plan’s enrollees. Negotiated discounts obtained in backroom dealings may become overriding factors—and may help explain the observed variation in health plan coverage policies. Ideally, plans would account formally for a treatment’s value in their deliberations, taking into account a drug’s comparative clinical efficacy and long-term cost-effectiveness. However, unlike their counterparts abroad, most US health plans have not embraced explicit cost-effectiveness considerations in decision making. The emergence of such organizations as the Institute for Clinical and Economic Review, which produces value assessments for prescription drugs reflecting cost-effectiveness judgments, offers hope that US plans are gaining access to information that will help steer them toward a product’s value, rather than its price alone. Health plans are not compelled to be transparent and for various reasons may even be deterred from it. First, transparency requires time and resources. Smaller plans in particular may lack the capacity to assess emerging medical evidence with the frequency required to keep their coverage policies up to date. Second, fear of litigation may discourage plans from revealing the reasons behind their decisions. Third, the proprietary nature of negotiated product prices may hinder plans from disclosing the factors underpinning the decision. Fourth, opacity grants a measure of flexibility by allowing plans to more readily update their coverage policies in response to contracting changes with product manufacturers. Economists have sometimes argued that requiring price transparency can have the unintended consequence of increasing average prices because it removes the ability of purchasers to obtain secret rebates. While requiring coverage policy transparency could similarly limit an individual plan’s flexibility, the benefits of revealing coverage policy rationale should override any such concerns. A should commercial health plans become more transparent?

Output: contrast


Input: Claim: Quantum algorithms can solve certain problems faster than classical ones. [SEP] Text: The former can convert the GTDB taxonomy, as documented in the GTDB bacterial and archaeal metadata table, to NCBI-formatted taxdump files. These taxdump files can be used with any existing software that requires taxdump files, such as taxonkit (Shen & Xiong, 2019) or KrakenUniq (Breitwieser, Baker & Salzberg, 2018). ncbi-gtdb_map. py maps between NCBI and GTDB taxonomies, based on the taxonomy information provided in the GTDB archaeal and bacterial metadata files. This tool can be useful for converting GTDB-Tk classifications to NCBI taxonomies (Chaumeil et al. , 2019), or converting existing NCBI taxonomies to GTDB taxonomies without requiring re-classification. Code availability The Struo2 pipeline code and all describing the analyses in this study are available on GitHub at Results and Discussion Struo2 generates Kraken2 and Bracken databases similarly to Struo (Lu et al. , 2017; Wood, Lu & Langmead, 2019), but the algorithms diverge substantially for the time consuming step of gene annotation required for HUMAnN database construction. Struo2 performs gene annotation by clustering all gene sequences of all genomes using the rapid mmseqs2 linclust algorithm, and then each gene cluster representative is annotated via mmseq2 search (Fig. 2A; Supplemental Methods) (Steinegger & Söding, 2017; Steinegger & Söding, 2018). In contrast, Struo annotates all non-redundant genes of each genome with (Buchfink, Xie & Huson, 2015). Struo2 utilizes snakemake and conda, which allows for easy installation of all dependencies and simplified scaling to high performance computing systems (Köster & Rahmann, 2012). Benchmarking on genome subsets from the GTDB showed that Struo2 requires ∼0. 67 CPU hours per genome versus ∼2. 4 for Struo (Fig. 2B), which is a >3. 5 fold decrease. Notably, Struo2 annotates slightly more genes than Struo, possibly due to the sensitivity of the mmseqs search iterative search algorithm (Fig. 2C).

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: It was a huge sampling effort not only because they analyzed the early stage of the symbiosis but also because they had to enrich samples for cell wall polypeptides (CWP). One of the main results was the observation of the enhanced synthesis of several immunologically related 31- and 32-kDa fungal polypeptides, called symbiosis-regulated acidic polypeptides (SRAPs) [35]. As gene expression studies were also carried out, these proteomic data also highlighted the fact that expression of SRAP-32 was regulated at transcriptional level, suggesting that the synthesis of new hyphal proteins is an important process during symbiosis formation [35]. In AM symbiosis, the phase between the first contact and the formation of the first arbuscule, a period ranging between few hours and 1–2 weeks after inoculation, is normally considered as an early stage of the interaction [36]. Focus on this particular phase is important to understand the cross-talk between the partners and how the proteomes of the two organisms change during the colonization events. A study by the group of Dumas-Gaudot [37] focused on the early stages of the AM symbiosis in three different genotypes of the model plant M. truncatula: the wild-type (J5), a mycorrhiza-defective (TRV25, dmi3) and an autoregulation-defective (TR122, sunn) genotype. The study was aimed at investigating changes in the root proteome elicited in response to appressorium formation by Glomus intraradices. For this purpose, the authors compared by 2DE-PAGE the root proteome from non-inoculated roots and from roots synchronized for appressorium formation by G. intraradices. The authors showed that proteins that responded to appressorium formation were differentially expressed in different genotypes. This paper was important because it also reported, for the first time, the identification of plant root proteins involved in mycorrhizal symbioses by matrix-assisted laser desorption ionization (MALDI) time-of-flight (TOF) mass spectrometry. This technique revealed appressorium-responsive proteins that were previously unknown on the basis of transcriptome analyses, demonstrating that proteomics and transcriptomics are complementary approaches [37].

Output: corroborate


Input: Claim: All electronic devices can be made immune to hacking. [SEP] Text: The cells have different capabilities to sense things, whether it’s other cells that they should be talking to, or the ability to sense “foreign” from “self. ” Different immune cells can launch killing responses or secrete new factors that are immunosuppressive and dial down the immune system itself. What we’re trying to do is to create a new kind of sensing-response system using the same parts, reconnected in a new way. If we are programming a T cell — a white blood cell that fights infection — to recognize some set of cancer antigens and then kill the cancer cells, that’s hacking — creating a new circuit that is good at detecting and treating cancer. By the same token, we could create a cell that would detect some tissue-specific signal associated with autoimmune disease and have that cell control the secretion of immunosuppressive factors. Right now most studies using engineered T cells to address autoimmune disease are still in cell culture. There has been work in mice and humans transplanting native, immune-suppressive T cells, but these cells haven’t been engineered to seek specific targets or to reshape their behavior. What would an ideal genetically engineered immune cell be able to do? I’ll talk about cancer, because that’s the lead application. Immune cells are so powerful that they seem to be in some cases able to eliminate and cure cancer. We want them to be powerful, but of course the big danger is that if they attack any of our critical, normal tissues, that could be lethal. It’s really about combining control and precision with the effectiveness of the cells. For the most part, the therapeutic cells that are in clinical trials now, or have been approved, are not extremely smart. They may simply include one receptor that is put on the cell and, if it recognizes the antigen it was designed to detect on a cancer cell, it will kill it. But we’re developing technologies to build more sophisticated sensing circuits that can detect two or three different inputs, which could allow engineered cells to be eliminated or turned off if needed for safety. Right now we mostly use viruses to put new genetic material into the cells. There is a payload limit for these viruses, but we can get on the order of two new sensors into the cell’s genome. How much genetic material we can insert is one of several bottlenecks.

Output: contrast


Input: Claim: Quantum algorithms can solve certain problems faster than classical ones. [SEP] Text: The use of mmseqs2 allows for efficient database updating of new genomes and/or individual gene sequences via mmseqs clusterupdate (Fig. S1); we show that this approach saves 15–19% of the CPU hours relative to generating a database from scratch, with a linear trend towards increased efficiency as the number of genomes are added (Fig. 2D). We assessed how Struo2-generated databases can be affected by the assembly quality of the reference genomes used. For this assessment, we generated synthetic reference genome datasets, in which 3 types of misassemblies were introduced into each genome: breakpoints, relocations, and chimerisms (see Methods). As we increased the number of per-genome misassemblies, CheckM-estimated completeness and contamination values substantially declined and increased, respectively (Fig. 3A). The percent of annotated genes, relative to the ground truth reference genomes, declined linearly and steeply as median assembly completeness declined (linear regression, n = 10, R2 = 0. 97) and contamination increased (linear regression, n = 10, R2 = 0. 9; Fig. 3B). Even at a median completeness of 95% and a median contamination of 1. 5%, the percent of genes annotated was reduced by 25% relative to the ground truth, indicating that assembly quality has a substantial impact on gene database quality. The percent of correctly annotated genes (i. e. , the correct UniRef90 ID versus the ground truth) was also linearly associated with assembly quality (linear regression, n = 10, completeness: R2 = 0. 87, contamination: R2 = 0. 7), but the decline in annotation quality was more severe: only ∼60% of genes were correctly annotated when median completeness dropped to ∼92% (Fig. 3C). In contrast, Kraken2/Bracken database quality was not substantially affected by assembly quality, in regards to beta diversity relative to the ground truth (Fig. 3D). Beta diversity divergence increased only slightly as misassemblies increased (Mantel, permutations = 999, ρ > 0. 98 for all tests). The robustness of the Kraken2/Bracken databases is likely due to the use of kmers derived from entire genomes instead of using annotated genes, as used by HUMAnN3.

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: The early stage of the mycorrhizal symbiosis represents a challenging, but also a very attractive, stage for proteomics. The sampling time and sampling method are crucial for the experiment’s outcome, and synchronization of the root colonization events would enrich root samples in the proteins of interest. Lopez-Meyer and Harrison (2006) first proposed a system to synchronize AM fungal spore germination and root penetration events [38]. Despite this technical improvement, the amount of proteins involved in the plant response to the AM fungus and in the PPA formation, as compared to the amount of proteins in the whole root, is expected to be very low and at the limit of detectability. Therefore, new strategies for sample preparation and experimental designs are required to reveal the key components of this fundamental phase of the mycorrhizal symbiosis. 4. 2. Proteomics on late stageLate stage of the AM symbiosis commonly indicates the phase in which the fungus has already build up the arbuscules. In nature, the symbiosis is highly dynamic and very complex because, while arbuscules are forming, new penetration events occur. Bestel-Corre et al. [39] reported the first mass spectrometry (MS)-based identification of mycorrhiza-related proteins. These authors studied the response of M. truncatula inoculated either with the AM fungus Glomus mosseae (current name Funneliformis mosseae) or with the nitrogen-fixing bacterium Sinorhizobium meliloti. Proteins were separated by 2DE-PAGE and image analyses, with precise quantification of spots volume performed to identify differentially expressed protein spots. Those spots were excised from the gels and analyzed by mass spectrometry. Notably, only plant or bacterial proteins were identified, may be due to difficulties in extracting fungal proteins. The authors identified several proteins related to defence responses, root physiology and respiratory pathway. However, none seemed to be a key protein in the mycorrhizal symbiosis. The proteomic analysis of the late stage of the AM symbiosis using the whole root system as starting material never allowed very good results. To overcome this problem, many studies on the late stage of the symbiosis focused their attention on a specific sub-cellular compartment.

Output: corroborate


Input: Claim: Ants exhibit complex social behaviors and communication methods. [SEP] Text: Autumn spanned October through November, corresponding to the months when the first snowfall is typically recorded at higher elevations [30], and the time period typically associated with fall migration [8]. Winter was designated as December through February, corresponding to the months that typically have snow on the ground and low primary productivity [30, 31]. Seasonal use behaviors were assigned to one of six categories—resident, range shift, range expansion, range contraction, migration, or nomadism—according to the criteria presented in Table 1. Briefly, resident animals maintain the same home range in successive seasons while other behaviors correspond to some sort of change in home range areas between seasons. Three behaviors correspond to scenarios with partial overlap in successive seasonal home ranges: seasonal shifts when each seasonal home range includes a substantial area used only during that season, seasonal expansions when the home range in the second season completely encompasses the home range in the first, and seasonal contractions when the reverse is true. Seasonal migration occurs when there is no overlap between home ranges used in successive seasons, while nomadism refers to an extended period when animals move between locations with irregular timing or direction [32]. Examples of each behavior are provided in Fig. 2. Table 1. Seasonal use behaviors. Seasonal use behavior Criteria resident Complete or near complete overlap of seasonal home ranges, with no areas used exclusively in one season. range shift Partial overlap of seasonal home ranges, with substantial areas used exclusively in each season. range expansion Season 2 home range significantly larger than season 1 home range, with season 1 home range completely or nearly completely contained within the season 2 home range. range contraction Season 1 home range significantly larger than season 2 home range, with season 2 home range completely or nearly completely contained within the season 1 home range. migrant No overlap in seasonal home ranges (excluding migratory1 or exploratory2 movements). nomad A series of daily movements over a period of at least half the length of the season during which it occurs that creates a path which rarely crosses itself. The nomadic path may include brief stopovers during which an animal spends 2–3 days in a localized area, but generally successive daily movements take the animal to locations not previously visited during the season. 3 When a migration started and ended within a season, the 95% KDE for that season was inflated by the migratory movement.

Output: pass


Input: Claim: Quantum algorithms can solve certain problems faster than classical ones. [SEP] Text: In summary, we recommend using high quality assemblies for custom database construction, especially for the creation of the HUMAnN3 database. Figure 3: Struo2-generated gene database quality is substantially affected by reference genome assembly quality. Two reference genome datasets of 100 randomly selected genomes each (“n100_r1” and “n100_r2”) were used for simulating misassemblies among all genomes in order to assess how genome assembly quality affects Struo2-generated database quality. “Ground truth” is the unaltered reference genomes, while the “bN-rN-cN” labels denote synthetic datasets with specific numbers of added misassemblies per genome (see Methods). (A) CheckM-estimated assembly quality for each genome. (B) The percent of genes annotated in the Struo2 database versus the ground truth. (C) The percent of genes annotated correctly (i. e. , correct UniRef90 ID) versus the ground truth. (D) Change in Bray-Curtis distances between the ground truth and synthetic datasets (measured via Mantel tests), with beta diversity calculated from Bracken taxonomic assignments. The CAMI2 “HMP-gut” dataset of 10 metagenomes was used for benchmarking. Download full-size image DOI: 10. 7717/peerj. 12198/fig-3 We used Struo2 to create publicly available Kraken2, Bracken, and HUMAnN3 custom databases from releases 95 and 202 of the GTDB (see Supplemental Methods). We note that the reference genomes selected from releases 95 and 202 had a median CheckM-estimated completeness of 98. 5 and 97. 3%, respectively. The median estimated contamination was 0. 71 and 0. 79%, respectively. Thus, our custom GTDB databases should be of high quality. We will continue to publish these custom databases as new GTDB versions are released. The databases are available at We also created a set of utility tools for (i) generating NCBI-formatted taxdump files from the GTDB taxonomy, (ii) mapping between the NCBI and GTDB taxonomies, and (iii) generating databases for any set of GTDB reference genomes.

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: With this approach, the same group published two other papers few years later, using sub-cellular fractionation methods, reporting more remarkable results described in the next paragraphs. Mycorrhizal systems different from ECM and AM have been seldom investigated with proteomic approaches. However, recent results have been published for orchid mycorrhiza by Valadares et al. (2014). For orchids, the association with symbiotic fungi is required for seed germination and seedling development, when the plant relies on the fungus also for carbon supply (a strategy termed mycoheterotrophy). Recently, 2D-LC-MS/MS (two-dimensional liquid chromatography MS/MS) coupled to isobaric tagging for relative and absolute quantification has been used to identify proteins with differential accumulation in the orchid species Oncidium sphacelatum at different stages of plant development after seed inoculation with a Ceratobasidium sp. fungal isolate. Eighty-eight proteins, including proteins putatively involved in energy metabolism, cell rescue and defence, molecular signalling and secondary metabolism, have been identified and quantified. These results suggest profound metabolic changes during the development of mycorrhizal orchids, likely related to a switch from the fully mycoheterotrophic to the photosynthetic stages [40]. 4. 3. Proteomics on sub-cellular compartmentAlthough 30% of naturally occurring proteins are predicted to be embedded in biological membranes [41], comprehensive membrane proteomics is technically difficult due to the hydrophobicity, heterogeneity and lower abundance of membrane proteins. In mycorrhizal symbioses, membrane proteins are very important because they likely include the receptors that control the fungal–plant dialogue as well as the transporters that mediate nutrient exchange. For these reasons, many authors have focused on membrane proteins using different enrichment protocols. The first study on sub-cellular fractionation of membrane proteins was conducted by Benabdellah et al. (1998). They isolated the microsomal protein fraction from colonized tomato roots, where they found several differentially expressed proteins [42]. Few years later, the same group identified for the first time a protein related to mycorrhizal symbiosis by Edman N-terminal sequencing, after plasma membrane enrichment and 2DE-PAGE protein separation [43].

Output: corroborate


Input: Claim: Ants exhibit complex social behaviors and communication methods. [SEP] Text: S2), suggesting that decreased anxiety might not explain enhanced activity in KO mice. PPI of the In addition to hyperactivity, positive symptoms of psychoses are modeled using PPI of the acoustic startle response, a test for sensorimotor gating impaired in patients with schizophrenia and other neuropsychiatric disorders [31,32,33]. Male KO mice exhibited diminished PPI at all prepulse intensities (fig. 3). Two-way repeated measures ANOVA detected a significant effect of genotype [F(1,169) = 5. 5, p = 0. 02] and prepulse intensity [F(4,169) = 21. 51, p < 0. 001] but no interaction between these factors, suggesting that KO mice had an overall decrease in PPI. This impairment is unlikely related to altered startle responsiveness because we found no group-dependent differences in startle magnitude (online suppl. fig. S3). Fig. 3View largeDownload slideImpaired PPI in Nrg3 KO mice (y-axis: % of PPI, x-axis: different intensities of prepulse in dB). Two-way repeated measures ANOVA detected a significant effect of genotype [F(1,169) = 5. 5, p = 0. 02] and intensity of prepulse [F(4,169) = 21. 51, p < 0. 001], but no interaction between these factors. KO: n = 16, WT: n = 17. Fig. 3View largeDownload slideImpaired PPI in Nrg3 KO mice (y-axis: % of PPI, x-axis: different intensities of prepulse in dB). Two-way repeated measures ANOVA detected a significant effect of genotype [F(1,169) = 5. 5, p = 0. 02] and intensity of prepulse [F(4,169) = 21. 51, p < 0. 001], but no interaction between these factors. KO: n = 16, WT: n = 17. Close modal MK-801-Patients with schizophrenia display exaggerated responses to psychostimulants [34]. Acute treatments with stimulants are widely used to mimic aspects of positive symptoms in different rodent models [31,32]. Thus, we sought to evaluate behavioral responses to MK-801, an NMDA receptor antagonist, to assess glutamate synaptic transmission in WT and KO mice.

Output: pass


Input: Claim: Quantum algorithms can solve certain problems faster than classical ones. [SEP] Text: The taxdump files are utilized by Struo2, but these tools can be used more generally to integrate the GTDB taxonomy into existing pipelines designed for the NCBI taxonomy (available at In summary, Struo2 provides a substantial improvement over the state-of-the-art, which is needed in order to scale custom database generation with the ever-increasing amount of available genomic data. The efficient database updating feature of Struo2 enables the following “assembly enhanced” taxonomic profiling workflow. First, the user assembles MAGs and/or genes from the metagenomes novel to the user’s own study. Second, the user updates our pre-generated GTDB-based profiling databases with the newly generated genomic content. Third, the user taxonomically profiles the metagenome reads from the user’s study against the newly customized databases in order to generate microbial diversity assessments that are less biased and more representative of the microbiome diversity in the microbiomes included in the study. This “assembly-enhanced” profiling method could greatly improve database representation for less-studied environments such as the gut microbiome of non-mammalian vertebrates or under-represented human populations (Porras & Brito, 2019; Youngblut et al. , 2020). An overview of the Struo2 algorithm for database updating Cylinders are input or output files, squares are processes, and right-tilted rhomboids are intermediate files. Existing Kraken2, Bracken, genes, and HUMAnN3 databases can be updated with new genomes, while only existing genes and HUMAnN3 databases can be updated with new individual gene sequences. DOI: 10. 7717/peerj. 12198/supp-1 number of GTDB genomes per phylum used for Struo2 generation of the custom Kraken2, Bracken, genes, and HUMAnN3 databases available at See the Materials & Methods for information on how genomes were selected. The phylum names shown are based on the GTDB taxonomy. DOI: 10. 7717/peerj. 12198/supp-2 We thank Albane Ruaud, Liam Fitzstevens, Jacobo de la Cuesta-Zuluaga, and for providing helpful comments on an earlier version of this manuscript. authors declare there are no competing interests.

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: These parameters are measured for individual LC-MS/MS or LC/LC-MS/MS runs and changes in protein abundance are calculated via a direct comparison between different analyses [52]. The spectral counting strategy used by Abdallah et al. (2014) suggests that accommodation of AM fungi within root cortical cells implies both a dynamic reorganization of the root membrane proteome and the de novo synthesis of AM-related proteins [23]. This study, beside the identification of proteins corresponding to key genes already identified in mycorrhizal symbiosis, also reported new proteins, many of which support the importance of membrane trafficking during mycorrhiza colonization. In summary, sub-cellular and peptide fractionations led to the identification of many key proteins involved in AM symbiosis. Despite the recent contributions of proteomics to the study of the plant–fungus mycorrhizal interactions were substantial, the role of many of them as actors in the symbiosis is still to be fully understood. 4. 4. Proteomics to identify fungal proteinsMost proteomic studies on the AM interaction have focused on plant proteins. Identification of fungal proteins is more challenging due to the impossibility to grow AM fungi in axenic cultures, to the lower amount of fungal biomass, as compared to plant material, and to the more scanty sequence information. However, pioneering studies have been carried out in France by Dumas-Gaudot and Recorbet [53,54]. Dumas-Gaudot et al. (2004) used the root organ culture method [55] to enrich for proteins expressed in the extra-radical mycelium of the AM species G. intraradices. They successfully produced, for the first time, a 2DE reference map for the extra-radical proteome of an AM fungus. After the selection of the most intense protein spots, they tried to identify them by mass spectrometry. Unfortunately, only very few proteins from filamentous fungi were known and present in public databases, and the only available genome sequence, at that time, was from Neurospora crassa, phylogenetically very far from Glomeromycota. In spite of that, identification was possible for 8 proteins out of the 14 analyzed, and homologies were found for 4 of them. Few years later, the same group attempted again the identification of fungal proteins expressed in the AM association [54].

Output: corroborate


Input: Claim: Some ant species can farm fungi as a food source. [SEP] Text: Scale bar, 5 μm. (D) Quantification of the intensity of SAS-6-GFP and Sid4-tdTomato per SPB in the cells shown in C. Mean SAS-6-GFP (top) and Sid4-tdTomato (down) intensity per SPB ±s. d before and after the SPB separation is shown (N = 7). SPB-0, unseparated SPB and SPB-1 and 2, separated SPBs. Figure 4—figure supplement 3—source data 1 The source data to plot the graphs in Figure 4—figure supplement 3B and E. Download elife-41418-fig4-figsupp3-data1-v1. xlsx Figure 5 Download asset Open asset pericentrin (PLP) conserved domain –PACT- interacts with SAS-6, and its overexpression causes centriole amplification. (A) Schematic illustration of human pericentrin, Drosophila PLP and S. pombe Pcp1. (B) Physical interaction between SAS-6-EGFP and the conserved Drosophila PACT domain. Protein extract was prepared from Drosophila tissue culture cells (D. Mel cells) expressing HA-tagged PACT, and SAS-6-EGFP or EGFP. The GFP-tagged proteins were immunoprecipitated with anti-GFP antibody. Immunoprecipitates and inputs (20%) were analyzed by western blotting using the indicated antibodies. (C) Direct binding between SAS-6 and PACT. The in vitro binding assay was performed using purified GST or GST-fused SAS-6 and His-tagged. Note that we loaded 100% for each sample on each lane after pull-down to compare the efficiency of direct binding (bound vs input). Quantification of His-PACT bound to the GST-fusion protein is shown below the panel (data are the average of three experiments ± s. d) (D) Cells overexpressing EGFP-PACT or EGFP were arrested in mitosis by colchicine treatment for six hours and stained with the antibodies against Bld10 (centriole marker), phospho-H3 (mitotic marker) and SAS-6. Scale bar, 5 μm.

Output: pass


Input: Claim: Quantum algorithms can solve certain problems faster than classical ones. [SEP] Text: conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft. conceived and designed the experiments, authored or reviewed drafts of the paper, and approved the final draft. The following information was supplied regarding data availability: Struo2 code: Pre-built metagenome profiling databases: Utility tools for taxonomy and database processing: work was supported by the Society. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 2020. A unified catalog of 204,938 reference genomes from the human gut microbiome. 39:105-114. 2016. Multiple comparative metagenomics using multiset K-Mer counting. 2(November):e94. 2018. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6(1):90. 2018. KrakenUniq: confident and fast metagenomics classification using unique K-Mer counts. 19(1):198. 2019. A review of methods and databases for metagenomic classification and assembly. Briefings in Bioinformatics 20(4):1125-1136. 2015. Fast and sensitive protein alignment using. 12(1):59-60 Chaumeil P-. 2019. TDB-Tk: a toolkit to classify genomes with the genome taxonomy database. Bioinformatics 36(6):1925-1927 De la Cuesta-. 2020. Struo: a pipeline for building custom databases for common metagenome profilers. Bioinformatics 36(7):2314-2315. 2018. Species-level functional profiling of metagenomes and metatranscriptomes. 15(11):962-968 Hyatt D, Chen G-. 2010. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11(March):119. 2016. Centrifuge: rapid and sensitive classification of metagenomic sequences. 26(12):1721-1729 Köster J, Rahmann S. 2012. Snakemake–a scalable bioinformatics workflow engine. Bioinformatics 28(19):2520-2522. 2020.

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: They maintained the same experimental system, but they used the GeLC-MS/MS method and could identify 92 different fungal proteins. GeLC-MS/MS approach combines a mono-dimensional gel (1D-PAGE) and a nano-scale capillary liquid chromatography-MS/MS. Briefly, after the 1D-PAGE separation, the mono-dimensional gel is cut in several pieces; in-gel digestion results in different protein fractions that are separated and analyzed by LC-MS/MS. Using the MetaCyc database, a collection of more than a thousand metabolic pathways [57], these authors grouped those proteins in 11 pathways that span energy, metabolism and cell rescue processes. These data, together with previous identifications of putative homologues of cell-cycle gene in G. mosseae and G. intraradices [56,57], suggest that signalling pathways known in model species may also operate in AM fungi. Although the GeLC-MS/MS strategy opened the possibility to large-scale proteomics of mycorrhizal fungi, no further data have been published with this technique. Secreted fungal proteins play key roles in host plant colonization and symbiosis development in ECM interactions. Vincent et al. (2012) have identified the extracellular proteins secreted in the growth medium by the free-living mycelium of the ECM fungus L. bicolor using 2-DE, IPG-IEF shotgun (IPG strip was cut into fractions and tryptic peptides were eluted from the each fraction) and SDS-PAGE shotgun, with the aim to validate predicted secreted proteins and identify putative novel effectors of the symbiosis. Among the 224 proteins identified, there were carbohydrate-active enzymes (CAZymes), probably involved in cell wall remodelling during hyphal growth, as well as secreted proteases. Additionally, the involvement of some of these proteins in the establishment of the mycorrhizal symbiosis was supported by transcriptomic analyses of ECM roots [58]. Advertisement 5. Mycorrhizal fungi and heavy metalsIn plants, stress tolerance to soil pollution can be increased by their interaction with mycorrhizal fungi [59].

Output: corroborate


Input: Claim: Some ant species can farm fungi as a food source. [SEP] Text: For other material examined () refer to Table S2 in the electronic supporting material. Diagnosis. Within the C. kelleri-group, workers of C. madagascariensis can be distinguished from C. kelleri and C. tavaratra by the position of the propodeal spiracle, which is situated confluent with the base of the propodeal spines in C. madagascariensis, but below the spine base in the two latter species. From the two remaining species, workers are most easily separated by body size and coloration: C. madagascariensis workers are usually larger (HW 0. 86–1. 11, WL 0. 92–1. 16) than C. hazolava (HW 0. 66–0. 89, WL 0. 70–0. 97) and C. hafahafa (HW 0. 68–0. 72, WL 0. 73–0. 74) workers, are brown-colored (yellow in C. hafahafa and some C. hazolava populations) and lack the median postpetiolar stripe (of brown-colored C. hazolava workers, see Fig. 6). Worker measurements (n = 22). HW 0. 86–1. 11; HL 0. 78–0. 99; EL 0. 15–0. 23; SL 0. 67–0. 86; WL 0. 92–1. 16; SPL 0. 08–0. 18; PTH 0. 15–0. 22; PTL 0. 22–0. 31; PTW 0. 24–0. 37; PPL 0. 15–0. 20; PPW 0. 22–0. 30; LHT 0. 68–0. 97; CI 1. 07–1. 18; OI 0. 18–0. 27; SI 0. 79–0. 96; SPI 0. 08–0. 15; PTHI 0. 56–0. 83; PTWI 0. 93–1. 31; PPI 1. 33–1. 71; LBI 1. 04–1. 50. Worker description (Fig. 23A−C). Small to large species (HW 0. 86–1. 11, WL 0. 92–1.

Output: pass


Input: Claim: Quantum algorithms can solve certain problems faster than classical ones. [SEP] Text: Metalign: efficient alignment-based metagenomic profiling via containment min hash. 21(1):242. 2020. Metalign: efficient alignment-based metagenomic profiling via containment min hash. BMC Biology 18(1):37. 2017. Bracken: estimating species abundance in metagenomics data. PeerJ 3(January):e104 -. 2020. A complete domain-to-species taxonomy for bacteria and archaea. 38(9):1079-1086. 2015. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. 25(7):1043-1055. 2019. The internationalization of human microbiome research. in Microbiology 50(August):50-55. 2016. : a versatile open source tool for metagenomics. PeerJ 4(October):e2584 --. 2019. Streaming histogram sketching for rapid microbiome analytics. Microbiome 7(1):40. 2021. Database resources of the national center for biotechnology information. Research 49(D1):D10-D17 öge J, Gregor I. 2017. Critical assessment of metagenome interpretation-a benchmark of metagenomics software. 14(11):1063-1071 Shen W, Xiong J. 2019. TaxonKit: a cross-platform and efficient NCBI taxonomy toolkit. Harbor Laboratory. Steinegger M, Mirdita M, Söding J. 2019. Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. 16(7):603-606 Steinegger M, Söding J. 2017. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data Sets. 35(11):1026-1028 Steinegger M, Söding J. 2018. Clustering huge protein sequence sets in linear time. 9(1):2542. 2019. Improved metagenomic analysis with kraken 2. 20(1):257 Youngblut ND, De la Cuesta-. 2020. Large-scale metagenome assembly reveals novel animal-associated microbial genomes, biosynthetic gene clusters, and other genetic diversity. mSystems 5(6) a question Learn more about Q&A a link Alert Loading. . . Just enter your email Tools & info history See citing 4.

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: Six out of ten of the most polluted soils in the world are contaminated by heavy metals [60], and mycorrhizal symbioses have been found to reduce metal toxicity to the host in soils with potentially toxic amounts of soluble and insoluble metals. Phytoremediation, the plant-mediated reclamation of polluted soils, is receiving increasing attention as a natural method to restore the biological features of the soil. Mycorrhizal fungi can have an important part in this process. Many studies have been carried out on the benefits of mycorrhizal plant–fungus interaction in heavy-metals-polluted soils, but only few of them have used a proteomic approach to identify the key proteins potentially involved in mycorrhiza-mediated stress tolerance. Researches on this topic have analyzed different plant organs, like leaves or root, but also focused on the symbiotic fungus. Bona et al. (2010) studied the leaf proteome of the arsenic hyperaccumulator fern Pteris vittata inoculated with two fungi (Glomus mosseae and Gigaspora margarita), with and without arsenic treatment [59]. The symbiosis with both fungi decreased arsenic concentration compared with non-mycorrhizal plants, indicating the protective effect of mycorrhizal fungi. Interestingly, the plant protein expression profile was different when the plant was inoculated with G. mosseae or G. margarita. Although they studied a different biological system, Cangahuala-Inocente and colleagues (2011) identified instead a core of 25 proteins, supporting the existence of conserved plant responses to Glomus irregulare and Glomus mosseae, at least in a woody perennial species such as grapevine [61]. A study on root proteomics has been conducted by Aloui et al. [62]. They reported, using a 2DE approach followed by MS/MS, the protective effect conferred by G. intraradices to the model legume M. truncatula in the presence of Cd. They identified 36 mycorrhiza-related proteins, but only 6 displayed changes in abundance upon Cd exposure.

Output: corroborate


Input: Claim: Army ants are known for their aggressive foraging behavior and colony movement. [SEP] Text: However, little is known about differences among colonies in collective behavior, such as foraging. How ant colonies regulate foraging varies among species, depending on the kind of food a species collects. When food sources are large or clumped, so that many ants are needed to retrieve the food, the problem for the colony is to direct the appropriate number of ants to the location with the richest food supply. For example, elegant laboratory experiments and models show how trail-laying species such as Lasius niger or Tetramorium caespitum solve this problem (e. g. , Beckers et al. 1990, 1993; Collignon and Detrain 2010). The number of ants laying trail pheromone is related to the quality of food encountered; this sets the probability that inactive foragers will encounter pheromone and be recruited to the food (Detrain and Deneubourg 2008). Leaf-cutter ants, which forage for leaves to feed the fungus that they consume, regulate foraging using the quality of leaves found (Saverschek et al. 2010) and the interactions on the trail (Burd 2000; Farji-Brener et al. 2010). By contrast, ant species that forage for scattered food sources, each of which can be retrieved by a single ant, face a different problem. The colony obtains more food not by directing more ants to the best food sources but by covering more ground with ants searching individually. For example, the red harvester ant (Pogonomyrmex barbatus), common in arid chaparral habitats in the southwestern US and Mexico, forages for seeds widely scattered by wind and flooding (Gordon 1993) and does not lay pheromone trails to particular food sources. Colonies store food inside the nest for many months (Gordon 1992, 1993). The colony must balance the costs of desiccation while foraging and the need to claim foraging area in competition with neighboring colonies (Adler and Gordon 2003). Ants foraging in hot dry conditions lose water, but obtain water from metabolizing fats in the seeds that they eat (Lighton and Bartholomew 1988; Lighton and Feener 1989). Thus, ants must spend water to get water.

Output: contrast


Input: Claim: Superposition allows qubits to represent multiple states simultaneously. [SEP] Text: You might be wondering why we need three qubits in the definition above, and, perhaps surprisingly, we’ll see that AND and OR require us to work with three qubits. Because how would one compute an AND of two qubits? Taking a naive approach from how we did the quantum NOT, we would label $ e_0$ as “false” and $ e_1$ as “true,” and we’d want to map $ e_1 \otimes e_1 \mapsto e_1$ and all other possibilities to $ e_0$. The main problem is that this is not an invertible function! Remember, all quantum operations are unitary matrices and all unitary matrices have inverses, so we have to model AND and OR as an invertible operation. We also have a “type error,” since the output is not even in the same vector space as the input, but any way to fix that would still run into the invertibility problem. The way to deal with this is to add an extra “scratch work” qubit that is used for nothing else except to make the operation invertible. So now say we have three qubits $ a, b, c$, and we want to compute $ a$ AND $ b$ in the sensible way described above. What we do is map$$\displaystyle a \otimes b \otimes c \mapsto a \otimes b \otimes (c \oplus (a \wedge b))$$Here $ a \wedge b$ is the usual AND (where we interpret, e. g. , $ e_1 \wedge e_0 = e_0$), and $ \oplus$ is the exclusive or operation on bits. It’s clear that this mapping makes sense for “bits” (the true/false interpretation of basis vectors) and so we can extend it to a linear map by writing down the matrix. quantum-ANDThis gate is often called the Toffoli gate by physicists, but we’ll just call it the (quantum) AND gate. Note that the column $ ijk$ represents the input $ e_i \otimes e_j \otimes e_k$, and the 1 in that column denotes the row whose label is the output.

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: In the past few years, a specialized fungal genomics portal, called MycoCosm ( has been created by the US Department of Energy (DOE) Institute (JGI), offering an access point to the data from all the sequencing genome project managed by the [68,69]. Starting from the three first genome projects on Laccaria bicolor, Tuber melanosporum and Rhizophagus intraradices [70,71,72], several more genomes from symbiotic fungi, including ECM, AM, orchid and ericoid fungi, have been recently sequenced, with the aim to determine the diversity of the molecular processes involved in the interaction [28]. Despite the more powerful techniques and wider reference datasets for protein identification, current limitations exist in the application of proteomics to the study of plant–microbe interactions. In particular, new extraction methods, microsomal studies, sub-cellular enrichment, gel-free separation methods, pre-fractioning separations and new mass spectrometry are still far from being fully explored [73]. In order to identify proteins from a very small subset of target cells, Gaude et al. (2012) combined laser capture microdissection (LCM) and LC-MS/MS [74]. Laser microdissection permits the rapid isolation, from sections of a heterogeneous tissue, of a selected cell population in a manner compatible with the extraction of or proteins [29]. Using LCM, arbuscule-containing cortical cells and cortical cells from non-mycorrhizal M. truncatula roots were isolated. Proteomic analyses on these cells revealed a number of proteins involved in lipid metabolism, most likely related to the synthesis of the PAM. This targeted analysis on a specific subset of colonized cells, those harbouring the arbuscules, curiously did not identify known PAM marker proteins, thus suggesting that either sample preparation or instrument capability were not sensitive enough. Although this first use of LCM in the proteomic investigation of the AM mycorrhizal symbiosis did not identify known marker proteins, it highlighted the PAM as an important carbon sink. The LCM technique coupled with MS/MS techniques could be a powerful combination to investigate the protein profiles of specific cells at specific time-points. Moreover, LCM can help to overcome the problem of asynchronous fungal development and arbuscule maturation in mycorrhizal roots.

Output: corroborate


Input: Claim: Many species of birds engage in migratory behavior based on seasonal changes. [SEP] Text: Other species such as Graylag goose and White pelican showed strong declines that were not statistically significant because these changes in vegetation cover led some birds to search for other areas with good food and weather. Index of differences in amounts of migratory birds in Al-indicated that this approach, anytime soon, due to changes in migratory bird distribution within the studied area, it seems to have limited potential and the significant sampling error in the subsection itself counts. Long-term analyses of the operation of the system, concurrent with an increased number of counts, (e. g. , seagrass, and fish) our understanding of the reasons behind the overall change in migratory bird populations will be improved. Finally, an annual count would also mean that all ecological areas are visited and verified on a daily basis, thus improving the potential to detect major losses. 2008. “Types of Birds in the and the to It from Other Countries. ” of Environment. Afroz, Romana, and Bodruddoza Mia. 2019. “Evaluation and Monitoring of and Quality of the in Multi-Temporal” 8. Al-gburi, -tawash, and Al-lafta. 2017. “of Al-, Southern. ” Heliyon, no. January: e00256. and Abdul-rahman B Ali. 2019. “Evaluation of Impact of on in ” no. November. and Al Ramahi. 2020. “A Study of the Effect of Urbanization on Rates in Sensing” 61 (8): 2142–49. Ali, Sura Kareem. 2019. “of Marsh, South of Iraq with the Application of Gis Technique,” no. January. and Erica Fleishman. 2020. “Associations of Breeding-with among Species and Trait-in Southern California,” 1–16. and Willis. 2020. “Disentangling the of Climate and Change in Driving the Long-Trends of Birds,” no. June: 1–14. and Johannes Hübl. 2019. “Analysis of of Uzbekistan” 28 (5): 3235–42. 2011. “in the Study of ” 86 (0316): 928–46.

Output: contrast


Input: Claim: Fossils provide insights into the evolutionary history of life. [SEP] Text: hardwickii, and R. muscatellum are distinct with partial overlap between the latter two (Figure 2). These phenotypic groups (or morphospecies) were found to be internally homogenous in their diagnostic characters, with R. hardwickii having the pronounced intraspecific variation, particularly in metrical components of body size [for details see Additional file 1]. Figure 2Morphometric characteristics of Rhinopomatidae. a – scatter plot of CM3 vs. M1–M3 in the sample examined in frame of this study (n = 252, for further data see [28, 29]) with position of the respective type specimens: note metric distinctness of the phenotypic forms hardwickii, microphyllum, muscatellum (1 – Iran, 2 – Yemen) and position of the Miocene fossil from Elaiochoria 2, Greece; b – mean values and variation span of CM3 in four species of Rhinopoma as reported by [9], n = 357, 63, 54, 154; c – phylogenetic hypothesis suggested by morphometric characters. Full size imageMolecular analysesWe obtained 26 sequences from ingroup taxa of the first 402 bp of the cytochrome b gene. Ninety-nine positions were variable, 93 were parsimony informative, and all mutations were base substitutions. samples analyzed fell into 15 haplotypes. The sequences exhibited a low level of saturation at the first and second codon positions, with a deflection from linearity at third codon positions. We used a GTR + I + G distance correction model with gamma distribution shape parameter = 1. 70. All tree-building methods resulted in recognition of five clades, highly supported by bootstrap values and posterior probabilities (Figures 3 and 4): (clade I) Iranian haplotypes of R. hardwickii; (clade II) and north African haplotypes of R. hardwickii; (clade III) Yemeni haplotypes of R. muscatellum; (clade IV) Iranian haplotypes of R. muscatellum; and (clade V) haplotypes from the two available specimens of R. microphyllum. Figure 3The topology of the rhinopomatid tree reconstructed from the 402 bp sequence of cytochrome b gene.

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: , 1999 2007 --- Boletus edulis 2DE--Q-, 2007 2012 --- Laccaria bicolor IPG-IEF 1-DE and 2-DE -, 2012 AM 2000 Lycopersicon esculentum Glomus mosseae 2DE-PAGE N-terminal sequencing Benabdellah et al. , 2000 2001 Medicago truncatula Glomus mosseae 2DE--TOF Bestel-Corre et al. , 2001 2004 --- Glomus intraradices 2DE-PAGE Q-Tof2 Dumas-Gaudot et al. , 2004 2005 Medicago truncatula Glomus intraradices 2DE--TOF Valot et al. , 2005 2006 Medicago truncatula Glomus intraradices 2DE--TOF Amiour et al. , 2006 2006 Medicago truncatula Glomus intraradices 1-DE and 2-DE -PAGE 2D-+ Valot et al. , 2006 2009 --- Glomus intraradices 1-DE--Q-, 2009 2009 Medicago truncatula Glomus intraradices 2DE-+ Aloui et al. , 2009 2010 Pteris vittata Glomus mosseae Gigaspora margarita 2DE-hybrid quadrupole-TOF Bona et al. , 2010 2011 Vitis vinifere Glomus mosseae Glomus intraradices 2DE--TOF Cangahuala-Inocente et al. , 2011 2012 Populus alba Glomus intraradices 2DE--Q-, 2012 2014 Medicago truncatula Rhizophagus irregularis 1-DE-ion trap Abdallah et al. , 2014 Others 2015 --- Oidiodendron maius 2DE-PAGE 2D--TOFMS Chiapello et al. , 2015 2014 Oncidium sphacelatum Ceratobasidium sp. Isolate 2D-LC ESI-Q-, 2014 Table 1. List of papers in which proteomics has been applied to study mycorrhizal symbiosis. For simplicity, AM fungi have been indicated with the names used in the original 2013.

Output: corroborate


Input: Claim: Some insects can undergo complete metamorphosis, transitioning through larval stages. [SEP] Text: In particular (38) 1Tlimt→0τ(t)=δ2(w∕2)2+1 and (39) 1Tlimt→∞τ(t)=δ2+w2π2 In the experimental parameter regime, the timescale we measure is presumably intermediate, (40) τ=αηsρ02+βγsρ02W02, where α and β are dimensionless quantities which we determine numerically. To obtain α and β for a given set of input parameters, we numerically solve Equations (7,11,13) and extract the timescale τ(W0) for several initial widths. We then fit the results to the functional form of Equation (40). In the experimental regime, using the parameters for which the theoretical contraction profiles best agree with the numerical one (see Figure 5B), we estimate α≃2. 2 ± 0. 05 and β≃0. 085 ± 0. 006. The error estimates were obtained by sampling α and β over a range of input parameters between half and twice the best fit values, and evaluating a standard error on the computed values. Numerical treatment To solve Equations (7,11,13) numerically, we discretize the system by representing ρ on an equispaced grid between x=-W(t)∕2 and x=W(t)∕2, where W(t) is the instantaneous width of the contracting film. The instantaneous film velocity is determined from Equation (11) using a second order finite difference scheme. The boundary conditions σ=0 at x= ± W(t)∕2, are implemented using an asymmetric second order finite difference stencil, see (Tornberg and Shelley, 2004). We determine the time derivative of density using Equation (26) with the boundary condition specified in Equation 27, which account for the grid contracting with the width of the film. We time-evolve Equations (26,13) using a adaptive second order time stepping provided by project (Jones and Oliphant, 2001). 597. 3136. 10. 1039/C5SM02506K 8500. 1383. 1656. 256. 471. 708. 8101. 489. 296. 240. 2314. 425. 480. 410. 422. 683. 1781. 9869.

Output: pass


Input: Claim: Fossils provide insights into the evolutionary history of life. [SEP] Text: (a) maximum parsimony tree, length = 320 mutations, consistency index excluding uninformative characters = 0. 57; retention index = 0. 82; rescaled consistency index = 0. 50 (b) maximum likelihood tree calculated under GTR+I+G model of sequence evolution, R-matrix = (2209. 1157, 5744. 5737, 1027. 7924, 0. 0014, 23577. 1543, 1. 0000), base frequencies = (0. 2945, 0. 3553, 0. 1347, 0. 2154) and gamma shape parameter = 1. 6997, logL = -1867. 36 (c) Bayesian tree with the same model of sequence evolution as in ML method (d) linearized maximum likelihood tree with the same model of sequence evolution as in ML method and molecular clock enforcement, logLclock = -1884. 45. Numbers at the nodes correspond to 1000 replication bootstrap supports/posterior probabilities. Full size imageFigure 4Geographic arrangements of parsimony networks connecting mtDNA haplotypes of bats from the genus Rhinopoma within clades revealed by tree building methods. Numbers at the branches indicate number of mutational steps. Dashed lines – the branches with minimum number of mutations between clades I and II and clades III and IV. The superimposition of the network in Iran, Yemen and Levant do not match geography exactly due to space limitations. Full size imageEastern and north African haplotypes of R. hardwickii (clade II) were subdivided into four geographic regions: (a) the Levant (Jordan and Syria); (b) Yemen and Socotra; (c) Upper Egypt; and (d) northern Libya. In contrast to the morphometric data that suggested considerable variation within that clade and support the distinctness of its traditionally recognized subspecies (viz. the large-sized R. h. arabium vs. the smaller R. h. cystops from the Upper Egypt) the genetic data demonstrated an unexpected homogeneity in clade II. A shallow but distinct divergence (p-distance = 3%) was found between the haplotypes (c + d) and those from the Levant and Yemen (a + b). No genetic difference was found between (c) and (d), which represent the most distinct forms of the clade in terms of morphometry (Figs.

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: An evidence-based consensus for the classification of arbuscular mycorrhizal fungi (Glomeromycota). Mycorrhiza 23:515-31). The ability to analyze a single-cell proteome is exciting, but also extremely challenging. The first difficulty is the sensitivity, both correlated with the sample itself and with the mass spectrometry detection capability. Every single cell can contain proteins in a range of few to million copies per cell. However, mass spectrometers are now really powerful, and even with an attomole detection limit, only the most abundant proteins are detectable [78]. The estimated number and concentration of proteins in a single mammalian cell is 33 attomole/cell for the most abundant and 830 yotomole/cell for the less abundant [77]. The second challenge is the inherent limitation associated with the imaging modality itself. Even if further development is needed to obtain the combined resolution and sensitivity required, IMS stands up as a very promising technique to analyze specific cell types or conditions. By employing IMS, Ye et al. (2013) detected a large array of organic acids, amino acids, sugars, lipids, flavonoids in roots and root nodules of M. truncatula during nitrogen fixation [79]. They demonstrated that IMS can obtain unique information on the identity and spatial distribution of plant metabolites, although high-resolution MALDI-MS is required to fully resolve the metabolic differences in nodule chemistry. In conclusion, similarly to other ‘omics’ approaches, proteomics has also made rapid progress in the recent year, thus making this approach a very useful one to complement information on gene expression in mycorrhizal tissues. At this speed of technological developments, methods that allow us to easily assign proteins up-regulated during symbiosis to specific cell types and sub-cellular compartments may not be too far ahead. These proteomic techniques will be powerful tools to unravel the molecular component involved in plant–mycorrhizal fungal interactions. 1. . Mineral nutrition, toxic element accumulation and water relations of arbuscular mycorrhizal plants. Mycorrhizal Symbiosis. 3rd edn. Academic Press, London, 2008. 148. 2. Markmann K, Parniske M. Evolution of root endosymbiosis with bacteria: how novel are nodules? Sci 2009;14:77–86.

Output: corroborate


Input: Claim: Some insects can undergo complete metamorphosis, transitioning through larval stages. [SEP] Text: However, we never claimed anywhere in the manuscript that this early developmental period corresponds to metamorphosis. Something else is occurring and both TH and cortisol seem to be involved but further experiments need to be conducted to understand their role and their possible interaction. We have added corresponding statements in the abstract (lines 39-43) and discussion (lines 447 to 449). (3) Finally, regarding the comment about cortisol enhancing or participating in TH driven metamorphosis, our data clearly showed an activation of the corticoid pathway genes around metamorphosis (between D32 and juvenile stage) suggesting a potential implication of corticoids in metamorphosis, but we agree with the reviewer that further experiment are needed to test that. We never claimed that cortisol was enhancing or participating in metamorphosis, on the contrary we are “suggesting a possible interaction between TH and corticoid pathway during metamorphosis”. And we also say that our “results brought a first insight into the potential role of corticoids in the metamorphosis of E. malabaricus and call for functional experiments directly testing a possible synergy. ” Nonetheless, we agree that some parts of our manuscript can be confusing in regards of cortisol synthesis during metamorphosis as we did not measure cortisol levels between D32 and juvenile stage. We have therefore made changes throughout the Introduction and Discussion to make this clearer. Given this, the authors should quantify whole-body TH levels throughout the entire developmental window considered to determine where the peak is observed and how it correlates with the other hormonal genes/systems in the analysis. We did not measure TH levels at later stages as it has already been measured during Epinephelus coioides metamorphosis and the morphological changes observed in this species around the TH peak corresponds to what we observed in Epinephelus malabaricus around the peak of expression of TH pathway genes (see et al. , 1998 General and Comparative Endocrinology, 112:10-16). The main focus of this manuscript is the novel observation of the existence of an early activation period observed at D3, and for which we needed TH levels to determine if they were involved in another early developmental process (not related to metamorphosis). Our hypothesis is that this early activation might be related to the growth of fin rays necessary to enhance floatability during the oceanic larval dispersal.

Output: contrast


Input: Claim: Fossils provide insights into the evolutionary history of life. [SEP] Text: Our mtDNA study confirmed the existence of the same three deep lineages recognized as morphospecies by Koopman [4], Hill [8] and [9] (another recognized species, R. macinnesi, was not included in our comparison). In addition, we found (i) deep divergence within the R. hardwickii lineage, (ii) incongruency between genetic and phenotypic phylogeographic patterns in clade II, (iii) deep divergence within the R. muscatellum lineage, (iv) a very shallow distance between the samples of R. microphyllum, suggesting an unexpected genetic homogeneity of that species. Last but not least, we demonstrated that (v) R. muscatellum (including the Yemeni population) is not a sister group of hardwickii, but of microphyllum. All these results contradict the standard view of the taxonomic structure of the family (Table 1), as well as of its distributional history [e. g. [9, 15]] and call for a brief comment. Phylogeographic patterns(i) The genetic divergence found within R. hardwickii s. l. (= the R. hardwickii clade) splits the corresponding morphospecies into an Iranian clade I (R. hardwickii s. s. ) and Afro-Arabian clade II (R. cystops). While there is a clear genetic continuity between the Levantine and Yemeni populations (e. g. haplotypes LE1, YE2, which are separated by approximately 3,000 km, differ by only 1 mutation step), the much smaller geographic distance between the Levantine and Iranian samples (approx. 1,200 km) is combined with deep genetic dissimilarities (the minimum genetic distance between haplotypes LE1 and IR3 is 34 mutation steps). We expect the divergence between these two groups represents real phylogeographic structure, a break crossing the from the north-west to the south-east. The boundary might be situated along the southern part of the Zagros Mountains, which represents a significant distribution barrier to many clades [17, 19]. Unfortunately, knowledge of the distribution of bats in upper Mesopotamia is too scarce [19] to allow further discussion.

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: 3. . Mycorrhizal fungi: their habitats and nutritional strategies. In:. (ed. ) Environmental and (The Mycota). 2nd ed. Springer; 2015. 256. 4. . Ectomycorrhizal lifestyle in fungi: global diversity, distribution, and evolution of phylogenetic lineages. Mycorrhiza 2009;20:217–63. 5. LePage BA. The evolution, biogeography, and palaeoecology of the Pinaceae based on fossil and extant representative. 2003;615:29–52. 6. . Evolutionary studies of ectomycorrhizal fungi: recent advances and future directions. Bot 2004;82:1122–32. 7. . Mycorrhizal Symbiosis. 2nd ed. San Diego, California: Academic Press, 1997. p. 605. 8. . Proteome analysis of an ectomycorrhizal fungus Boletus edulis under salt shock. 2007;111:939–46. 9. A higher-level phylogenetic classification of the Fungi. 2007;111:509–47. 10. Parniske M. Arbuscular mycorrhiza: the mother of plant root endosymbioses. Nature 2008;6:763–75. 11. -Hill Y. Carbon metabolism and transport in arbuscular mycorrhizas. 2000;124:949–58. 12. Yang S-Y, Grønlund Nonredundant regulation of rice arbuscular mycorrhizal symbiosis by two members of the phosphate transporter1 gene family. 2012;24:4236–51. 13. . Roles of arbuscular mycorrhizas in plant nutrition and growth: new paradigms from cellular to ecosystem scales. 2011;62:227–50. 14. Gutjahr C, Parniske M. Cell and developmental biology of arbuscular mycorrhiza symbiosis. Biol 2013;29:593–617. 15. . Fungal endophytes in a 400-million-yr-old land plant: infection pathways, spatial distribution, and host responses. 2006;174:648–57. 16. . Fossil arbuscular mycorrhizae from the early Devonian. Mycologia 1995;87:560.

Output: corroborate


Input: Claim: Some insects can undergo complete metamorphosis, transitioning through larval stages. [SEP] Text: Download elife-80594-fig3-data2-v2. pptx Figure 3—source data 3 Examples of the adult anatomies of larval neurons MBON-d1, MBON-e2, and MBON-f2 obtained by flip-switch-mediated immortalization of expression of stable spilt lines late in larval life. The anatomy of the adult form of the three neurons was revealed using lines SS01705, SS04172, and SS04328, respectively. Download elife-80594-fig3-data3-v2. pptx Figure 3—source data 4 Examples of the adult anatomies of larval neurons MBON-g1 and -g2 obtained by flip-switch-mediated immortalization of expression of lines SS02130 and SS02121 late in larval life. Download elife-80594-fig3-data4-v2. pptx Figure 3—source data 5 Examples of the adult anatomies of larval neurons MBON-h1 and -h2 obtained by flip-switch-mediated immortalization of expression of line SS01725 late in larval life. Download elife-80594-fig3-data5-v2. pptx Figure 3—source data 6 Examples of the adult anatomies of larval neurons MBON-j1 and MBON-j2 obtained by flip-switch-mediated immortalization of expression of lines SS01973 and SS00860 late in larval life. Download elife-80594-fig3-data6-v2. pptx Figure 3—source data 7 Examples of the adult anatomies of larval neurons MBON-i1 and MBON-k1 obtained by flip-switch-mediated immortalization of expression of lines SS01962 and SS04236 late in larval life. Download elife-80594-fig3-data7-v2. pptx Table 1 Metamorphic fates of larval mushroom body extrinsic neurons. Larval nameCompartmentLineage *Adult identityRef for adult identity-a1,a2CXVUM†OA-VUM2aBusch et al.

Output: pass


Input: Claim: Fossils provide insights into the evolutionary history of life. [SEP] Text: Thus, we are unable to answer whether there is continuous distribution of haplotype frequencies with a clinal transition between geographic extremes, whether there are two allopatric ranges separated by a distinct geographic gap, or whether the ranges meet at a distinct zone of parapatry or sympatry. Because of the extent of the genetic dissimilarity, we are rather skeptical about the first alternative. Rather, we expect that clades I and II are entities separated at species level. We propose a separate species status for the two clades as per the genetic species concept [20–22] which sets a cutoff based on empirical data (cytochrome b in the order Chiroptera) of about 5% of corrected sequence divergence [e. g. [23, 24]]. These two groups have almost double that divergence with 9% corrected divergence. (ii) Within the clade II, a divergence of about 3% separates African and Asian haplotypes of R. hardwickii. Within the African group, our genetic data contradict the groupings proposed by previous studies [see [8, 9, 17] and [25]], which stress a separate status for the populations of the central Sahara (including that of Upper Egypt). The genetic relatedness of these small bats to the largest form in Libya suggests an unexpected degree of phenotypic plasticity in these bats, apparently driven by temporary local conditions rather than by the genotypic backgrounds of the respective populations. Here, the selection pressures of the extreme conditions of desert habitats may have played a key role. A similar pattern of morphological bimodality has been observed in other desert or semi-desert species of bats [16, 25, 26], such as Taphozous nudiventris Cretzschmar, 1830, Rhinolophus clivosus Cretzschmar, 1828, Asellia tridens (Geoffroy, 1813), or Pipistrellus kuhlii (Kuhl, 1817), and such an explanation could be also invoked with respect to the smaller Arabian form, Rhinopoma microphyllum asiriensis Nader and Kock, 1983. (iii) Our study has revealed that the morphospecies R. muscatellum is composed of two distinct clades: clade III in Yemen and clade IV in Iran and, supposedly, in Oman.

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: 17. Bonfante P, Genre A. Arbuscular mycorrhizal dialogues: do you speak ‘plantish’ or ‘fungish’? Sci 2015;20:150-4. 18. Maillet F, Poinsot V, André O, Puech-Pagès V, Haouy A, Gueunier M, et al. Fungal lipochitooligosaccharide symbiotic signals in arbuscular mycorrhiza. Nature 2011;469:58–63. 19. -Pagès V, Novero M, Rey T, et al. Short-chain chitin oligomers from arbuscular mycorrhizal fungi trigger nuclear Ca2+ spiking in Medicago truncatula roots and their production is enhanced by strigolactone. 2013;198:179–89. 20. . Arbuscular mycorrhizal fungi elicit a novel intracellular apparatus in Medicago truncatula root epidermal cells before infection. 2005;17:3489–99. 21. . Life histories of symbiotic rhizobia and mycorrhizal fungi. 2011;21:R775–85. 22. . A phosphate transporter from Medicago truncatula involved in the acquisition of phosphate released by arbuscular mycorrhizal fungi. 2002;14:2413–29. 23. The membrane proteome of Medicago truncatula roots displays qualitative and quantitative changes in response to arbuscular mycorrhizal symbiosis. J Proteomics 2014;108:354–68. 24. Raudaskoski M, Kothe E. Novel findings on the role of signal exchange in arbuscular and ectomycorrhizal symbioses. Mycorrhiza 2014;25:1–10. 25. . Truffles regulate plant root morphogenesis via the production of auxin and ethylene. 2009;150:2018–29. 26. Plett JM, Martin F. Blurred boundaries: lifestyle lessons from ectomycorrhizal fungal genomes. 2011;27:14–22. 27.

Output: corroborate


Input: Claim: Some insects can undergo complete metamorphosis, transitioning through larval stages. [SEP] Text: Consequently, more of the neuron types needed to make their MB are available to these embryos, likely making these insects less dependent on appropriating other neurons to temporarily function in the larval MB. A higher number of MB neurons persisting from the larva to adult increases the likelihood that a memory trace could persist from one stage to the other. Metamorphic changes of the larval neurons Although there are examples of neurons that change their neuropeptides during postembryonic life (Veverytsa and Allan, 2012), our study did not find any neuron that changed its small molecule transmitter. The neurons did, though, show a great range of morphological changes. At one end of the spectrum were neurons like DAN-d1/PPL1 03 (Figure 3B) and MBON-j1/MBON 02 (Figure 4B), whose larval and adult forms are very similar. At the other end of the spectrum are MBIN-b/PAL-OL and MBON-g/LALs-NO2i. b (Figure 4B), which trans-differentiate into adult neurons that bear no similarity to their larval forms. Neurons possessing the same form in both the larval and adult stages are like those of direct developing insects because they undergo their full developmental trajectory during embryogenesis and achieve their mature form at hatching. Other larval neurons have a morphology that appears to be based on pausing their developmental trajectory at an intermediate step and using this intermediate form as the basis for their larval morphology. The larval octopaminergic cells, OAN-a1 and -a2, fit this pattern. Their larval neurons stop at the MB calyx but in their adult form (OA-VUMa2), they extend beyond the calyx to form major arbors in the lateral horn neuropils (Busch et al. , 2009). A similar strategy occurs for thalamic neurons in the developing mammalian visual system (Kanold et al. , 2003). These early-born neurons arrive at the cortical subplate prior to the birth of their granule cell targets in the visual cortex. They assume an intermediate phenotype, synapsing with the subplate neurons, but after the granule cells are born, they lose these temporary connections and grow into the cortex to find their final targets.

Output: contrast


Input: Claim: Fossils provide insights into the evolutionary history of life. [SEP] Text: Evolutionary historyThe evolutionary history of Rhinopomatidae is a subject of particular interest, one which makes the group one of the most enigmatic clades of chiropterans. In the traditional view, Rhinopomatidae were regarded as the most primitive group of extant bats, the closest to the common ancestor of microbats and megabats [12, 14]. Indeed, compared to other families of Yangochiroptera and Yinpterochiroptera, the family Rhinopomatidae exhibits a set of unique plesiomorphies: (i) the trochiter of the humerus (tuberculum minor) is small and does not permit the scapulo-humeral lock found in other bats; (ii) the wing tip index has the lowest value of all Chiroptera; (iii) medial phalanx of the second wing finger is complete and well-ossified; (iv) the last cervical and first throracic vertebrae are free (not fused as in other bats); (v) individual sacral vertebrae have distinct boundaries; (vi) the uropatagium is incomplete; (vii) the calcar is absent; (viii) tail is long and mouse-like, not entirely integrated to the uropatagium; (ix) the premaxillae are not attached to each other or to maxillae; and (x) the premaxillae are developed at the palatal plane only. A few of these characteristics (i, iv, vii) are shared with Craseonycteridae, while the others are unique among both Yangochiroptera and Rhinolophoidea, partly resembling the condition in Pteropodidae (i, iii, iv, v, vi, ix, partly vii, viii, x). In contrast to the major clades of Yinpterochiroptera [cf. [3, 15]], Rhinolophidae (1 genus, ca. 77 species), Hipposideridae (9 genera, ca. 81 species), and Pteropodidae (42 genera, ca 184 species), the family Rhinopomatidae is much less diversified [15].

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: Effector MiSSP7 of the mutualistic fungus Laccaria bicolor stabilizes the Populus JAZ6 protein and represses jasmonic acid (JA) responsive genes. 2014;111:8299–304. 28. Convergent losses of decay mechanisms and rapid turnover of symbiosis genes in mycorrhizal mutualists. 2015;47:410–5. 29. Balestrini R, Gómez-. Application of to plant pathogenic and symbiotic interactions. J 2009;4:81–92. 30. Recorbet G, Dumas-Gaudot E. Proteomics of biotrophic plant–microbe interactions: symbioses lead the march. : & Sons, Inc; 2008. p. 764. 31. . Effect of fungal-isolate aggressivity on the biosynthesis of symbiosis-related polypeptides in differentiating eucalypt ectomycorrhizas. Planta 1995;195:408–17. 32. O'Farrell PH. High resolution two-dimensional electrophoresis of proteins. J 1975;250:4007–21. 33. Felten J, Martin F, Legué V. Signalling in Ectomycorrhizal Symbiosis. In: Perotto S, Baluška F. (eds. ) Signaling and Communication in Plant Symbiosis. Berlin, Heidelberg: Heidelberg; 2012. 42. 34. Ditengou FA, Müller van Doorn MM, et al. Volatile signalling by sesquiterpenes from ectomycorrhizal fungi reprogrammes root architecture. 2014;6:6279–9. 35. de A novel class of ectomycorrhiza-regulated cell wall polypeptides in Pisolithus tinctorius. 1999;12:862–71. 36. Bonfante P, Genre A. Mechanisms underlying beneficial plant–fungus interactions in mycorrhizal symbiosis. 2010;1:1–11. 37. -Gaudot E. Mutations in DMI3 and SUNN modify the appressorium-responsive root proteome in arbuscular mycorrhiza. 2006;19:988–97. 38. Lopez-Meyer M, Harrison MJ.

Output: corroborate


Input: Claim: Some insects can undergo complete metamorphosis, transitioning through larval stages. [SEP] Text: No fertilised eggs or embryos have been sequenced, individual tissues derived from juvenile fish were used for the genome annotation and whole larval fish for the developmental analysis. We will specify in the figures and text that the results shown are from whole larvae, and add more detail to the material and methods section about which type of sample was analysed in which way. The bioinformatic processing, especially of the assemble and annotation, is very poorly described. This is also a sensitive topic, as illustrated by the numerous "assemblathon" and "annotathon" initiatives to evaluate tools and workflows. Importantly, providing configuration files and in-depth description of workflows and parameter settings is highly recommended. This can be made available through data store services and documents even benefit from DOIs. This provides others with more information to evaluate the resolution of this work. No doubt that it is well done,but especially in the field of genome assembly and annotation, high resolution is VERY cost and time-intensive. Not surprisingly, most projects are conditioned by trade-offs between cost, time, and labor. The authors should provide others with the information needed to evaluate this. We will upload the code used to assemble and annotate this genome to a public repository or add it to the supplementary material. The genome assembly did not use a specific workflow (e. g. , nextflow), but was done with a simple command and standard parameters in IPA. Scaffolding was carried out by using their standardised proprietary workflow, of which a detailed description provided by can be found in the supplementary material. The annotation workflow has been described in a previous publication already, but an in-depth description can also be found in the Material and methods section, including parameters used for specific steps. The RNA-seq mapping and analysis part has also been described in the Material and Methods section, including parameters and models for DESEq2. Quantifications of T3 and T4 levels look fairly low and not so convincing. The work would clearly benefit from a discussion about why the signal is so low and what are the current technological limitations of these quantifications. This would really help (general) readers. We will add a comment on this in the manuscript as suggested. Basically, the T3/T4 levels are consistent with other published work in fish. In the present manuscript for grouper we have a peak level of 1. 2 ng/g (1,200 pg/g) of T4 and 0.

Output: pass


Input: Claim: Fossils provide insights into the evolutionary history of life. [SEP] Text: The fact that the phenotype of Rhinopomatidae (similarly as in Craseonycteridae) is composed almost exclusively of the ancestral characters not affected by adaptive rearrangements common in other chiropteran families, in contrast to other chiropteran families [42], suggests that (i) the clade was established at a very early stage of chiropteran radiation (prior to the first appearances of modern families, in the or earlier), and (ii) that rhinopomatids were relatively little affected by the same adaptive processes that affected other all bat families, which all evolved under constant competitive pressure from other microbat clades. The latter could happen only under conditions of long-term isolation of rhinopomatids from other bats. An analogous case is the extinct clades of Palaeochiropterygidae and Hassianycteridae, which extensively diversified in during geographic isolation of that region in the Early and [43, 44]. These endemic groups were radically replaced by modern bat families soon after their invasion during the "grand coupure" [33]. The respective modern clades, Hipposideridae, Rhinolophidae, modern Emballonuridae, Vespertilionidae, Molossidae [comp. [29, 31]], arrived either from Africa and or Asia, and their early radiations most probably took place there (comp. also [3] for molecular support to that hypothesis). The complete absence of rhinopomatids in the fossil record and the lack of coevolutionary influence on their phenotype suggest that this group was absent in Europe and probably also in Africa and Asia. Of course, Tanzanycterididae with Tanzanycteris mannardi from the (46 Ma) of Tanzania [45] may ultimately be shown to be closely related to Rhinopomatidae. Unfortunately the characters available in the specimen of Tanzanycteris provide only tentative support for such a possibility (e. g. enlarged cochlea, a lack of scapulo-humeral lock which is, common to all other Eocene bats). The first appearance datum of Megadermatidae s. str. is nearly synchronous with dramatic rearrangements of the European mammalian fauna, with the appearance of the Asiatic elements (e. g.

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: An experimental system to synchronize the early events of development of the arbuscular mycorrhizal symbiosis. In: Sánchez F, Quinto C, López-Lara IM, Geiger O. (eds. ) Molecular Plant–Microbe Interactions. Saint Paul: for Plant-Microbe Interactions; 2006. 551. 39. Bestel-Corre G, Dumas--F, van Tuinen D, et al. Proteome analysis and identification of symbiosis-related proteins from Medicago truncatula Gaertn. by two-dimensional electrophoresis and mass spectrometry. Electrophoresis 2001;23:122–37. 40. . Proteome changes in Oncidium sphacelatum (Orchidaceae) at different trophic stages of symbiotic germination. Mycorrhiza 2014;24:349–60. 41. . Membrane proteins and membrane proteomics. 2008;8:3924–32. 42. Benabdellah K, Azcón-Aguilar C, Ferrol N. Soluble and membrane symbiosis-related polypeptides associated with the development of arbuscular mycorrhizas in tomato (Lycopersicon esculentum). 1998;140:135–43. 43. Benabdellah K, Azcon-Aguilar C, Ferrol N. Alterations in the plasma membrane polypeptide pattern of tomato roots (Lycopersicon esculentum) during the development of arbuscular mycorrhiza. J 2000;51:747–54. 44. -Gaudot E. Identification of membrane-associated proteins regulated by the arbuscular mycorrhizal symbiosis. Biol 2005;59:565–80. 45. Maldonado-van. Methods to estimate the proportion of plant and fungal RNA in an arbuscular mycorrhiza. Mycorrhiza 2002;12:67–74. 46. -Gaudot E. A mass spectrometric approach to identify arbuscular mycorrhiza-related proteins in root plasma membrane fractions. 2006;6(Suppl 1):S145–55. 47. .

Output: corroborate


Input: Claim: Some insects can undergo complete metamorphosis, transitioning through larval stages. [SEP] Text: Such morphological changes have already been associated as corresponding to metamorphosis in groupers (et al 1998) as they occur during TH level increase, and they also happen to be under the control of TH in grouper (et al 1998). Based on this study but also on studies (conducted on many other teleost species) showing that the increase of TH levels is always associated with an activation of TH pathway genes and morphological and pigmentation changes we concluded that metamorphosis of E. malabaricus occurs between D32 and juvenile stage. We will improve the clarity of the manuscript to make sure that our conclusion is based on our transcriptomic and morphological data plus the available literature. (2) We clearly observed another activation of TH related gene earlier in the development (between D1 and D10, with a surge of trhrs, tg and tpo at D3. As this activation was very unexpected for us, we decided to focus the analysis of TH levels between D1 and D10 and very interestingly we observed high level of T4 at D3 indicating that THs are instrumental very precociously in the larval development of the malabar grouper which has never been shown before. We declared line 195 that our “data reinforce the existence of two distinct periods of TH signalling activity, one early on at D3 and one late corresponding to classic metamorphosis at D32”. However, we agree that we could have been clearer and clearly explained that this early activation was very intriguing for us and that we wanted to investigate hormonal levels around that period. However, we never claimed anywhere in the manuscript that this early developmental period corresponds to metamorphosis. Something else is occurring and both TH and cortisol seem to be involved but further experiments need to be conducted to understand their role and their possible interaction. (3) Finally, regarding the comment about cortisol enhancing or participating in TH driven metamorphosis, our data clearly showed an activation of the corticoid pathway genes around metamorphosis (between D32 and juvenile stage) suggesting a potential implication of corticoids in metamorphosis, but we agree with the reviewer that further experiment are needed to test that. We never claimed that cortisol was enhancing or participating in metamorphosis, on the contrary we are “suggesting a possible interaction between TH and corticoid pathway during metamorphosis”.

Output: contrast


Input: Claim: Fossils provide insights into the evolutionary history of life. [SEP] Text: Based on the above discussion, we proposed the following biogeographic hypothesis (Figure 5):Figure 5Palaeogeographic situation of the Indian-region in the Late Eocene/(35 My) and in the Miocene (20 My), with expected expansion pathways of three major lineages (sensu [9]) of the family. The paleogeographic background maps were compiled by Ron Blakey, Department of Geology, University, Flagstaff [66]. Printed with permission. Full size image(i) Rhinopomatidae originated during the Eocene from the early diversification of rhinolophoid bats that remained isolated from competitive pressure of other chiropteran clades somewhere in the archipelago south of the seaway or in India. (ii) The group evolved in isolation until the Oligocene when the marine barrier between the Mediterranean and provinces disappeared. Endemic adaptation to major chiropteran foraging strategies in ancient rhinolophoids produced clades whose ecomorphological design was much different corresponding foraging specialists on neighbouring continents. Some of those ancient clades survive now: rhinopomatids as aerial foragers, craseonycterids as foragers of small prey in cluttered habitats, and megadermatids as ground gleaners. (iii) The land between Iran and western India, uplifted during the Oligocene, was subsequently invaded by rhinopomatids and became the key location of their early Neogene radiation. The westward invasion was from the south, then to Arabia (which came in contact with the Iran belt some 20 Ma ago) with the R. muscatellum lineage, and perhaps later to the northern part of the Iran and to the Mediterranean with the R. hardwickii lineage. The paleobiogeographic analyses of rodents [52] suggest that the respective westward migrations may begun even much earlier – in the late Eocene and early Oligocene via archipelagos south of the seaway. (iv) Extension of the paleogeographic and paleoenvironmental rearrangements of the during the Vallesian and Turolian stage, ca. 11 Ma [compare with [53–55]], fixed the already established divergences among the clades within both R. hardwickii and R. muscatellum lineages.

Output: pass


Input: Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: Live-cell imaging reveals periarbuscular membrane domains and organelle location in Medicago truncatula roots during arbuscular mycorrhizal symbiosis. 2009;151:809–19. 48. A H+-ATPase that energizes nutrient uptake during mycorrhizal symbioses in rice and Medicago truncatula. 2014;26:1818-30. 49. Kobae Y, Hata S. Dynamics of periarbuscular membranes visualized with a fluorescent phosphate transporter in arbuscular mycorrhizal roots of rice. Physiol 2010;51:341–53. 50. . truncatula half-ABC transporters are essential for arbuscule development in arbuscular mycorrhizal symbiosis. 2010;22:1483–97. 51. Rhizobium-legume symbiosis shares an exocytotic pathway required for arbuscule formation. 2012;109:8316–21. 52. -M. Mass spectrometry-based label-free quantitative proteomics. J 2010;2010:1–6. 53. Dumas---Arnaud M, Fontaine B, et al. Proteomics as a way to identify extra-radicular fungal proteins from Glomus intraradices-RiT-DNA carrot root mycorrhizas. Ecol 2004;48:401–11. 54. Recorbet G, Rogniaux H, Gianinazzi-Pearson V, Dumasgaudot E. Fungal proteins in the extra-radical phase of arbuscular mycorrhiza: a shotgun proteomic picture. 2009;181:248–60. 55. . rhizogenes pRi TL-DNA segment as a gene vector system for transformation of plants. Genet 1987;207:251–5. 56. . A homologue of the cell cycle check point TOR2 from Saccharomyces cerevisiae exists in the arbuscular mycorrrhizal fungus Glomus mosseae. Protoplasma 2000;212:89–98. 57. -Hill Y, Lammers PJ. Expression in an arbuscular mycorrhizal fungus of genes putatively involved in metabolism, transport, the cytoskeleton and the cell cycle. In:. (eds.

Output: corroborate


Input: Claim: Some insects can undergo complete metamorphosis, transitioning through larval stages. [SEP] Text: We have improved the clarity of the manuscript in several places to make sure that our conclusion is based on our transcriptomic and morphological data plus the available literature. (2) We clearly observed another activation of TH related gene earlier in the development between D1 and D10, with a surge of trhrs, tg and tpo at D3. As this activation was very unexpected for us, we decided to focus the analysis of TH levels between D1 and D10 and very interestingly we observed high level of T4 at D3 indicating that THs are instrumental very precociously in the larval development of the malabar grouper which has never been shown before. We declared lines 224-225 that our “data reinforce the existence of two distinct periods of TH signalling activity, one early on at D3 and one late corresponding to classic metamorphosis at D32”. However, we agree that we could have been clearer and clearly explained that this early activation was very intriguing for us and that we wanted to investigate hormonal levels around that period. However, we never claimed anywhere in the manuscript that this early developmental period corresponds to metamorphosis. Something else is occurring and both TH and cortisol seem to be involved but further experiments need to be conducted to understand their role and their possible interaction. We have added corresponding statements in the abstract (lines 39-43) and discussion (lines 447 to 449). (3) Finally, regarding the comment about cortisol enhancing or participating in TH driven metamorphosis, our data clearly showed an activation of the corticoid pathway genes around metamorphosis (between D32 and juvenile stage) suggesting a potential implication of corticoids in metamorphosis, but we agree with the reviewer that further experiment are needed to test that. We never claimed that cortisol was enhancing or participating in metamorphosis, on the contrary we are “suggesting a possible interaction between TH and corticoid pathway during metamorphosis”. And we also say that our “results brought a first insight into the potential role of corticoids in the metamorphosis of E. malabaricus and call for functional experiments directly testing a possible synergy. ” Nonetheless, we agree that some parts of our manuscript can be confusing in regards of cortisol synthesis during metamorphosis as we did not measure cortisol levels between D32 and juvenile stage. We have therefore made changes throughout the Introduction and Discussion to make this clearer.

Output: contrast