Input: Task: contrast the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: 001), but showed no such effect when tested on CNO (CS period x CS type interaction, p =. 684). In contrast, analysis of data from the mCherry group (excluding hM4Di data) found a CS+ specific increase in lever pressing (CS Period x CS Type interaction, p <. 001) that was not significantly altered by CNO (Drug x CS Period x CS Type interaction, p =. 780). This null effect in the mCherry group is in line with a recently published study led by Dr. (coauthor) that systemic CNO treatment does not significantly disrupt expression of the PIT effect in -free rats (Collins et al. , 2019), which is cited in the current manuscript. It is also important to note that the same systemic CNO treatment did not significantly affect lever-press performance in Experiment 4 (Figure 5B). Based on these results and others noted below, we believe our original conclusions are strongly supported by the data, though we agree that they should be discussed in the context of potential nonspecific CNO effects. We have also added an acknowledgement of the potential for CNO microinfusions to produce a partial nonspecific suppression in lever pressing in the mCherry rats used in Experiment 3B (subsection “Surgery”; Figure 4—figure supplement 2). Importantly, this effect did not reach significance (Drug * Site * CS Period * CS Type interaction, p =. 068) and was driven by intra-mPFC (not NAc) CNO injections. Moreover, further analysis revealed that CNO did not significantly disrupt the CS+ induced increase in lever pressing (PIT score) in either group (p’s >. 165). These findings indicate that the disruption of PIT detected in Experiment 3A (Figure 4C and 4D) following intra-NAc CNO injections in rats expressing hM4Di in VTA dopamine neurons resulted from local dopamine terminal inhibition and not a nonspecific action of that drug. As detailed below, we believe that reviewer #2’s separate concern about effect sizes stems partly from variability in lever press rates across groups, drug conditions, and CS periods, which tended to obscure the effect of chemogenetic dopamine neuron inhibition on the increase in lever pressing that is specifically attributable to noncontingent CS+ presentation. [SEP] Justify:

Output: The text contrasts the claim by providing evidence that the disruption of the PIT (Premack Intention Test) detected in the experiment was not due to the chemogenetic dopamine neuron inhibition, but rather from local dopamine terminal inhibition. This indicates that the perceived effects on reward processing and motivation may not solely be linked to dopamine, as the results showed that the systemic CNO treatment did not significantly affect lever-pressing performance or the CS+ induced increase in lever pressing in either tested group. This suggests a more complex relationship between dopamine and the reward processing being investigated.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: I chose to use the commercial software MEGAX [46] (see Methods) for inferring phylogenetic relationships since it is more accurate for evaluating clusters among different input-proteins lengths [47, 48] within specific species. The tree showed the clusters and allowed to identify the putative conserved elements. The analysis was performed 100 times: with reference to Additional file 1, the bootstrap is shown in the tree as the number of events in which that particular cluster has been reached. This gives a probabilistic demonstration high protein conservation and the results were significant in a number of more than 75 times [46]. However, I considered bootstrap values between 11 and 50 as acceptable putative homologous since there are errors caused by the “multiple noise interferences” in the clustering due to the effect of a broad sampling of different protein sequences lengths – “weights”. Eventually, a number of 180 proteins in B. rapa Pekinensis group were noted in a table as potential chloroplast carotenoid orthologs (Additional file 15: Table 3, see also Additional file 2). GO terms of putative carotenoid orthologsThe list of 180 potential orthologs resulted from the phylogenetic analysis was studied for GO terms enriched groups cataloged for molecular function, since the previous deterministic filter was affected by some noise due to the broad amount of data analyzed. Among the GO terms found to be associated with the 180 putative orthologs, the following were related to oxygenic photosynthesis: carotenoid dioxygenase activity [GO:0010436], oxidoreductase activity [GO:0016730], metal ion binding [GO:0046872], deoxy-D-xylulose-5-phosphate synthase activity, [GO:0016744], transferase activity, transferring aldehyde or ketonic group, GO:0102067- geranylgeranyl diphosphate reductase activity, carotenoid isomerase activity [GO:0046608], oxidoreductase activity [GO:0052887], farnesyl-diphosphate farnesyltransferase activity [GO:0004310]. The 180 elements were additionally analyzed according to the for checking the functional domains by means of Models when available (Additional file 15: Table 3) [8, 49]. [SEP] Justify:

Output: The text does not directly address the claim regarding the importance of bioinformatics for analyzing genomic data. It discusses the use of software for inferring phylogenetic relationships and the analysis of protein sequences, but it does not specifically argue that bioinformatics is crucial or provide evidence that supports the claim.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: We also observed a significant improvement on run time for cached iterative computations (K-means clustering), compared to non-cached ones (Fig. 2K). 3. 1. 5 Fragment recruitment comparison with Metahave compared Sparkhit-recruiter to another Spark based distributed fragment recruitment tool (Fig. 3), MetaSpark (Zhou et al. , 2017). Sparkhit-recruiter ran 92–157 times faster than MetaSpark across different numbers of worker nodes (10, 20 and 30), different numbers of input reads (1 million and 6 millions) and different sizes of reference genomes (72 and 142 MB). Although our tool recruited 10–12% reads less than MetaSpark using the same K-mer size, we have adjusted to a smaller K-mer size that recruits more reads than MetaSpark, while still ran 47–124 times faster (see Discussion section). 3. 1. 6 Accuracy and sensitivity of natively implemented tools To assess the accuracy and sensitivity of Sparkhit-recruiter and Sparkhit-mapper, we used public datasets from the Genome in a (GIAB) (Zook et al. , 2014) and simulated 6 datasets (doi: 10. 4119/unibi/2914921) from three microbial reference genome templates (Ref-1, Ref-2 and Ref-3; Supplementary file S2, Table S2). In general, Sparkhit-recruiter has slightly higher accuracy than Fr-hit, Bowtie2 and SOAP, while having slightly lower accuracy than BWA (Fig. 2M–L and Supplementary file S2, Table S1). For sensitivity, Sparkhit-recruiter, Fr-hit, Bowtie2 and BWA are higher than SOAP on 100 nt simulated reads. For 150 nt simulated reads, all mappers have similar sensitivities. Sparkhit-mapper has slightly higher accuracy and sensitivity than Sparkhit-recruiter on the GIAB data. 3. 2 Large scale genomic data analyses on the cloud To demonstrate that large scale genomic analyses on the cloud can be easily accessible with Sparkhit, we tested our framework on 100 TB of genomic data from 3 genome projects and a transcriptomics study on the Amazon EC2 cloud (Fig. 4). [SEP] Justify:

Output: The text corroborates the claim by demonstrating the importance of bioinformatics tools, like Sparkhit-recruiter, in the analysis of large-scale genomic data. It specifically mentions that the framework was tested on 100 TB of genomic data, which illustrates the application of bioinformatics in genomic data analysis, supporting the assertion that it is crucial for such tasks.


Input: Task: contrast the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: It responds to pain and attention-demanding tasks [3], during response conflict and inhibition of prepotent responses [4], and it activates the sympathetic system, heart rate, and autonomic response to physical and mental stress [5–7]. The ACC was divided [8] based on its connectivity and control of goal-directed behavior, into cognitive dorsal (Brodmann area (BA) 24′, 32′) and affective ventral (subcallosal) regions (BA 25, 24, 33), with pregenual ACC (pgACC) in between. dACC was linked to premotor response selection, noxious stimuli, effortful processing, and motivation, while vACC was linked to rest and parasympathetic activation [8]. We summed up what the numerous observed dACC activations have in common. It seems to identify danger, harm, potential risks, and bad or suboptimal outcomes of our actions, decisions, and predictions. We propose that the main computational role of dACC is to generate a warning signal (Beware! Attention! ) toward the real or predicted bad outcomes – to call for changes in the current course of actions and behaviour; to prevent pain, harm, loss, or failure; and also to prod for rethinking and updating the wrong or faulty strategies, predictions, guesses or cognitive models of the world around us. This warning signal in the dACC is issued when things are going bad for us – leading to negative consequences: to increase our precaution, engagement with the problem, attentional focus, and cognitive motor mobilization – to improve our performance and adjust our behavior. Therefore, the purpose of the dACC is to learn and predict the bad consequences of current actions and decisions, to warn us about harms, risks, and failures (errors in actions, reasoning, thoughts, plans) to prevent bodily harm and personal loss. It also urges us to update/adjust the wrong, suboptimal, or inappropriate behavior, strategies, and plans – when getting negative feedback. Thus, the dACC detects danger and potential risk to prevent bad consequences and harm incurred by current actions, decisions, and behavior yet to be conducted. The proposed warning signal, generated by the dACC, resembles the negative surprise or unexpected uncertainty signal [9] found to drive noradrenaline release. [SEP] Justify:

Output: The text contrasts the claim by discussing the role of the dorsal anterior cingulate cortex (dACC) in identifying danger and signaling negative outcomes. It emphasizes the dACC's function in preventing harm, improving performance, and adjusting behavior in response to negative feedback rather than focusing on dopamine's role in reward processing. The insights highlight how attention to potential risks and harms deviates from the claim about dopamine being tied to motivation and reward.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The following Pfam domains were found: amino_oxidase (PF01593) and carotenoid oxygenase (PF03055), the two ones associated with many elements. Furthermore, Pyr_redox_2 (PF07992) includes families of oxyreductase and it was associated with one protein element. Based on the GO and Pfam analysis described above, the orthologs that did not play a role in oxygenic photosynthesis, labeled in Additional file 15: Table 3 in yellow, were discarded, which resulted in 44 potential orthologs as the result of my analysis (see Additional file 16: Table 4). The association between each protein ID and GO term or Pfam term is represented by the means of two heatmaps, i. e. two color-coded matrices where blue and yellow represent the absence or presence of a specific GO for a given protein ID, respectively (Fig. 4, see also Additional file 2, in orange the removed elements; see also Additional file 10 for the code). Fig. 4GO terms and Pfam domains in the 44 identified orthologs. The heat maps visualize the metabolites involved in the oxygenic photosynthesis. The 44 elements are indicated in the y axis. GO terms (A) and Pfam domains (B) are in the x axis. BRARP = Brassica rapa subsp. pekinensis, BRACM = Brassica rapa campestris var. pekinensis. and BRACM are cataloged as B. rapa Pekinensis group. The association of a specific GO term or Pfam domain with each of the proteins is indicated in yellowFull size imageForest analysis coupled with phylogenetic analysisAs mentioned above, when using the Uniprot database as a starting source, 44 proteins in B. rapa Pekinensis group were identified as highly conserved putative chloroplast protein orthologs involved in carotenoid biosynthesis. Next, a “population” analysis has been applied: here and hereinafter the use of the term “population” refers to “all related protein from a species”. I wanted to apply a population analysis by using reviewed sequences obtained from (TAIR)[50]. For this purpose, I used the total Pekinesis group chloroplast filtered population in order to help the localization of all the pathways that produce isoprenoids. [SEP] Justify:

Output: The text discusses specific findings related to Pfam domains and protein elements, as well as a population analysis of protein sequences in a certain plant group. However, it does not explicitly address the importance of bioinformatics in the analysis of genomic data nor does it provide insight into its significance. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: This analysis took 10:49:54 h (Fig. 4C and Supplementary file S2, Table S5). 3. 2. 3 Mapping 106 samples of the 1000 The 1000 hosts all WGS data on Amazon S3 (Virginia region). It provides a detailed catalogue of human genetic variants in the studied populations. Before variant detection, a mapping step is required to present all matches and mismatches on the reference genome. To test the run time performance of Sparkhit on whole genome sequence mapping, we downloaded 5. 6 TB compressed sequencing data to the Spark cluster. invoking BWA (Li and Durbin, 2009), we mapped 106 samples to a human reference genome. After sequence mapping, all results (SAM format) were uploaded to Amazon S3 for persistent storage. The entire process was completed in 5:31:17 h (Fig. 4D and Supplementary file S2, Table S6). 3. 2. 4 Gene expression profiling on prostate cancer RNA-seq data Profiling tumor specific gene expression is a critical step for cancer research. To enable fast transcriptome quantification on the cloud, we tested the run time performance of Sparkhit on profiling 3. 2 TB compressed RNA-seq data of a prostate cancer transcriptomics study (Wyatt et al. , 2014). Since all datasets are archived on the ENA ftp server (see Supplementary file 1 Materials), the downloading process consumed 1:29:28 h, whereas gene expression profiling with Sparkhit invoked Kallisto (Bray et al. , 2016) was completed in 21 min and 9 s (Fig. 4E and Supplementary file S2, Table S7). After uploading the final result to Amazon S3, we shut down all EC2 instances. 3. 3 Recruiting HMP metagenomic data to microbial reference genomes Metagenomics can capture and obtain genome fragments of uncultivated microbes (the microbial dark matter) by using shotgun sequencing to aggregated microorganisms sampled directly from the environment (Rinke et al. , 2013). applied metagenomic shotgun sequencing to characterize microbial communities at different human body sites. As all HMP data is hosted on Amazon S3, we can directly access and analyze these metagenomic datasets on the cloud. To demonstrate that associating HMP metagenomic data with private datasets (e. g. [SEP] Justify:

Output: The text corroborates the claim by highlighting the process involved in genome sequence mapping and gene expression profiling, essential components of bioinformatics. It mentions that mapping genomic data requires significant computational resources and steps, emphasizing the importance of bioinformatics tools like Sparkhit and Kallisto for analyzing large datasets, including whole genome sequences and RNA-seq data. This shows that bioinformatics plays a critical role in effectively analyzing genomic data.


Input: Task: contrast the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: But we claim that the dACC warning signal is also issued in response to rewarding or attractive stimuli if there is a need to react quickly – not to miss/lose the passing chance of gain (i. e. , prodding fast reaction to passing pretty woman). The dACC has all the connectivity needed – not only to identify the risky options and inadequate courses of actions to learn to avoid them, but also to react to potential danger by recruiting the alarm response of the brain and body. dACC passes its warning signal via top-down projections to several effector regions (Figure 2): to pre-SMA to make a fast switch of current motor action; to Subthalamic nucleus (STN) to stop inaccurate motor response; to frontal eye field (FEF) and intraparietal cortex to focus our visual and spatial attention; to noradrenergic and cholinergic nuclei to speed up reactivity and increase alertness; to the sympathetic system to increase heart and breath rate in demanding situations; and also to the striatum, LHb, and MDT to bias choice behavior. dACC projects to the stress-related hypothalamic nucleus and reciprocally to the threat-processing amygdala. It provokes, by its robust reciprocal projections with lateral prefrontal cortex (LPFC) and lateral BA 10, an update in failed predictions, decisions, or strategies – to switch from wrong/inadequate behavior to more optimal choices, actions, or performance. The role of lateral BA 10 is to seek to find out a more fitting model, prediction or solution for what is going on, that can be applied to reach our goals and avoid harm. It generates novel guesses, hypotheses, and ideas about studied systems by figuring out relevant interrelations, extracting contingencies, invariances, and rules (“if”… “then”), and explaining incongruities in the facts. Testing the validity of new predictions involves the lateral BA 10, dACC, and dorsal striatum (DS). Figure 2. A causal role of dACC connectivity in enabling propagation of its warning signal. The warning signal generated by dACC is forwarded to specific regions to induce caution, attention, arousal and cognitive-motor mobilization toward potential danger, to prevent suboptimal consequences. This signal is similar to ‘Beware! ’ command and is evoked by demanding or risky situations and tasks. [SEP] Justify:

Output: The text contrasts the claim by focusing on the role of the dorsal anterior cingulate cortex (dACC) in response to rewarding stimuli and highlights its function in signaling caution, attention, and cognitive mobilization rather than directly addressing the role of dopamine in reward processing and motivation. The emphasis on dACC's connectivity and its alerting functions suggests alternative mechanisms at play that may contradict the simplified view that dopamine alone is crucial for these processes.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: A number of 386 elements were used, as result of the filtering steps section. The population analysis retrieved a variety of 47 characterized proteins when using “carotenoid biosynthetic process” as a string query (reviewed on TAIR [50]). These 47 proteins play a role in the MEP pathway and in the oxygenic photosynthesis. Therefore, I had a total of 47 potential A. thaliana orthologs (see Additional file 17: Table 5) that I use as reference sequences to obtain a much more “discriminated” situation already with the single samples. The 47 sequences were aligned versus the chloroplast proteins and carotenoid factors retrieved from Additional file 14: Table 2. The resulting values of each input population against the 8190. 90) is shown. The associated taxa clustered together next to the branches are defined by a normalized scale. The bootstrap is highlighted by pink circles (normalized scale from 0 to 1). The internal nodes are represented by rectangular shape in blue and each protein ID is highlighted with dashed lines. The leaf nodes are displayed with a circular shape. ARA = A. thaliana, RA = B. rapa Pekinensis group are the labels to refer to the species. B The tree with the highest log likelihood (-71352. 70) is shown. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The bootstrap is highlighted by purple circles (normalized scale from 0 to 1). The internal nodes are represented by rectangular shape in green and each protein ID is highlighted with dashed lines. The leaf nodes are displayed with a rectangular shape in red. ARA = A. thaliana, RA = B. rapa Pekinensis group. Four outgroups are also inclided in the tree: Bkt from C. reinhardtii, Pds C. reinhardtii, FNOV0100002 - Brt/brk from H. psycrophilus, P45086A1 - cytochrome P450 from A. thaliana. C The tree with the highest log likelihood (-41791. 08) is presented. The bootstrap is highlighted by purple triangles (normalized scale from 0 to 1). The internal nodes are represented by rectangular shape in green and each protein ID is highlighted with dashed lines. The leaf nodes are displayed with a circular shape in red. [SEP] Justify:

Output: The text does not directly address the claim regarding the significance of bioinformatics in genomic data analysis. Instead, it describes a specific methodological approach involving protein analysis and phylogenetic tree construction without discussing the role or importance of bioinformatics. Thus, it is classified as irrelevant.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: cultured microbial genomes or uncultured single cell genomes) can provide more biological insights, we downloaded and recruited all HMP whole genome shotgun data (2. 3 TB compressed) to 12 selected microbial genomes (Ref-1, see Supplementary file S2, Table S2). Based on the metadata of the HMP samples, we present the fragment recruitment profile of 7 different microbial genomes across 6 different body sites (15 sub-body sites) in Supplementary file S2, Figure S1. The abundance of each microbial genome was normalized and illustrated across different mapping identities (from 75 percent to 100 percent). In general, sub body sites of the same main body site have similar profiles, but with few exceptions. For instance, the abundances of Neisseria meningitides are different between saliva and gingival plaque from the oral body site. Streptococcus aureus has higher abundance in buccal mucosa, attached gingivae and saliva compared to other sub body sites in the oral body site. By changing the input reference genomes, users can easily produce the abundance profile of other microbes (see discussion). 3. 4 Functional study based on HMP fragment recruitment result Mapping metagenomic sequences to a new strain could reveal potential structural variances. A common method for functional studies is based on the hypothesis that such structural variances can have functional impact on the gene level. When recruiting HMP data to the reference genomes, the recruitment result was sensitive enough to pick up structural differences between genome sequences of two E. coli strains (Supplementary file S2, Fig. S1A and B). We, then, followed up a functional analysis, gene prediction and pathway enrichment, based on the sequences (see Supplementary file 1 Method-1. 10). We extracted all gaps on the E. coli O104: H4 strain and predicted 158 new genes. These genes are enriched in two pathogenic pathways: Shigellosis and Pathogenic E. coli infection (Supplementary file S2, Fig. S2E–G). We annotated all genes and identified two toxic genes (Shiga toxin 2 subunit A and B) present in the E. coli strain that caused the 2011 German E. coli outbreak (Rasko et al. , 2011). 4 this study, we presented a Spark based distributed computational framework for large scale genomic analytics, called Sparkhit. [SEP] Justify:

Output: The text supports the claim by discussing the utility of bioinformatics techniques, specifically in analyzing genomic data from various microbial genomes. It mentions methods for mapping metagenomic sequences to reference genomes, which highlights bioinformatics' role in revealing structural variances and conducting functional studies. Additionally, the mention of extracting genes and analyzing pathways using computational frameworks indicates the importance of bioinformatics in genomic data analysis.


Input: Task: contrast the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: Skip to Content eLife home page your research Search by keyword or author Reset form my search to 10. 7554/eLife. 58321 Open access Copyright information Version of RecordNovember 30, 2020 Version of RecordOctober 30, 2020 Accepted ManuscriptOctober 1, 2020 Open annotations (there are currently 0 annotations on this page). 5,646 views 398 downloads 23 8321. Copy to clipboard Download BibTeX Download. RIS Full text Figures and data Peer review Side by side Peer review process Decision letter Author response Peer review process This 2. In relation, the authors should further interpret the fact that HC subjects differ from PD patients on measures of amplitude, saccadic RT and endpoint variability, but seem to resemble PD ON for peak velocity residuals (as shown in Figure 2). While the authors briefly discuss the negative results in HC subjects, it is not clear why there is no effect of motivation in these subjects, as one might expect. The pattern observed in HC subjects might help to explain the data from the PD subjects and therefore should be better incorporated into the manuscript. HC data should be included in Figure 3 and the accompanying analyses, as well. 4) Additional "discussion should be provided regarding the conclusion that these data suggest that dopamine is necessary only for model-based, and not model-free learning; as there are other potential explanations; especially since the HC data don't seem to support model-based learning in this task. 5) Considerable parts of the Introduction and Discussion should be rewritten. Specifically, the data do not undermine the (i) theory that dopamine is key for reward expectation (Abstract: “challenging the theory that tonic dopamine encodes reward expectation”) or (ii) average reward rate (Discussion: “this contrasts with previous research which suggests tonic dopamine encode the average reward rate”) or (iii) model-free behavior (“thus the work fits with previous research in which dopamine is necessary only for model-based, not model-free learning”). These conclusions are not supported by the current findings, because: i) Detrimental effects of dopamine implicate dopamine as much as do beneficial effects as the authors conclude later on in the discussion. ii) Average reward rate was not manipulated and its effects not measured. Related to this issue, I do not think that the Niv view is optimally characterized by attributing to it the prediction that dopamine would leave contingent motivation unaltered. [SEP] Justify:

Output: The text contrasts the claim by discussing various findings that challenge the notion that dopamine is essential for reward processing and motivation. It mentions that the results do not support the conclusion that dopamine is necessary for model-based learning and discusses other theories that dopamine only encodes aspects related to reward expectation, average reward rate, and model-free behavior. The author suggests that detrimental effects of dopamine are just as important to consider as beneficial effects, thereby undermining the claim that dopamine is crucial for reward processing and motivation.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The four outgroups are included as in the previous analysis. ARA = A. thaliana, RA = B. rapa Pekinensis group are the labels to refer to the speciesFull size imageTo attempt to retrieve all the potential syntenic and non-syntenic orthologs in B. rapa Pekinensis group, A. thaliana “reference” carotenoid sequences and B. rapa Pekinensis group potential carotenoid elements were retrieved by using string-searches in Uniprot database, and linked to Uniprot [54–61]. A number of 35 elements from B. rapa Pekinensis group were retrieved from and using the string queries “oxidoreductase” and “reductase” (see Additional file 4). These 35 were added to the 40 elements from B. rapa Pekinensis group obtained from the forest coupled with phylogeny. Following the 75 B. rapa Pekinensis group elements were added to the 49 A. thaliana sequences (see Additional file 5) of putative carotenoid elements involved in the MEP pathway retrieved from Inparanoid, EsemblPlants, RefSeq [62], InterPro via string queries “carotenoid” and “isoprenoids”. Four outgroups were included in the analysis. Outgroups were selected in different organisms where Carotenoids have an important role in the protection of the photosynthetic apparatus, but there are differences in the distribution or regulation of these metabolites [31, 34, 38, 43, 63–66]. The outgroup C. reinhardtii and H. psychrophilus were used to improve the clustering method of the cladograms for better estimating the divergence [48, 67, 68]. C reinhardtii was used since various carotenoids play important roles in response to abiotic stress conditions [36–38]. Additionally, H. psychrophilus, a psychrophilic bacterium, has been introduced as outgroup since the evolution of carotenoid biosynthetic genes have occurred in a different way, probably due to some adoptive mechanisms in cryospheric environment [31, 39]. I finally selected the hemoprotein cytochrome P450 from A. [SEP] Justify:

Output: The text does not address the importance of bioinformatics in analyzing genomic data. Instead, it focuses on the retrieval of genetic sequences and the use of different organisms in analysis, without making a direct connection to bioinformatics being crucial for analysis.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Sparkhit incorporates a variety of tools and methods that are programmed in the Spark extended MapReduce model. We have described (i) the implementation of a fragment recruitment tool and a short-read mapping tool using Spark’s (ii) the construction of a general tool wrapper to invoke and parallelize external tools and (iii) the integration of Spark’s machine learning library for downstream data mining. We also presented the architecture of Sparkhit and the utilities that we used for deploying Spark clusters and downloading public datasets. Sparkhit outperforms most Hadoop and Spark based bioinformatics tools in computational run time. Using our framework, we analyzed large amount of public genomic data on the cloud within a short time. The performance benchmarks demonstrated the scalability of Sparkhit. Sparkhit-recruiter scaled linearly (Fig. 2E) with the increasing amount of input data, as we utilized Spark RDD to balance data distribution and optimized the computational parallelization. In addition, the distributed data I/O via HDFS further reduces latency. On HDFS, data is distributed and loaded locally or from the closest node (depending on the redundancy setting of HDFS), avoiding massive data transfer across the network. We also observed the advantage of using HDFS when comparing the run times of the machine learning library between the Amazon EC2 cloud and the private cluster, which stored input data on an NFS shared by all worker nodes. On NFS, all data was read and written through the network connection to a mounted volume that can saturate the bandwidth. When scaling out to more worker nodes, a slight slowdown was observed (Fig. 2F). The slowdown was caused by the overhead of building the reference index, which runs solely on the driver node. This can be improved by pre-building the reference index using our locally implemented recruiter (a Java-based tool included in our framework). Moreover, the overhead for constructing reference index is small compared to the run time of the fragment recruitment process. Our tool had excellent run time performance on data preprocessing compared to Crossbow (18–32 times faster) and significant run time improvement on fragment recruitment compared to MetaSpark (92–157 times faster). Although we recruited 10–12% less reads than MetaSpark, we can adjust to a smaller K-mer size that recruits slightly more reads than MetaSpark, while still ran 47–124 times faster (Fig. 3). [SEP] Justify:

Output: The text corroborates the claim by detailing how Sparkhit, a bioinformatics tool, leverages various computational methods to analyze large amounts of public genomic data efficiently. The discussion of its performance improvements and capabilities in handling genomic data highlights the importance of bioinformatics in data analysis. Specifically, phrases like 'analyzed large amount of public genomic data' and 'outperforms most Hadoop and Spark based bioinformatics tools' directly support the assertion that bioinformatics is vital for genomic data analysis.


Input: Task: contrast the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: We agree with the reviewers that it would be useful to add interpretation to the HC results. We have therefore moved the paragraphs describing the HC-only findings to earlier in the first Results section, and rewritten it to be clearer. We have explained why residual velocity did not show a main effect of PD vs HC, and added a sentence evaluating the comparison to HC in the Results section: “The HC peak velocity residuals were not affected by contingency, motivation or the interaction (p >. 05; see Table 2), suggesting that healthy older adults do not adjust their response vigour for contingent or guaranteed rewards. […] HC did not significantly differ from PD ON or OFF in peak velocity residuals, although their pattern was numerically closest to PD ON with greater contingent motivation. ” And have expanded the Discussion of these results: “PD patients had slower saccadic RTs, and slower, smaller and more variable saccades compared to age-matched controls. […]This could suggest ageing decreases both contingent motivation and reward-expectation, although a study directly comparing ages would be needed to conclude this. ” We have added HC data to all data figures, and the accompanying analyses. We have moved the autocorrelation data to a new separate figure in order to include the HC data, and we now present two correlation matrices (for contingent and guaranteed reward effects) for each group () and the ON-OFF difference. 4) Additional "discussion should be provided regarding the conclusion that these data suggest that dopamine is necessary only for model-based, and not model-free learning; as there are other potential explanations; especially since the HC data don't seem to support model-based learning in this task. We have rewritten this part of the discussion, discussing PIT and instrumental model-free associations, and have clarified that model-based associations are only one possibility here: “The results suggest that dopamine is necessary for contingent motivation. […] At a more general level, our result is also consistent with dopamine being necessary for behaviours involving a causal state-action-state model (Sharpe et al. , 2017), but not simple value-guided actions (Sharp et al. , 2016). ” 5) Considerable parts of the Introduction and Discussion should be rewritten. [SEP] Justify:

Output: The text contrasts the claim by implying that dopamine's role is not solely critical for reward processing and motivation. It states that the healthy controls (HC) did not adjust their response vigor for contingent rewards, suggesting that factors other than dopamine may influence motivation and behavior. The discussion section indicates that while dopamine is considered necessary for certain types of motivation (specifically 'contingent motivation'), it emerges that it might not be essential for all forms of learning or reward processing, as 'there are other potential explanations' provided which weaken the assertion that dopamine is crucial for these processes.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: thaliana as an outgroup since it is not related to carotenoids. Finally, a phylogenetic analysis was inferred to a total of 128 elements. This analysis (Fig. 6B) was coupled with a phylogenetic analysis to highlight the GGPS elements conserved in B. rapa Pekinensis group. Carotenoid putative gene products were retrieved by using the string search of “isoprenoids” for a total number of 37 B. rapa Pekinensis group KOG, InParanoid and EnsemblPlant elements (Additional file 6) and 27 B. rapa Pekinensis group elements via queries “carotenoid oxygenase” (Additional file 7) from InParanoid, InterPro and EMBL [69]. A number of 17 carotenoid elements (Additional file 8) were retrieved from and EggNOG by using the string search “GGPS” for A. thaliana. The four outgroups used in the MEP pathway tree were also added to the list above in order to infer a more specific phylogenetic clustering computational method. The final dataset of 85 elements was subjected to phylogenetic analysis and the evolutionary tree is presented in Fig. 6A. Taken together the results presented in Fig. 6B and C, I finally reported my putative orthologs in Additional file 19: Table 7. GGPS6, GGPS9, GGPS12 and LUT1 from A. thaliana were not found in B. rapa Pekinensis group and confirmed the results on carotenoids studies [53]. GGPS4 and GGPS7 from A. thaliana correspond to Bra038544 in B. rapa Pekinensis group. Here, “putative synteny” is assumed as “high homology” which is shown in the cluster at the level of phylogenetic branch length and node of the tree (Fig. 6C). A number of 43 putative orthologs were noted as a final result by combining the Forest analysis coupled with phylogeny and the phylogenetic analysis of the MEP pathway enzymes. A summarized flowchart of the whole process is shown in Additional file 12. [SEP] Justify:

Output: The text does not directly address the role of bioinformatics in analyzing genomic data. It primarily discusses the phylogenetic analysis and retrieval of putative gene products without mentioning the bioinformatics tools or methods that are crucial for genomic data analysis.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: In addition, our tool has a reasonable accuracy and sensitivity on sequence mapping (Supplementary file S2, Table S1). Sparkhit-recruiter also offers more options for fragment recruitment, such as an option for reporting the best match for each read and an option to choose between global or local alignment, whereas MetaSpark can only apply local alignment. Spark and Hadoop based bioinformatics tools are not widely used in genomic studies as they require users to comprehend certain amount of knowledge on cloud computing. Therefore, we particularly focus on providing a simple and comprehensive cloud application to directly access and analyze public genomic datasets. We described a simple way to setup a Spark cluster on the Amazon cloud with one line of command. We also facilitated downloading and preprocessing large amounts of data by leveraging distributed Amazon S3 storage and optimizing parallel data decompression. In addition, Sparkhit enables users to parallelize their own tools or public bio-containers. Our large scale data analyses on 100 TB data presented how we completed the entire cloud utilization cycle in only 21 h. Moreover, the fragment recruitment application on all WGS data of HMP was completed in less than 2 h on Amazon EC2. The fragment recruitment application presented a study model that users can easily query the entire HMP data to a personalized reference dataset on the cloud. In microbial studies, combining metagenomic data with microbial reference genomes has been commonly used (Eloe-Fadrosh et al. , 2016). In our use case, we recruited all WGS data of the HMP to two different strains of E. coli: a toxic strain and a non-toxic strain. The intention is to find genome sequence segments that are not presented in the metagenomic samples. These sequence segments, which are unique for the toxic strain, might have functional impact that differs from the non-toxic one. We applied a functional analysis using the sequence segments and reproduced two toxic genes that caused the 2011 German E. coli outbreak. The same method can be applied to other isolates or single cell genomes. thank for proof reading the preliminary manuscript. Gratitude to and Dr. for bringing insights to the project. project has been supported by the German-Canadian DFG international research training group ‘for the Analysis of the Diversity and Dynamics of Genomes’ (DiDy) GRK 1906/1. cloud benchmarks and applications are funded by an Amazon research grant. [SEP] Justify:

Output: The text corroborates the claim by discussing how bioinformatics tools, specifically those that utilize cloud computing, are developed to access and analyze public genomic datasets effectively. It emphasizes the simplicity of using these tools for large-scale data analysis, which is integral to genomic data interpretation. For instance, it highlights how they completed the entire cloud utilization cycle with significant efficiency in processing genomic information, demonstrating the importance of bioinformatics for effective analysis in genomic studies.


Input: Task: contrast the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: Specifically, the data do not undermine the (i) theory that dopamine is key for reward expectation (Abstract: “challenging the theory that tonic dopamine encodes reward expectation”) or (ii) average reward rate (Discussion: “this contrasts with previous research which suggests tonic dopamine encode the average reward rate”) or (iii) model-free behavior (“thus the work fits with previous research in which dopamine is necessary only for model-based, not model-free learning”). These conclusions are not supported by the current findings, because: i) Detrimental effects of dopamine implicate dopamine as much as do beneficial effects as the authors conclude later on in the discussion. We have thought carefully about the interpretation of our findings, and we agree with the reviewers that our main result does not fit exactly where we placed it relative to the extensive animal literature. We have changed this claim: “We posit that dopamine promotes goal-directed motivation, but dampens reward-driven vigour, contradictory to the prediction that increased tonic dopamine amplifies reward expectation. ” ii) Average reward rate was not manipulated and its effects not measured. Related to this issue, I do not think that the Niv view is optimally characterized by attributing to it the prediction that dopamine would leave contingent motivation unaltered. The reviewer correctly argues that although we manipulated the average reward rates between conditions, the reward rate over time was not manipulated. It is therefore not possible for us to directly address the Niv view. We have clarified this and added a sentence pointing out this difference may be relevant for the lack of effect. We also mention that the fact dopamine reduces guaranteed motivation is in fact consistent with dopamine’s being involved with this kind of motivation: “Our finding that dopaminergic medication attenuates the cue-driven reward expectation effect on vigour can be contrasted with previous work suggesting that tonic dopamine couples vigour to average reward rate (Beierholm et al. , 2013; Niv et al. , 2007). […] Because our contingent and random conditions were matched for average reward rate, and thus opportunity cost, invigoration by contingent reward indicates a truly instrumental effect. ” iii) The noncontingent Pavlovian procedure tested here doesn't address model-free learning, as measured in the papers referenced. [SEP] Justify:

Output: The text contrasts with the claim by presenting findings that question the role of dopamine in reward processing and motivation. It states that dopamine does not solely encode reward expectation and can reduce motivation, as indicated by the sentence: 'dopaminergic medication attenuates the cue-driven reward expectation effect on vigour.' This suggests that rather than being simply crucial for reward processing and motivation, dopamine may dampen some aspects of motivation, which directly challenges the traditional view that emphasizes its importance.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: To verify the syntenic relationships I only suggest to run a Alignments (MSA) scanning algorithm if necessary [79, 80]. To look for putative syntenic clusters of proteins, two phylogenetic analyses were inferred by taking the elements belonging to the MEP pathway and the GGPS isoforms related (Fig. 6B, C). Here, three outgroups (Bkt - β carotene ketolase - from C. reinhardtii, Pds from C. reinhardtii, - Brp/brk β-carotene from H. psychrophilus), and one outgroup (hemoprotein cytochrome P450 - from A. thaliana) were included in the analysis in order to better estimate the divergence. The aforementioned species had some different evolution events in relation to the carorenoids pathway [31, 81] leading to a different regulation of carotenoid elements due to various adaptive fitness of the species (see Results). The C. reinhardtii element was selected as outgroup since it is an essential plant carotenoid biosynthetic enzyme. H. psychrophilus element was used as outgroup by retrieving the few information in literature databases [31, 42] ( Furthermore, the hemeprotein cytochrome P450 from A. thaliana was used as an outgroup since it is working as monooxygenase for metabolizing various xenobiotic substances. P450 was used as a negative control because it is not a carotenoid biosynthetic gene product [31, 82]. First, I used various string searches queries via API linked to Uniprot-Swiss to apply different type of sampling and minimize the noise due to database redundancy and not univocal ID. Via different API (see Methods and Results) I retrieved different datasets of proteins in. The results of the last two phylogenic analysis (Fig. 6B, C) confirmed that a number of carotenoid elements were not found in B. rapa Pekinensis group and that some carotenoid biosynthetic gene products of A. thaliana correspond to one gene product in B. rapa Pekinensis group. Interestingly, GGPS11 gene product was not found in B. [SEP] Justify:

Output: The text does not address the claim regarding bioinformatics and genomic data analysis directly. It discusses phylogenetic analyses, syntenic relationships, and other specific methodologies, but does not specifically mention or support the importance of bioinformatics in the broader context of genomic data analysis.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Developing a bioinformatics pipeline for comparative protein classification analysis access Published: 06 June 2022 Developing a bioinformatics pipeline for comparative protein classification analysis Benedetta Pelosi1 BMC 2. In the first step of the pipeline, and in order to retrieve all the proteins of A. thaliana that are annotated as being related to chloroplasts and/or plastids in public databases, I used the string searches “chloroplast proteome”, “thylakoid proteome” and “carotenoids” in UniProtKB and UniParc. I mainly focused on the aforementioned taxa in relation to the role of carotenoids in the oxygenic photosynthesis and in oxidative reactions involved in ROS/AO balance (Fig. 1). This search resulted in 5222 proteins. This large number originates from the fact that a great number of proteins is reported from the fusion of two databases (UniProtKB composed of Swiss-Prot and TrEMBL sections linked to UniParc proteomics) in which some protein names are doubled under not univocal entry name – leading to “database redundancy” – and some entry are not specifically classified. All the data retrieved (5222 elements) were merged and a number of filtering steps were devised to identify putative carotenoid elements. Fig. 2Flow of filter of the bioinformatics pipeline. 1) Database extrapolation of data related to A. thaliana and B. rapa Pekinensis group 2) Unorganized raw data 3) scripts 4) Functional annotation retrieved from Uniprot Swiss/TrEMBL 5) section 6) “Raw” phylogenetic analysis 7) potential homologous selection 8) Gene ontology/Pfam screening association for functional domains 9) Population analysis simulated – statistics score applied; reference sequences from A. thaliana TAIR database a) phylogeny to analyze the population analysis result coupled with the previous raw phylogenetic analysis results; new databases extrapolation of input data through API and specific phylogenetic analysis of MEP pathway gene products b) comparison of my pipeline with previous findings in B. rapaFull size imageFirst, the organism specific proteins for the plant species of interest were selected. [SEP] Justify:

Output: The text corroborates the claim by detailing the development of a bioinformatics pipeline specifically aimed at analyzing proteins related to A. thaliana and B. rapa. It describes multiple steps involved in retrieving and filtering genomic data, which highlights the essential role bioinformatics plays in the analysis of genomic datasets.


Input: Task: contrast the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: In contrast, reward that is independent of what an agent does might motivate us because in a variable environment, we capitalise on rewards while they are available (Niv et al. , 2007). One proposed mechanism for this is that tonic dopamine encodes expected reward rate, such that in a rich environment agents are motivated to respond faster to maximise the rewards they receive (Niv et al. , 2007). Equally, dopamine can be viewed as signalling an opportunity cost– time is more costly when reward is available, and so organisms act faster (Otto and Daw, 2019; Shadmehr et al. , 2010). The dopaminergic drive has not only generalised motivating effects, termed vigour (Beierholm et al. , 2013; Guitart-Masip et al. , 2011; Niv et al. , 2007), but also context-specific effects. For example, a stimulus that predicts rewards drives conditioned responses that are uncoupled with reward (Lovibond, 1981) – similar to how salary increases might improve job performance. This phenomenon, known as Pavlovian-to-Instrumental transfer, requires dopamine projections to nucleus accumbens (Hall et al. , 2001; Kelley and Delfs, 1991; Talmi et al. , 2008; Wassum et al. , 2013; Wyvell and Berridge, 2000). Similarly, animals tend to approach stimuli associated with rewards, even in the absence of action-contingency, a behaviour called autoshaping or sign-tracking, which also relies on nucleus accumbens dopamine (Day et al. , 2006; et al. , 2001). The dopaminergic basis of instrumental and Pavlovian motivation could potentially explain the impaired motivation seen in PD patients and the rescue of such deficits by rewards (Ang et al. , 2018; Chong et al. , 2015; de Wit et al. , 2011; Kojovic et al. , 2014). However in certain situations, motivation by reward can paradoxically be stronger in patients with low dopamine (Aarts et al. , 2012; Timmer et al. , 2018), making dopamine’s exact role in motivation unclear. These two effects of contingent and expected rewards frequently overlap in real life and in previous experiments – higher stakes raise reward expectation, but also mean that actions carry more weight. However, experimental control of expectation and contingency allows them to be dissociated (Manohar et al. [SEP] Justify:

Output: The text contrasts the claim by stating that while dopamine plays a role in motivation and reward processing, there are scenarios in which motivation may be stronger in patients with low dopamine. The mention of conditions where motivation can paradoxically be heightened despite low dopamine levels, as highlighted by 'in certain situations, motivation by reward can paradoxically be stronger in patients with low dopamine,' suggests that dopamine's role in motivation is not straightforward and can vary significantly, which challenges the assertion that dopamine is crucial for these processes.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: rapa Pekinensis group, but a further Blastp query search (E-value around 80%) in NCBI coupled with a string search in EnsemblPlant database could get a putative correspondence unreviewed on EnsemblPlant. Interestingly, I found three putative conserved elements (Bra001832, Bra036950 and Bra008358 gene products) that need further investigation, via a syntenic analysis. Despite of the presence of some “noise” due to the unbalanced classification of the databases, my pipeline generally confirmed the results previously found in B. rapa Pekinensis group [53]. The final findings of the present work are shown in Additional file 19: Table 7. It is worthwhile to mention that a sort of error occurs. This error, that I estimate as the percentage of extra putative orthologs not found in the reference work [53] with respect to all the orthologs found here, can be calculated as 5. 6%. No comparisons can be made with similar methodologies, as my pipeline is a totally new method for applying deterministic filtering coupled with different phylogenetic analyses, starting with manually filtering then combining the filtered data and the more specific dataset retreived and finally implementing biased analyses. On the contrary, in previous findings, datasets are generally retrieved by using different type of sampling which mainly relies more or less on biased approaches [83–87]. Therefore, no previous similar pipeline was applied and no comparative study can be conducted with regard to error estimation, which should be done in comparison of the findings of the reference work that I took in consideration to evaluate the validity of my pipeline. ConclusionsA filtering process is a helpful tool for screening and classifying information from proteomics studies (Fig. 7). report the use of a pipeline for classification of protein orthologs for the study of carotenoid biosynthesis proteins in a group of species of commercial interest, the cabbages of the Pekinensis group. Fig. 7Model of bioinformatics pipeline method. A A method of filtering raw data could be applied to retrieve information at the level of families and superfamilies (domains, catalytic activities conserved motif). Deep learning is not always a feasible solution, because the signal-to-noise ratio must be high. [SEP] Justify:

Output: The text discusses various methodologies, data filtering, and pipelines in a biological research context, but it does not provide any direct affirmation or rejection regarding the claim about bioinformatics' importance for genomic data analysis. The focus is on specific methods and findings rather than a broader discussion about the role of bioinformatics in genomic data analysis.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Following, to shortlist the proteins involved in photosynthesis, photo-protection, oxidative stress, plant coloration and cell signaling, I examined the gene ontology terms with a particular focus on “gene ontology molecular function” and “gene ontology biological process” (see Additional file 13: Table 1 and see the code in Additional file 9). retrieved further information by means of Pfam classification databases, and Panther [45] to filter the 5222 elements identified above. The code strings processed the proteins by a continuous filtering analysis based on Pfam and GOs for making a reduction of the A. thaliana proteome. Starting from a number of 5222, I obtained 5137, 2793, 1558, 1478, 1031, 813 proteins in six steps (Fig. 3A, see Additional file 13: Table 1). The same analysis was repeated for Pekinensis group: starting from 1046 proteins, I obtained 1017, 831, 386 in three steps Fig. 3B, see Additional file 14: Table 2). Fig. 3Filtering steps in A. thaliana and B. rapa Pekinensis group [in x-axis the steps of the filter, in y-axis the population corresponding to each step]. The plot A shows the trend of the filter population in A. thaliana. Starting from 5222, the population is filtered in 8 steps till reaching 813 elements: 5222, 5137, 2793, 1558, 1478, 1031, 813. The plot B shows the trend of the filter population in B. rapa Pekinensis group. From 1046, the population is filtered in three steps till reaching 386 elements. I obtained 1046, 1017, 831, 386 proteinsFull size imageInferred phylogenyTo firmly establish a link between the elements identified above and the carotenoid biosynthetic pathway, I carried out a phylogenetic analysis and looked for putative potential orthologs. The 1199 proteins obtained from the analysis above – 813 in A. thaliana and 386 in – were screened for duplicates/redundancy, which resulted into 1089 unique entries that were used for the phylogenetic analysis. [SEP] Justify:

Output: The text corroborates the claim as it describes a detailed process involving the filtering and analysis of genomic data, specifically proteins related to photosynthesis and carotenoid biosynthesis, showing how bioinformatics tools and methodologies were used to derive insights from large genomic datasets. The use of Pfam classification databases, gene ontology terms, and phylogenetic analysis highlights how bioinformatics is integral to analyzing and interpreting genomic data.


Input: Task: corroborate the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: The mean measures for the four conditions (Performance, Random, Guaranteed 10p, Guaranteed 0p) for each variable, with individual data points. The difference between Performance and Random shows the effect of Contingent motivation, while the difference between 10p and 0p shows the motivating effect of reward expectation. (a) Peak velocity residuals indexed behavioural vigour. When ON dopamine, patients were motivated to invigorate their saccades when reward depended on response time, but not when expecting a guaranteed reward. In contrast, when OFF dopamine, vigour was driven by expectation of guaranteed reward, but not by contingency (F (1, 200) = 9. 5190, p =. 0023, ηp2 =. 0454). (b) HC were similar to PD ON dopamine (please note the different y-axis limits). (c–e) No dopaminergic effects were observed for (c) saccade amplitude, (d) saccade RT, (e) endpoint variability, or (f) raw peak velocity, although PD patients had slower, smaller and more variable saccades than HC. All measures are in visual degrees, except saccade RT (ms). Error bars show within-subject SEM. Statistics are presented in Table 2. Data are available in Figure 2—source data 1. Figure 2—source data 1 Source individual data for all saccade measures for and HC. The figures can be constructed using the DrawSaccadeFigures. m Matlab file available from the ContingentAnalysis GitHub repo (see Data and section of manuscript). Download elife-58321-fig2-data1-v3. mat. zip Table 2 Statistics for main behavioural analyses. Three-way (motivation*contingency*drug) repeated-measures ANOVA on each behavioural measure, for the PD patients ON and OFF medication. An effect of contingency means the guaranteed conditions (10 p, 0 p) were different to the contingent conditions (Performance, Random). An effect of motivation means the 10 p and Performance conditions were different to the Random and 0 p conditions. An interaction of the two means that contingent rewards differed from guaranteed rewards. * Motivation * Drug condition means that the effects of contingent and non-contingent rewards differed by PD medication state. Significant effects are highlighted in red. *p < 0. 05, **p < 0. 01. [SEP] Justify:

Output: The text supports the claim by explaining how dopamine influences motivation during reward-related tasks. It states that when dopamine is active, 'patients were motivated to invigorate their saccades when reward depended on response time,' demonstrating dopamine's role in motivation linked to reward. Furthermore, the contrasting effects when dopamine is off also imply that dopamine is involved in processing contingent rewards, reinforcing its importance in motivation and reward processing.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: pekinensis were retrieved by using the string search of “isoprenoids biosynthesis” for a total number of 37 KOG elements from and 27 elements from EMBL and InterPro via the query “carotenoids oxygenase” from KOG, Inparanoid and Interpro. 17 carotenoids elements were retrieved from [109–111] and EMBL by using the string search “GGPS” for A. thaliana. The four outgroups used in the MEP pathway tree analysis were added to the list above in order to obtain a more specific phylogenetic clustering computational method. The final dataset was subjected to phylogenetic analysis by using method and JTT matrix-based model and the resulting tree with the highest log likelihood (-41791. 08) is shown in Fig. 6C. Initial tree for the heuristic search was obtained automatically by applying the method. A discrete Gamma distribution was used for modeling evolutionary rate differences among sites (5 categories (+G, parameter = 2. 1935)). The rate variation model permitted for some sites to be evolutionarily invariable ([+I], 0. 00% sites). All positions with less than 50% site coverage were eliminated, i. e. , fewer than 50% alignment gaps, missing data, and ambiguous bases were allowed at any position (partial deletion option) with a result of a total of 410 positions in the final dataset. Availability of data and materials The datasets analysed during the current study are available in the following repository: UniProt repository [ Pantherdb repository [ [ Arabidopsis repository [www. arabidopsis. org/] InParanoid [ Prosite [ KOG [ KEGG [ [ EMBL-EBI [ NCBI [ [ ncbi repositry [ The datasets used as input of the proposed classification method are also available as supplementary files. The resultant datasets, output of the proposed classification method, are available as excel file. AbbreviationsABA: Abscisic acid API: : Arabidopsis thaliana BLAST: : B. rapa subsp. pekinensis BRACM: B. rapa L. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not discuss bioinformatics or its role in analyzing genomic data. Instead, it provides details about data retrieval methods and phylogenetic analysis without addressing the importance of bioinformatics.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: With reference to Additional file 19: Table 7 the non syntenic ortholog shared by GGPS1, GGPS2, GGPS3, GGPS7, GGPS8 was not found (highlighted in green in table), whereas I retrieved some potential orthologs for DXS (Bra001832), GPS11 (cag7890226(Bra)) and BCH2 (Bra008358) (in bold and highlighted in blue in Additional file 19: Table 7) which need further verification (e. g. syntenic analysis [70–73]). Interestingly, at least GPS11 showed to have an ortholog by doing a Blast search on NCBI and (Blastp not shown). Furthermore, I also identified a gene product probably related to carotenoid elements (highlighted in orange in table): PSBS-like gene (Bra036950). In conclusion, a sort of error occurs, which can be estimated as the percentage of the orthologs not found in the reference work [53] with respect to all the orthologs retrieved, and can be calculated as 5. 6%, computed as the number of the extra elements found (4, namely Bra001832, cag7890226, Bra008358, Bra036950) over the total number of orthologs counted in the table (72). DiscussionIn this study, I performed a comparative protein classification analysis between A. thaliana and B. rapa Pekinensis group (i. e. the mustard family group called either B. rapa Pekinensis group or B. rapa campestris L. ) using the protein sequences and annotation information of the two species [54]. I only focused on protein level, and not on nucleotide sequences, to better exploit the higher degree of conservation that exists in the amino acid sequences. In order to validate the proposed pipeline, the ontology analysis of carotenoid biosynthetic gene products in B. rapa Pekinensis group was compared with results achieved in a previous study [53]. With this purpose, I developed a system of classification in steps – pipeline – for cataloging putative elements in the carotenoids pathways of A. thaliana and B. rapa Pekinensis group. [SEP] Justify:

Output: The text corroborates the claim by detailing a comparative protein classification analysis between two plant species, A. thaliana and B. rapa Pekinensis, which highlights the use of bioinformatics methods. The author mentions the use of protein sequences, ontology analysis, and classification pipelines, indicative of bioinformatics being employed to analyze genomic data regarding carotenoid biosynthetic gene products. This demonstrates the importance of bioinformatics in the analysis of genetic information.


Input: Task: contrast the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: In particular, the degree to which dopamine improved performance-contingent motivation did not predict the degree to which it reduced motivation by guaranteed rewards. Figure 5 Download asset Open asset No correlations between contingent and guaranteed rewards. Scatter plots of the effect of contingent and guaranteed rewards (i. e. contingent effect = Performance minus Random trials, guaranteed effect = guaranteed 10 p minus guaranteed 0 p trials) on peak velocity residuals, within each group (top row: ), and between medication conditions (bottom row). Dots show the mean values. No Spearman’s correlations were significant (ON: ρ = −0. 1549, p=0. 4503; OFF: ρ = 0. 3730, p=0. 0614; HC: ρ = −0. 2153, p=0. 2609; Contingent ON vs OFF: ρ = −0. 3429, p=0. 0869; Guaranteed ON vs OFF: ρ = 0. 1432, p=0. 4834; ON-OFF Contingent vs Guaranteed: ρ = −0. 2438, p=0. 2291). Figure 5—source data 1 Source individual data for velocity residual correlations for and HC. The figures can be constructed using the DrawCorrelFigures. m Matlab file available from the ContingentAnalysis GitHub repo (see Data and section of manuscript). Download elife-58321-fig5-data1-v3. mat. zip Source data are available in Figure 5—source data 1. Pupil dilatation We examined pupil dilatation after the cue onset and before the target appeared (after 1400 ms). Previous research has shown a greater effect of contingent than guaranteed reward on pupil dilatation, maximal around 1200 ms after the cue (Manohar et al. , 2017), so we used a window-of-interest analysis on the mean pupil dilatation 1000–1400 ms after the cue. There were no significant effects or interactions (p>0. 05; Figure 6, see Supplementary file 2A-C for statistics), suggesting that dopamine and reward did not affect pupil responses in PD patients. Figure 6 with 2 supplements see all Download asset Open asset No effects of motivation on pupil dilatation. [SEP] Justify:

Output: The text contrasts the claim by discussing findings related to voluntary motivation not being predicted by dopamine under certain conditions. It specifically mentions that dopamine improved performance-contingent motivation but did not predict the reduction of motivation when guaranteed rewards were present, indicating a more complex role of dopamine in motivation rather than it being crucial as claimed.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: (pekinensis group) CNN: : : : JTT: Jones-Taylor-Thornton KOG: : using : MethylML: MSA: : for NJ: Neighbor-Joining algorithm NPQ: Non-Pfam: protein families RA: Brassica rapa Group RF: ROS: : : : with Arithmetic mean WGT: Triplications 12. 52. 849. 41. 69. 28. 2018. 2019. Res. 2019; 47(D1):427–32. 1801. 74. 92. 11. 6. 28. 70. 2004. p. 3–11. 72. 52. 508. PubMed Pub88. 7. Lyon: L. Favre; 1886. 60. CAS PubMed 9. 8924. 2012. 2015. p. 1–15. 45. 9. 2016. p. 115–40. 95. 22. 90. 32. 10. 564. 2099. 2. Burlington: Academic Press; 2009. 1250. 0306. 11. . 2019; 366(14):177. 63. 57. 7. 7. 41. 35. MEGA X: 2018, 9. Whole genome and phylogenetic analysis of two sars-cov-2 strains isolated in italy in january and february 2020: additional clues on multiple introductions and further circulation in europe. Eurosurveillance. 2020; 25(13):2000305. 1068. 8. 85. 45. 26. 11. 3613. 40. Humana Press, New York. O’brien. Inparanoid: a comprehensive database of eukaryotic orthologs. Res. 2005; 33(suppl_1):476–80. 9. 60. 22. 2. 0 database: a significant proteome update and a new webserver. Res. 2019; 47(D1):490–4. 12. 8. 79. 8. 39. 606. 605. 60. The embl-ebi search and sequence analysis tools apis in 2019. Res. 2019; 47(W1):636–41. 4. 8678. Restrepo-. Orthology and synteny analysis of receptor-like kinases “rlk” and receptor-like proteins “rlp” in legumes. BMC Genomics. [SEP] Justify:

Output: The text provided does not directly address the claim concerning the importance of bioinformatics in analyzing genomic data. Instead, it appears to be a collection of references and various abbreviations related to biotechnology and genomics, without explicitly supporting or contradicting the claim of bioinformatics being crucial.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: (RF) could not work for the purpose, because the biological properties of the proteins are not exact weights in terms of numeric values to develop a classifier and a neural network system [74]. Instead, I developed a computer-based analysis which classified a number of 1089 elements retrieved as chloroplastic proteins and reviewed enzymes of the carotenoid biosynthestic pathways. The functional analysis method that I propose makes use of GOs, Pfams, “population analysis” and phylogeny to identify the orthologs in B. rapa subsp. pekinensis using reference sequences from the well characterized model of the vascular plant A. thaliana. Previous studies showed that there are 67 carotenoid biosynthesis genes in B. rapa and 42 out of them have ambiguous orthologs (syntenic and not syntenic) in A. thaliana [53]. To assess the performance of my pipeline, I applied it to the well known carotenoid biosynthetic genes of A. thaliana and B. rapa Pekinensis group. As a first step, I retrieved the chloroplast and thylakoids proteomes of the two species and I filtered them by means of GO and Pfam terms for specific carotenoid oxidative function or linkage to the photosynthetic apparatus (“deterministic filtering process”) (Fig. 3). The high number of starting elements retrieved is due to the redundancy resulting from the fusion of Uniprot-Swiss/TrEMBL with UniParc proteomics [75]; it happens that one protein is referred with different IDs which carries to a not univocal identification of the same protein. This is a typical issue of not univocal annotations [76]. Following, I looked for the carotenoids conserved in the A. thaliana and B. rapa Pekinensis group. All the proteins obtained from the analysis above – 813 in A. thaliana and 386 in B. rapa Pekinensis group – were screened for redundancy via a script and, after the screening, 1089 total non-redundant elements were used for the next analysis. At this point, a phylogenetic analysis was inferred and the bootstrap method gave a first probabilistic demonstration of the protein conservation (Additional file 1). [SEP] Justify:

Output: The text corroborates the claim by describing the development of a computer-based analysis for classifying proteins and analyzing genome data from difficult-to-analyze biological properties. It emphasizes the use of various bioinformatics tools such as GOs and Pfams to identify orthologs and perform phylogenetic analysis, demonstrating the importance of bioinformatics in genomic data analysis.


Input: Task: contrast the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: The effects of contingent (top) and guaranteed rewards (bottom) on pupil dilatation in the different conditions up to 1400 ms after the reward cue. Pupil dilatation is baselined to the time of cue onset. There were no significant clusters of difference between any groups (cluster-wise permutation testing: p>0. 05), nor did a window-of-interest (1000–1400 ms) ANOVA find any significant effects (Supplementary file 2A-C). Shading shows SEM. Source data are available in Figure 6—source data 1. Figure 6—figure supplement 1. No correlation of pupil dilatation and motivational effects on velocity. Figure 6—figure supplement 2. Individual data for pupil dilatation. Figure 6—source data 1 Source individual data for pupil dilatation for and HC. The figures can be constructed using the DrawPupilFigures. m Matlab file available from the ContingentAnalysis GitHub repo (see Data and section of manuscript). Download elife-58321-fig6-data1-v3. mat. zip We also used a hypothesis-free analysis, using cluster-wise permutation testing across the whole time-course to look for significant differences between conditions and groups, which also found no significant effects (p>0. 05). We found no correlations between pupil dilatation and motivation effects in any group, or overall (p>0. 05; Figure 6—figure supplement 1). Thus, the vigour effects were not related to pupillary dilatation before the movement. PD severity We looked to see whether the dopaminergic effects on velocity residuals could be tied to PD symptom expression. (Martínez-Martín et al. , 2015) is a measure of PD symptom severity and was performed in each session; part III measures motor symptom severity. We found no correlations between UPDRS-III scores and reward effects on peak velocity residuals in PD ON (Guaranteed: ρ = −0. 1256, p=0. 5410; Contingent: ρ = −0. 2327, p=0. 2527) or OFF (Guaranteed: ρ = −0. 2067, p=0. 3110; Contingent: ρ = 0. 1553, p=0. 4487). Thus, the reward effects were unrelated to PD symptom severity. [SEP] Justify:

Output: The text contrasts the claim by providing results that indicate no significant effects related to pupil dilatation and motivation, suggesting that the effects of rewards are not linked to motivational processes as assessed by pupillary response. It states that there were no correlations found between various forms of dopaminergic effects and reward influences on motivation metrics, which contradicts the assertion that dopamine is crucial for these functions.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: 2021; 22(1):1–17. 198. 507. 18. 11. 19. 8. CAS 1190. 42. 1. J Phycol. 2006; 42(6):1208–16. 89. 23. 54. 70. 95. 4. 279. 2015. p. 301–17. . Protein structure prediction by alphafold2: are attention and symmetries all you need? . D Struct Biol. 2021; 77(8):982–91. 9. 3005. 0235. . Hidden markov model for the prediction of transmembrane proteins using matlab. Bioinformation. 2011; 7(8):418. 9. 3. 0 (R2017b). Natick: The MathWorks Inc. ; 2021. 34. 620. 24. 2013. 2108. 01243. 2021. . Mega5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Evol. 2011; 28(10):2731–9. 9. 2. 43. 11. 13. 90. 36. 30. 51. 51. 1048. 2. Additional file 11 Script used for the forest analysis based on the gaussian fitting - refer to Fig. 5 and Additional file 12. Additional file 12 Flow-chart summarizing the whole process combining the forest analysis coupled with phylogeny and the phylogenetic analysis of the MEP pathway enzimes. Additional file 13 Steps of the filter population in A. thaliana. Additional file 14 Steps of the filter population in B. rapa Pekinensis group. Additional file 15 Table showing the discarding of the orthologs which did not play a role in oxygenic photosynthesis (labeled in yellow) resulting in 44 potential orthologs. Additional file 16 GO and Pfam terms reviewed Carotenoid biosynthetic gene products. Additional file 17 Characterized A. thaliana reference sequence from TAIR. Additional file 18 Orthologs resulting from the tree obtained from the phylogenetic analysis of 44 elements of B. rapa Pekinensis group and 47 conserved Carotenoid gene products from A. thaliana. Additional file 19 Table showing the Final finding of the whole process. Rights and permissions This 4. [SEP] Justify:

Output: The text does not address the claim regarding the importance of bioinformatics in analyzing genomic data. It consists of a series of citations and references without providing any substantial information about bioinformatics or its role, making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: A well established commercial software, [46], that has given satisfactory results in studies of inferred phylogeny, was employed. The use of this commercial software needs a preliminary alignment (-UPGMA or ClustalW [77]) of the sequences for estimating a preliminary covariance at the level of the substitution sites [78]. Eventually, a number of 180 gene products in A. thaliana had one ortholog in B. rapa Pekinensis group because I considered also bootstrap results between 11 and 80 for this preliminary analysis (Additional file 15: Table 3). The list of 180 potential orthologs from the phylogenetic tree was studied for GO and Pfam terms reviewed carotenoid biosynthetic gene products (Additional file 16: Table 4, Fig. 4). To further confirm the orthologs groups, a forest analysis (Fig. 5) was exploited as a “population analysis” using 47 well characterized A. thaliana reference sequences from TAIR (Additional file 17: Table 5) and the total B. rapa Pekinensis group population filtered (Additional file 14: Table 2, 386 elements); the analysis was performed to have a different and broader spectrum of sampling. In details, the 62 sequences from the “forest analysis”, the 44 elements of B. rapa Pekinensis group potential orthologs derived from the previous phylogeny and the 47 conserved carotenoid gene products from A. thaliana were subjected to a new phylogenetic analysis (Fig. 6A). The phylogenetic tree gave a result of 40 orthologs (see Additional file 18: Table 6). All the aforementioned biased analysis allowed to specifically identify 40 not conserved carotenoid gene products in B. rapa Pekinensis group, but I were not sure about how many orthologs a single carotenoid element can have. Different A. thaliana proteins have more than one syntenic ortholog as also shown in previous finding [53]. I did not perform a syntenic analysis since I were not focused at the level of a pangenome architecture or genome assemblies, as the main purpose of this work has been a filtering of raw data. However, the high conservation among different protein clusters gave putative information about a possible syntenic relation (Fig. 6A). [SEP] Justify:

Output: The text corroborates the claim by providing a detailed description of the bioinformatics methodologies employed in the analysis of genomic data. The mention of commercial software for phylogeny studies and preliminary alignments like ClustalW highlights the significance of bioinformatics tools in the analysis process. Additionally, it discusses various analyses conducted to identify orthologs and the conservation of genes, demonstrating how bioinformatics is integral to interpreting genomic data effectively.


Input: Task: contrast the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: Depression and apathy We gave participants questionnaires measuring apathy, the AMI (Ang et al. , 2017) and depression, BDI-II and HADS (Beck et al. , 1996; Zigmond and Snaith, 1983). We found no significant correlations between these questionnaires and contingent or guaranteed motivational effects on peak velocity residuals in PD ON or OFF (p>0. 05, see Supplementary file 4 for statistics). Fixation period We looked at whether motivation was affecting behaviour during the fixation period (1400 ms between condition cue onset and target onset) differently, which could potentially lead to differences during the movements. We excluded trials with saccades, blinks, deviations greater than 1. 8° and segments with velocities greater than 30°s−1. PD OFF had more microsaccades (<1°) during the 1400ms fixation period than PD ON (F (1, 201) = 5. 0451, p =. 0258, ηp2 =. 0245), but there were no other effects or interactions (p >. 05, 3A for statistics). Conversely, ocular drift speed was higher in PD ON than OFF (F (1, 216) = 5. 4327, p =. 0207, ηp2 =. 0245), but there were no other significant effects or interactions (p >. 05, see 3B). Importantly, the lack of interactions means that while patients may have differed in their fixation activity, this was unaffected by motivation conditions, and thus a different pattern to the main effects shown above. To quantify ocular tremor, we performed Fourier transforms on the eye position in the early (200–700 ms) and late (700–1200 ms) fixation periods, and compared these between conditions with cluster-wise permutation tests to look for clusters of frequencies where patients differed. We found no significant clusters (p>0. 05). this study, we tested two competing theories of dopaminergic motivation – that dopamine improves instrumental, contingent motivation, and that dopamine improves guaranteed reward motivation via reward expectation. Patients with PD made more vigorous responses, measured by peak saccade velocity residuals (Figure 2a), when rewards were either contingent on performance or guaranteed, but these two effects were differentially affected by dopaminergic medication. When ON medication, PD patients were motivated by rewards contingent on performance, but not by guaranteed rewards. [SEP] Justify:

Output: The text contrasts the claim by presenting evidence that casts doubt on the role of dopamine in motivation. It mentions the study findings that 'there were no significant correlations between these questionnaires and contingent or guaranteed motivational effects on peak velocity residuals,' suggesting that motivation was not significantly influenced by dopamine levels as indicated by the performance metrics of Parkinson's disease patients. This calls into question the assertion that dopamine is crucial for reward processing and motivation.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The terms in the are not independent, but rather form a directed acyclic graph with more specific terms as the children of more general parents. For example, mismatch repair is a child of DNA repair. Simple term-for-term analysis does not take into account the potential relationships among different GO terms such as a parent-child relationship. Grossmann et al. [23] present a novel approach for detecting overrepresentation of GO terms using parent-child analysis. Their method addresses not only the hierarchical nature of GO terms but also occurrences of the same term in multiple branches of the graph. The less rigorous Parent-Child-Union strategy defines the set of parents of a term t as the union of genes annotated with any parent of t. The Parent-Child-Intersection strategy reduces the number of enriched terms by defining the set of parents of a term t as the intersection of genes annotated with term t, counting the genes annotated for all of the parents. Grossmann et al. conclude the parent child approach avoids many of the false positives that the Term-For-Term approach produces. IEA, Inferred from Electronic Annotation, consists only of evidence from computational analysis and is considered by some as less trustworthy. included allows for either (no IEA) or (include IEA). Types increases the evidence, but perhaps decreases the perceived quality. term hits allows for either targeted or more general searches. Each result from enrichment analysis includes the enrichment term, the gene or gene pair found to be enriched for that term, and scores that represent the confidence in the enrichment result. The Score-from-source is the p-value representing the likelihood that a subset with shared GO terms happened randomly as compared to the general population. This value and the user's background knowledge form the Score-from-source credibility value. Another important factor in enrichment studies is the size of the enriched set relative to the set as a whole. The Size-of-group value is the number of genes enriched for the same term. The Size-of-group credibility score allows the user to define the importance of this measurement. Sidekick combines all of the credibility scores using theory. Enrich: for disease terms uses NCIBI's Gene2MeSH web service to find the disease mesh terms enriched in subsets of the gene inputs. Sidekick only uses the disease category mesh terms from the NCBI's of Medicine. [SEP] Justify:

Output: The text does not directly address the claim about the importance of bioinformatics in genomic data analysis. Instead, it discusses specific methods related to gene function analysis and overrepresentation of Gene Ontology (GO) terms without explicitly connecting these methods to bioinformatics as a whole or its significance in genomic data processing.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Thus, it is necessary to preprocess the input data to minimize the noise, either selecting the best hits by the means of Blastp or performing an 2 analysis. If neither of these processing is possible (mainly, due to the lack of univocal identification numbers reported in databases), I can preceded by filtering functional annotations in various databases to avoiding false positive weights in sampling and bias applied to the analysis. B In the latter case, the way to proceed is by the filtering pipeline shown here, which summarizes the investigation carried out by this paperFull size imageA number of 45 carotenoid biosynthetic gene products was retrieved (see Additional file 19: Table 7). However, the table included the photosystem II element (PBS) and the GPS11 which needed to be retrieved via Blast and searches. In conclusion, discarding the above mentioned elements (which would need further investigation), the retrieved orthologous are 43. The error of the analysis is estimated to be 5. 6%. The proposed tools are particularly useful when there is uncertainty about which factors are more relevant than others when classifying data (Fig. 7A). When a system is perturbed by the means of irrelevant datasets included in the analysis, I are tempted to apply wrong biased analysis which can lead to false-positive results (theory of chaos model, [88, 89]). The proposed method could be applied when enough information are available at a level of family and superfamilies (domains, catalytic activities conserved motifs) about the proteins of interest and can be applied to different type of protein datasets. On the contrary, as shown in Fig. 7B, when dealing with lack of information, I suggest to perform a Blastp with restricted E-value selection to underline possible characterized proteins in the database or, if there is low similarity, I suggest to use 2 algorithm [90] for a structure-based homology search [91]. MethodsIn this work I evaluated the efficiency and accuracy of a pipeline for protein classification in the two well-studied plants which belong to the Brassicaceae family: A. thaliana and B. rapa Pekinensis group. To notice, the different databases screened catalogue B. rapa Pekinensis group under different labels: B. rapa subsp. pekinensis and B. rapa campestris. Hence, the results presented in the tables keep the original labels of the database sources. [SEP] Justify:

Output: The text corroborates the claim by discussing the importance of preprocessing genomics data using methods such as Blastp and functional annotation filtering to minimize noise and avoid bias in analysis. The explanation of needing accurate identification and filtering to ensure the quality of data aligns with the assertion that bioinformatics is crucial for analyzing genomic data.


Input: Task: contrast the claim using the given text. Claim: Dopamine is crucial for reward processing and motivation. [SEP] Text: In contrast, when patients were OFF their dopaminergic medication, the opposite pattern was observed; they were motivated by guaranteed rewards, but not by rewards contingent on performance. In this study, older healthy controls were not significantly invigorated by either guaranteed or contingent rewards, although they showed a numerically similar pattern to PD ON. Guaranteed rewards led to PD OFF having earlier increases in velocity and acceleration (Figure 3c & d), which was not seen in PD ON or when rewards were contingent, and this was accompanied by increased autocorrelation of eye position (Figure 4), suggesting increased motor noise early in the saccade. The two motivational effects were uncorrelated across people and between medication states (Figure 5) indicating that dopamine does not promote one type of motivation over another in a competitive fashion, and were not associated with changes in pupil dilatation (Figure 6). Rather, reward expectation and contingency provide distinct motivational drives (Figure 7), which can be dissociated by dopaminergic medication. Figure 7 Download asset Open asset Proposed model for dopaminergic dissociation of reward expectation and contingent motivation. We propose that dopamine (in PD patients) increases contingent motivation by acting on the caudate nucleus, which disinhibits the superior colliculus (via the basal ganglia output nuclei) and affects the firing activity within the saccade, influencing vigour. Separately, high tonic dopamine impairs reward expectation motivation via the nucleus accumbens and ventral pallidum, which also disinhibit the basal ganglia output nuclei to affect superior colliculus firing activity and thus vigour within the saccade. Possible mechanisms for this dissociative dopamine influence include separate dopaminergic regions innervating the two pathways, ‘global’ vs ‘local’ signalling, or different expression of D1-like and D2-like receptors (see text for details). The results suggest that dopamine is necessary for contingent motivation. Contingent motivation requires the use of stimulus-action-outcome associations for goal-directed behaviour (Daw and Dayan, 2014; Dickinson, 1985), while reward expectation can occur via stimulus-outcome associations (Niv et al. , 2007) that do not require understanding the causal role of action. [SEP] Justify:

Output: The text contrasts the claim by illustrating that dopamine does not uniformly promote motivation in reward processing. It describes a study where patients OFF their dopaminergic medication were motivated by guaranteed rewards but not by contingent rewards, indicating that dopamine's role in motivation can be dissociated between reward expectation and contingent motivation. Furthermore, the text states that dopamine is necessary for contingent motivation but can impair the motivation related to reward expectation, suggesting that dopamine's influence is complex and not solely supportive of the claim.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Like GO terms, disease mesh terms are hierarchical. Sidekick uses a modified version of the GO term enrichment algorithm of Grossmann et al. to combine mesh terms in parent-children relationships. The outputs are similar to those of GO term enrichment, with the Score-from-source as the p-value representing the likelihood that a subset with shared mesh terms happened randomly as compared to the general population. Gene expression refers to the number of transcripts produced from a gene, which is loosely related to the number proteins produced. Sets of genes that are over-expressed or under-expressed under a specific condition as compared to the population as a whole may be related. provides a web service, Atlas within ArrayExpress [24] that contains curated data for gene expressions under different biological conditions across experiments. The conditions include cell type, developmental stage, and disease state among many others. Enrich: for gene expression allows input of a gene list and selection of multiple conditions. ArrayExpress does not directly return a p-value but instead returns the number of experiments in which the gene is up/down regulated for that condition. Sidekick forms the Score-from-source by subtracting the number of down-regulated experiments from the number of up-regulated experiments. If the final Score-from-source is positive, more evidence for up regulation is present. The other scores are similar to those of the other enrichments. Enrich: for chromosomal proximity displays the gene groups that are most enriched for chromosomal proximity as determined by the number of base pairs separating the start positions of genes. The Score-from-source is the p-value of the enrichment indicating the probability that the proximity of the subset of genes could have happened at random. All of Sidekick gene enrichment analyses allow gene pair lists for input. Sidekick pair-list enrichment analysis finds gene pairs where both elements are enriched for a specific term. As an example, Enrich: for GO terms can determine whether a subset of gene pairs localize to the same location in the cellular context. from source is the p-value for the enriched subset that contains the genes. CombineThe union operation finds all genes or gene pairs present in any input list, while intersect finds genes or gene pairs present in every input list. The difference operation asks the user to designate one of the input lists as the superset and removes all items of the other list from the superset. [SEP] Justify:

Output: The text does not directly address the claim regarding the importance of bioinformatics in analyzing genomic data. Instead, it provides a detailed description of various algorithms and tools used for gene expression analysis, enrichment, and data retrieval, without explicitly stating the significance of bioinformatics as a field in the genomic analysis process.


Input: Task: corroborate the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: A method of classification of raw data was evaluated to understand how different types of analysis can be applied depending on the sampling of the data in order to minimize the noise which can lead to a wrong bias and compromise the output results (false positive). The analysis has been compared with previous studies on carotenoid biosynthetic genes in B. rapa where phylogenetic analysis was computed and coupled with transcriptional profiles analysis [53]. I chose to screen libraries in public databases and avoid alignment-based techniques (i. e. BLAST – Tool – [92], MAFFT – using Transform – [93]) since sequences with low similarity among them are subjected to performance degradation and moreover this method could need a long processing time on large datasets. The computational proceeding includes the use of a “deterministic filtering” step, GO terms and Pfams screening for function and “population analysis” coupled with phylogenetic analysis. I worked on the amino acid sequences which provide functional information (domain and motif) that is not straightforwardly visible in the nucleotide sequence. Flow of filtersThe chloroplast and plastid biomes of A. thaliana were retrieved from the databases (Uniprot-Swiss/TrEMBL) where they were identified based on the proteomic studies. Additionally, the string searches related to the “photosynthetic process”: “chloroplast proteome”, “thylakoid proteome”, “carotenoids” were used. Taken all the data together, 5222 proteins were retrieved. Panther and PFamCrossReference were also used as a collection of protein families compiled using multiple sequence alignments and hidden Markov models [94]. Pfam and GO information were saved in an excel file expanded to include available protein information in Uniprot - database and related API information linked. The same string searches were used for B. rapa Pekinensis group as well. Filtering steps carried out with a commercial software ([95, 96]) were computed afterward (see Additional file 13: Table 1, Additional file 14: Table 2). The final populations of A. thaliana and B. rapa Pekinensis group consist on 813 and 386 proteins respectively. The populations of the two species were fused and the redundancy of protein elements was checked via script with a result of 1089 total elements after the screening. B. [SEP] Justify:

Output: The text corroborates the claim by discussing various methodologies employed in the analysis of genomic data, such as classification of raw data, filtering steps, and the use of computational tools to minimize noise in data, thus ensuring that analyses yield accurate results. Specifically, it details workflows using bioinformatics tools to handle large datasets and perform functional analysis of proteins, demonstrating the importance of bioinformatics in genomic research.