Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: Full size imageDiscussionAdvances in non-culture-based analyses of the intestinal microbiome have demonstrated that environmental influences (e. g. , diet, antibiotic therapy, age, colonization history, geography) have profound effects on microbiota community structure, membership, and function [18], which are region specific within the gastrointestinal tract [4, 5]. Although intestinal surgery in the form of resection and anastomosis has been practiced for decades, until now, there has been no systematic assessment of the bacterial diversity and functional potential associated with this surgery. It has been observed in germ-free and antibiotic-treated animals that disruption of the normal microbiota results in a weaker intestinal anastomosis as measured by burst strength (i. e. , the pressure at which an anastomosis will burst) [19, 20]. In these studies, they also observed a loss of collagen, a major determinant of anastomotic healing and burst strength. Conversely, empiric antibiotic treatment in other animal models prevents anastomotic leak when pathogenic bacteria or pathologic conditions are present [21]. This latter observation suggests that tissue injury alone, and the physiologic stress it incurs, may significantly influence the microbiome. Our results indicate that intestinal resection and anastomosis induce a change in the microbiota associated with intestinal tissues, which is not observed in the luminal contents. Our findings also predict that tissue-associated microbial communities express an adhesive phenotype, which may explain, in part, their tropism toward injured tissues. Previous work from our laboratory has indicated that soluble products released during surgical injury can act as both chemoattractants and ‘cues’ that induce a phenotype shift among pathogenic bacteria such as P. aeruginosa[22, 23]. In the present study, anastomotic tissues may select for microbes that express enhanced virulence (i. e. , E. faecalis) and thus may have the potential to complicate anastomotic healing. A critical loss of the cytoprotective microbiota (i. e. , Firmicutes and Bacteroidetes) on anastomotic tissues may further facilitate colonization and invasion by disease-associated microbiota, again with the potential to complicate healing. The precise explanation by which the process of surgery in the present study shifted the intestinal microbiota remains to be clarified. [SEP] Justify:

Output: The text contrasts the claim by discussing the diversity and variations in microbiome composition due to environmental influences such as diet, age, and geographical factors. It emphasizes that microbiota community structure is region-specific within the gastrointestinal tract, indicating that not everyone has identical microbiome compositions.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: In dopaminergic neurons (Figure 3b-g), D2Rs were found in DM1, pPAM, DL2b, and some DL1 neurons. In the mushroom body, D2Rs were expressed in the soma and lobes, but not in the calyx (Figure 3h and i). Even though D2Rs were widely found in vertical lobes, medial lobes, and peduncles, they were not expressed in every mushroom body neuron. A transection of the peduncle region showed the absence of D2Rs in the core area (Figure S3i), which is composed of densely packed newly created fibers and lacks Fasciclin II (FAS II)49. Figure 3. D2Rs are expressed in dopaminergic neurons and mushroom body in Drosophila larval brains. (a) The expression pattern of D2R in a general view. D2R is shown with tagged GFP (D2R-GFP). Magenta represents neuropils marked by nc82 antibody (a’). (b-g) D2Rs (presynaptic) are found in most DANs: DM1a (b) and DM1b (c), pPAM (d), DL1 (e), DL2a (f), and DL2b (g) clusters. D2Rs are expressed in parts of DL1 neurons (white arrows in e) but not in DL2a neurons (yellow arrow heads in f). (h-i) D2Rs (postsynaptic) are found in soma of mushroom body (MB) neurons (white arrows in h), and MB lobes and peduncles (i), but not in calyx (yellow arrow heads in h). (j) A schematic diagram shows the expression pattern of pre- and postsynaptic D2R in DANs and MB (yellow) in the Drosophila larval brain, respectively. Scale bars: 200 µm (a); 50 µm (b-g); 20 µm (h, i). Abbreviations: DL, dorsolateral; DM, dorsomedial; pPAM, primary protocerebral anterior medial. [SEP] Justify:

Output: The text is irrelevant to the claim because it discusses the expression and localization of D2 receptors (D2Rs) in dopaminergic neurons and the mushroom body of Drosophila larval brains, without mentioning olfactory receptors or any quantum effects related to their functioning.


Input: Task: corroborate the claim using the given text. Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: To comply with future energy demands, potential renewable energy sources should meet the following criteria: (1) the sources should be technically and economically accessible, (2) the sources should be large enough to sustain a long-lasting energy supply to generate the required electricity for the country, (3) the sources should be environmentally friendly and thus should be low GHG emitters in order to make significant contribution to global warming mitigation, and (4) the sources should have a wide geographic distribution [1, 2, 3]. Low-enthalpy geothermal energy (LEGE) resources extraordinarily satisfy all of these important criteria. This vast LEGE resource has already been utilized for electric power generation by some countries, such as the USA, Mexico, the Philippines, Indonesia, Austria, Germany, and Iceland [2]. The installations of several commercial LEGE electrical power systems in these countries have substantially proved the ability of low-temperature geothermal fluids to generate electricity [2]. In most developing countries, LEGE resources have not received much attention for electricity production. The key reason for not utilizing these resources by most developing countries and several industrialized countries for commercial exploitations is that they are not considered as cost-effective for generating electricity [2]. Recent increases in the cost and uncertainty of future conventional energy supplies for power generation are improving the attractiveness of LEGE resources. Generating electricity from medium- and low-enthalpy geothermal resources (water-dominated resources) can be effectively achieved using a binary cycle method which is also known as organic Rankine cycle (ORC) method [1, 2]. LEGE-ORC technology has virtually no GHG emissions to the atmosphere [1, 2, 3] and is a promising technology due to its simplicity and its limited number of components, all of them being very common and commercially available. In this introductory 2. Basic concept of LEGE-binary ORC technology for electrical power generation A schematic diagram showing a LEGE-ORC binary-fluid system used for electric power generation is shown in Figure 1. Figure 1. A schematic showing the basic concept of LEGE-ORC technology for power generation [1]. In this system, the first (primary) fluid being the geo-fluid (brine) is extracted from the LEGE resource through the production well. [SEP] Justify:

Output: The text corroborates the claim by explaining how low-enthalpy geothermal energy (LEGE) resources can be utilized for electric power generation, highlighting that these resources harness heat from beneath the Earth's surface. It provides examples of countries that have successfully implemented LEGE for electricity generation and mentions specific technologies, such as the organic Rankine cycle (ORC) method, which effectively utilize geothermal heat for power production.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: The surgery performed in the present study included a large abdominal incision, colon resection, anastomosis, and wound closure, all of which has been shown to result in a catabolic stress and immune activation as a compensatory mechanism to deal with the process of healing the wounds [24, 25]. There likely exist multiple mechanisms that drive the compositional and functional changes in the microbiota including redistribution of nutrients away from the gut to healing tissues, immune activation, and changes in luminal and mucosal oxygen status [26]. It is also possible that the microbiota ‘sense’ a dramatic change in host health status and undergo compositional and functional changes as a mechanisms of adaptation until recovery to homeostasis is complete. Further work is needed to determine precisely which of the factors predominate in this model. It is noteworthy that most of the current antibiotic regimens used as prophylaxis agents for intestinal surgery are based on culture-based analyses of expelled stool. The limitation of this approach is that antibiotics are not based on a tissue-specific microbiome. As such, we do not know which tissue microbiota are associated with healing versus non-healing. This may be critically important to prevent complications such as anastomotic leaks, which still persist today at unacceptable rates even under the most expert of care [27]. While antibiotic use has been demonstrated to be highly effective in preventing anastomotic leak, the type, duration, and microbial targets of currently recommended antibiotics have remained empirical and invalidated. Data from the present study would suggest that direct genomic and functional analyses of the tissue-associated microbiota at anastomotic sites may provide a more directed approach to antibiotic use in anastomotic surgery by understanding their effect on the microbial community structure, membership, and function. The observed 500-fold increase in Enterococcus in postoperative anastomotic tissues is interesting. Although enterococci are commensal inhabitants of the mammal gut, they have been observed to be regularly associated with human infections [28]. In terms of intestinal anastomoses, it has been reported that E. faecalis is highly prevalent at leaking anastomotic tissues, although its role as a causative agent in leakage remains unknown [29, 30]. Enterococcus has a high adherence affinity to extracellular cellular matrix proteins such as fibronectin, laminin, and various different types of collagens [31–33] including collagen IV [34]. [SEP] Justify:

Output: The text contrasts the claim by discussing the variability of the microbiome based on individual health status and responses to surgical procedures. It indicates that there are compositional and functional changes in the microbiota due to factors such as nutrient redistribution and immune activation. Furthermore, it highlights the limitation of culture-based analyses of stool, implying that different tissues may harbor different microbiota, thus supporting the idea that microbiomes are not identical across individuals.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: To inspect whether the pattern of GFP signals indeed reflected the expression of D2R, three D2R enhancer driver strains (R72C04, R72C08, and R72D03-GAL4) were crossed with the GFP-tagged D2R strain. R72C08-GAL4 covered three DM1 dopaminergic neurons (Figure S3c), and R72C04-GAL4 labeled one DM1 and two DL2b dopaminergic neurons (Figure S3d and e). R72D03-GAL4 identified parts of mushroom body neurons, whose axons spread on the surface of the mushroom body lobes (Figure S3f); R72C08-GAL4 also identified a subset of neurons from four MB neuroblasts, with soma in four clusters and a converged bundle of axons (Figure S3g and h). These results revealed the expression of D2R in the mushroom body and dopaminergic neurons in the third-instar larval brain. D2R in DAN-c1 influences larval aversive learningOur previous work reported that D2R knockdown (UAS-RNAi) in dopaminergic neurons driven by TH-GAL4 impaired larval aversive learning63. Using a microRNA strain (UAS-D2R-miR)61, a similar deficit was observed (Figure S5c). To further understand the roles of D2R in aversive learning, its expression in distinct DANs, as well as corresponding learning assays were both investigated. Crossing the GFP-tagged D2R strain with a DAN-c1-mCherry strain demonstrated the expression of D2R in DAN-c1 (Figure 4a). Figure 4. Presynaptic D2R in DAN-c1 is necessary for larval aversive learning. (a) D2R is expressed in DAN-c1. The expression pattern of D2R is shown with tagged -c1 is marked by mCherry, and all DANs are marked with TH antibody (blue). (b-c) Knockdown of D2R by D2R-miR reduces fluorescent intensities of D2R-tagged GFP (D2R-GFP). [SEP] Justify:

Output: The text discusses the expression of D2R in dopaminergic neurons and its influence on larval aversive learning but does not mention olfactory receptors or any quantum effects. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: The geo-fluid carries the heat from the liquid-dominated resource and efficiently transfers this heat to the low boiling point (BP) organic working fluid (the secondary/binary fluid) using an effective heat exchanger. Shell-and-tube heat exchangers are widely used in these applications [1, 2]. The ORC is a thermodynamic Rankine cycle that uses the organic working fluid instead of steam (i. e. , water). In this binary-fluid system, the low boiling point organic liquid absorbs the heat which is transferred by the geothermal fluid and boils at a relatively much lower temperature (compared to water) and as a result develops significant vapor pressure sufficient to drive the axial flow or radial inflow turbine. The turbine is coupled to an electric generator which converts the turbine mechanical shaft power into electrical power. The organic working fluid expands across the turbine and then is cooled and condensed in the condenser before it is pumped back as a liquid to the heat exchanger using a condensate pump to be re-evaporated, and the power cycle repeats itself. One of the most important performance criteria in LEGE-ORC power generation technology requires the optimal selection of the ORC organic working fluid. Organic fluids used in binary ORC technology have inherent feature (compared to water) and that is they have low boiling temperature and high vapor pressure at relatively low temperatures, compared with steam (water) [1]. It was noted in [1] that typical ORC organic fluids may include pure hydrocarbons (e. g. , pentane, butane, propane, etc. ), refrigerants (e. g. , R134a, R218, R123, R113, R125, etc. ), or organic mixtures. The optimal energy conversion performance of a LEGE-ORC power generation system depends mainly on the type of organic fluid being used in the system. The selection of the type of organic fluid is typically based on the following criteria [1, 2]:The ORC organic fluid should result in high thermal efficiency by allowing maximum utilization of the available low-temperature geothermal heat source. It should be safe (non-flammable and nontoxic) and non-corrosive. It should not react or disassociate at the pressures and temperatures at which it is used. It should have suitable thermal stability and high thermal conductivity. It should have appropriate low critical temperature and pressure. [SEP] Justify:

Output: The text corroborates the claim by detailing the process through which geothermal energy is utilized for power generation, specifically mentioning how geo-fluid transfers heat to an organic working fluid in the ORC system. It describes the thermodynamic cycle that uses geothermal heat, supports electrical power generation, and emphasizes the importance of utilizing heat from within the Earth, which is the essence of geothermal energy.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: What role, if any, Enterococcus plays in the etiopathogenesis of anastomotic leak will require further study. However, the fact that its abundance is increased to such a high degree raises the possibility that it may complicate healing given that it is known to confer a high degree of pathogenicity when it predominates. In fact, analysis [6] in the current work predicted the capacity of anastomotic tissues-associated microflora to possess coccolysin known as a gelatinase GelE of E. faecalis that is able to degrade collagen. Similarly, Escherichia genus also displayed a dramatic increase in relative abundance (i. e. , 200-fold) at anastomotic tissues which is another commensal capable of confer a pathogenic phenotype [35]. Further work will be necessary to independently assess the virulence potential of strains isolated from anastomotic tissues versus luminal contents in both cases. Given that the normal microbiota provide pathogen colonization resistance [36–38], there may be value in understanding how the microbiota assemble from the lumen to anastomotic tissues over the course of anastomotic healing. For example, our results demonstrated that there is a postoperative decrease in Ruminococcaceae, a family of Firmicutes that belong to obligate anaerobes. It has been recently shown that the families Ruminococcaceae and Lachnospiraceae (Clostridium cluster XIV group) colonize mucosal folds in the proximal murine colon [5]. Taken together, our data indicate that a significant membership of the normal microbiota may be lost at anastomotic tissues, the consequence of which is unknown. The family Lactobacillaceae was also reduced at anastomotic tissues and its known cytoprotective action [39–41] to accelerate healing may play a role in leak when they are critically decreased and additional physiologic stress occurs. Although taxonomic profiling by the 16S rRNA analysis can be very informative, it does not provide any functional context to the bacterial communities. Metagenomics and metatranscriptomics would provide greater detail on their potential and actual functions yet are still technically challenging in terms of analysis. However, using [6], we observed predicted global changes in metabolic and virulence functions of bacterial communities among microbiota associated with anastomotic tissues. [SEP] Justify:

Output: The text contrasts the claim by indicating the variability in microbiome composition. It mentions changes in the abundance of specific bacterial families (e.g., Ruminococcaceae, Lactobacillaceae) at anastomotic tissues, implying that individuals may have different microbiome compositions rather than identical ones. The discussion of the roles and presence of different bacteria also suggests that microbiome composition is not uniform among individuals.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: The TH-GAL4 driver is used to express mCherry. The intensity of D2R-tagged GFP is reduced in the DM1 cluster (yellow arrowheads in b), which is still intact in pPAM neurons (white arrows in c). (d) D2R-GFP fluorescent intensity is quantified by standardizing the values in DM1 with those in the pPAM. Data are shown as mean ± SEM. For the TH-GAL4 group, N=32 from 8 brains; for the TH×D2R-miR group, N=24 from 6 brains. Unpaired t test, ****p < 0. 0001. Scale bar: 20µm. (e) Knockdown of D2R in DAN-c1 by D2R-miR impairs larval aversive learning. QUI, quinine. DW, distill water. SUC, sucrose. Data are shown as mean ± SEM. Two-way multiple comparisons test, **p < 0. 01, ***p < 0. 001, ****p < 0. 0001. numbers, see Table S3. (f-i) D2R knockdown in DAN-c1 does not affect naïve sensory and motor functions. Data are shown as mean ± SEM. One-way multiple comparisons test. numbers, see Table S3. (Note) Figures S5 and S6 show additional information on naïve sensory and motor functions in larvae related to D2R-miR experiments. To reduce the expression of D2R in dopaminergic neurons, a microRNA strain UAS-D2R-miR was used when crossing with distinct driver strains. The efficiency of D2R knockdown was confirmed by crossing the GFP-tagged D2R strain with TH-GAL4;UAS-D2R-miR. In these larval brains, GFP signals in DM1 were not detected, while those in pPAM were still intact (Figure 4b and c). Quantification showed a significant decrease of GFP signals in the knockdown group compared to the control (Figure 4d), indicating reduced transcripts of D2R linked GFP by D2R-microRNA (Figure S4f). [SEP] Justify:

Output: The text is irrelevant (pass) to the claim about olfactory receptors and quantum effects. It discusses the expression and effects of D2R (Dopamine receptor) in specific neuronal clusters, their quantification, and results from various genetic modifications and tests related to D2R in larval brains. There is no mention or implication regarding olfactory receptors or quantum effects.


Input: Task: corroborate the claim using the given text. Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: These units can then be integrated to form power plants of 10–50 MWe which are considered to be small power plants. It was estimated that 1 MWe power plant could serve about 20,000 households assuming that the demand for electricity per person at off-grid sites will be of the order of 500 W [1, 2]. The convenience of the small power plants is most evident for areas without ready access to conventional fuels, but with access to LEGE resources, and for communities that it would be very expensive to connect to the national electric grid [2, 3, 4]. Basic LEGE-ORC power generation systems typically include the following list of equipment [2]:Downwell pumpsGeo-fluid (brine) supply system (sand removal system)Heat exchangers (evaporator/condenser, shell-and-tube type)Turbine generator and controlsCondensate pumpPiping systemHeat rejection system (cooling tower)Backup electric power systemFire protection system (if the ORC working fluid is flammable) The area required to support a geothermal power facility, including the well field, access roads, and auxiliary buildings, depends on the geothermal power plant power rating, the properties of the geothermal reservoir fluid, and the piping system selected for collecting the geo-fluid from the production wells and disposing the waste brine to the reinjection wells. The geothermal power facility should be installed close to the production wells to avoid thermal and hydraulic losses caused by long geo-fluid pipelines. The pipelines used to transport the geo-fluids are usually mounted on stanchions, run along service roads, and incorporate vertical and horizontal expansion loops [2]. It should be noted that a low-enthalpy geothermal power plant typically requires (per MWe) 5% of the area needed for a solar thermal power plant and 2% for a solar PV power plant located in the best solar insolation area in the USA. It was reported also in [2] that a 20 MWe geothermal binary power plant (excluding wells) requires a land area of 1415 m2/MWe. Advertisement 5. Environmental aspects of using LEGE-ORC technology Geothermal energy is relatively free from pollution problems and considered to be a clean technology. It seems to have the largest technological potential compared to other renewable energy sources [1, 2, 3, 4]. [SEP] Justify:

Output: The text corroborates the claim by discussing geothermal energy and its utilization in power generation. It mentions that geothermal power facilities utilize geo-fluid, which is directly related to harnessing heat from beneath the Earth’s surface. Additionally, the text references the efficiency and clean nature of geothermal energy, reinforcing its role in power generation.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: These findings provide a path forward in which we can apply this technique to models of anastomotic leak and determine its predictive capacity and its ability to inform a microbial mechanism for anastomotic leakage. ConclusionAn anastomotic injury appears to significantly alter the tissue-associated microbiota without affecting the luminal microbiota. A more detailed analysis of the tissues associated microbiota over the full course of anastomotic healing could lead to novel strategies for a leak prevention that may include allowing for a balance of microbial community structure, membership, and function. MethodsRat model of anastomosisfor at the University of Chicago approved all animal experiments. Adult, male Wistar rats 250–300 g () were used for all experiments and were allowed unrestricted access to rat chow and tap water. Prior to surgery, rats were sedated with 40–80 mg/kg ketamine, 5–10 mg/kg xylazine via an intraperitoneal injection. Using aseptic technique, rats were subjected to an abdominal incision (laparotomy) and a 0. 5-cm distal colon resection followed by an end-to-end rectosigmoid reconnection (anastomosis) using 12 interrupted 6–0 non-absorbable sutures. Colon luminal content was collected for DNA isolation. The resected piece of colon was washed with saline and then swabbed for tissue associated microbiota. In all cases, anastomotic integrity was confirmed immediately after construction by injecting saline into the rectum and observing that the anastomotic was water tight. On postoperative day 6 all rats were sacrificed and the luminal contents adjacent to anastomosis were collected. For tissue-associated microbiota, anastomotic tissues were removed, and mechanically homogenized, and then DNA was extracted. [SEP] Justify:

Output: The text contrasts the claim by discussing the variability in microbiome composition associated with anastomotic injury. It highlights that the tissue-associated microbiota is significantly altered, implying that individual microbiome compositions can differ based on specific conditions, thus rebutting the idea that everyone's microbiome is identical.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: To investigate the roles of D2R in distinct dopaminergic neurons during larval associative learning, UAS-D2R-miR strain was crossed with distinct single dopaminergic neuronal drivers. Among them, the knockdown of D2R in DAN-c1 impaired aversive learning with the odorant pentyl acetate, while appetitive learning was unaffected (Figure 4e). In contrast, although D2R was also found in DAN-d1 and DAN-g1, neither D2R knockdown in DAN-d1 nor in DAN-g1 affected larval olfactory learning (Figure S4c-e). As the naïve sensory and motor functions were not affected, this deficiency was caused by impairment in learning abilities (Figure 4f-i). Similar learning deficits were observed in the same strain trained with another odorant, propionic acid (Figure S5a), as well as in larvae with D2R knockdown using UAS-RNAi (Figure S5b). These results demonstrated that D2Rs are expressed in DAN-c1, and they are necessary for larval aversive learning. Presumably the knockdown of presynaptic inhibitory D2R autoreceptors on DAN-c1 will result in increased and excessive dopamine release, which leads to aversive learning deficiency. These results are consistent with the activation studies with dTRPA1 above, in which increased dopamine release during training results in impaired aversive learning (Figure 2k). Over-excitation of DAN-c1 during learning impairs larval aversive learningTo exclude possible chronic effects of D2R knockdown during development, optogenetics was applied at distinct stages of the learning protocol. Channelrhodopsin2 (ChR2) is a blue light activated cation channel from algae, which can be used to activate target neurons64,65. Over-excitation of DANs under a TH-GAL4 driver with ChR2 during training impaired aversive learning but left appetitive learning intact (Figure S7), which is consistent with D2R knockdown results. To investigate the mechanisms with a better temporospatial resolution, ChR2 was expressed in DAN-c1, and blue light was applied at distinct stages of the learning protocol (Figure 5a). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the role of D2R in larval associative learning related to olfactory stimuli, but does not mention olfactory receptors functioning through quantum effects. It focuses on dopamine receptors and learning deficits rather than quantum biology.


Input: Task: corroborate the claim using the given text. Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: Emissions are typically zero when LEGE reservoirs are utilized using ORC binary technology, since all of the produced geo-fluid is injected back into the reservoir. Some chemical constituents of geothermal water (e. g. , trace metals) may need to be monitored in case their concentrations exceed permitted pollution limits. One of the effective ways of getting rid of hazardous chemicals is reinjection. Low-enthalpy geothermal binary power generation systems are far less environmentally intrusive than alternative power generation systems in several respects, for example, they are essentially zero-GHG emission systems and have low land usage per installed megawatt [2]. As far as physical environmental effects, geothermal projects may cause some kind of disruption activities as other same size and complexity of civil engineering projects. Also, the locations of excavations and sitting of boreholes and roads will have to be taken into account; soil and vegetation erosion, which may cause changes in ecosystems, has to be watched. There is considerable noise involved during geothermal drilling, construction, and production phases of development, so that protection has to be ensured for residents, and some permanent noise-reduction measures may need to be considered. It should be noted that many geothermal developments are in remote areas where the natural level of noise is low and any additional noise is very noticeable [1, 2]. At the social-economic level, the construction of geothermal power installations involves a temporary increase in employment, and the building of new roads may open up areas and possibly increase tourism, especially if natural geothermal manifestations are left intact. Geothermal power facilities utilize low-source temperature to produce the primary thermal energy for conversion to power production, and therefore the waste heat per MWe of electricity generated is much larger than others types of power generation [2]. Appropriate measures should be applied to prevent leakage of the binary working fluid from ORC power generation units to the environment [1, 2]; normally the installations of these units are made perfectly tight to meet high safety requirements. Advertisement 6. Economic aspects of using LEGE-ORC technology Generating electricity using geothermal ORC technology is very cost-effective and reliable [1, 2]. Table 1 [2] compares the unit cost of electricity generated from low enthalpy-based small power plants. Moreover, the unit cost of electricity from small-scale geothermal plants (<5 MWe) is much lower than the average cost of 0. [SEP] Justify:

Output: The text corroborates the claim by explaining the process of geothermal power generation, specifically mentioning that geothermal power facilities use low-source temperature to produce thermal energy for conversion into power. This supports the idea that geothermal energy harnesses heat from beneath the Earth’s surface.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: 16S rRNA analysis of bacterial compositionThe following are the four groups of samples in our studies: (1) POD0 tissue for which the resected piece of colon was washed with saline and then swabbed for tissue associated microbiota; (2) POD0 luminal bacteria representing luminal content from a resected piece of colon; (3) POD6 tissue for which tissues at the site of anastomosis were collected, washed with saline, and then swabbed; (4) POD6 luminal bacteria representing luminal content from collected piece of anastomotic site of the colon. Twenty rats were included in the experiment. Samples with sequences containing low confidence base calls (phred < 20) were eliminated. Samples were analyzed by 16S rRNA V4 iTAG amplicon sequencing analysis. Following joining of paired 2 × 150 bp MiSeq read sequences () containing low confidence base calls (phred < 20) were dropped, and the remaining sequences clustered into 97% similarity operational taxonomic units (OTUs) using uclust [42, 43]. After dropping clusters composed of a single sequence, OTUs were assigned taxonomic annotations by aligning representative members of each cluster to the greengenes 12_04 database using the RDP classifier [44, 45]. Sequencing depth among samples was normalized by rarefaction to a depth of 3,000 sequences per sample. Along with clustering, taxonomic assignment, and rarefaction, PCoA plots were derived from weighted unifrac distance matrices using the QIIME v1. 6. 0 software suite [46]. Significant differences in the abundance of taxonomic groups between treatments were calculated with Student’s t test followed by the Benjamini-Hochberg correction for multiple comparisons using an alpha of 0. 05 [47]. Taxa tree was generated using GraPhlAn. Functional analysis of bacterial compositionWe predicted the functional composition and abundance of the microbial community using on the OTUs derived from the 16S rRNA analysis [6]. Differential functional abundance between postoperative day 0 and day 6 for stool contents and anastomosis tissue was performed using the Wilcoxon rank sum statistical test, and p values were Bonferroni corrected for multiple testing. [SEP] Justify:

Output: The text contrasts the claim by presenting detailed methodologies and results from a study on the analysis of microbiome composition, which inherently implies variability among individuals due to the diverse group of samples analyzed and the differences in bacterial abundance measured. It describes distinct groups of samples that show significant differences in taxonomic groups, indicating that microbiome compositions are not identical across individuals.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: Optogenetic activation of DAN-c1 during training impaired aversive learning, not appetitive learning (Figure 5b-d). This result is consistent with the effect of D2R knockdown in DAN-c1, indicating that increased, excessive dopamine release during training leads to impaired aversive learning. Figure 5. Over-excitation of DAN-c1 impairs larval aversive learning. (a) A schematic diagram of optogenetic manipulations of neuronal excitability in DAN-c1 during distinct stages in learning. (b-d) Activation of DAN-c1 during training impairs larval aversive learning (b), while keeps appetitive learning intact (d). Unconditioned stimuli used were quinine (b) and sucrose (d). No learning behaviors are observed in the control distilled water (DW) groups (c). Third-instar larvae with ChR2 expression in DAN-c1 are used. ATR, all-trans-retinal. Data are shown as mean ± SEM. Two-way multiple comparisons test, ***p < 0. 001, ****p < 0. 0001. numbers, see Table S4. D2R in mushroom body mediates larval learning through inhibitionWe have shown that D2R in DAN-c1 plays a critical role in larval aversive learning. Since D2Rs are also expressed in soma and axons in most mushroom body neurons (Figure 3h and i), we examined the role of D2R in MB neurons, the center for learning in Drosophila. Knockdown of these D2Rs by D2R-miR impaired both appetitive and aversive learning (Figure 6a). Similarly, optogenetic activation of mushroom body neurons during training led to deficiencies in both appetitive and aversive learning (Figure 6b and d). These deficiencies were not observed in larvae with activation during the resting stage. As D2Rs are inhibitory receptors, and optogenetic activation leads to greater neuronal excitation like what may occur with knockdown of D2Rs, these data show that the inhibitory effect of D2Rs in mushroom body neurons is necessary for larval olfactory associative learning to occur. Figure 6. D2R in mushroom body is necessary for both aversive and appetitive learning. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the role of D2 receptors in larval aversive and appetitive learning without addressing olfactory receptors or any potential quantum effects. It focuses on experimental findings concerning dopamine signaling and learning in Drosophila larvae, which does not relate to the proposed quantum functions of olfactory receptors.


Input: Task: corroborate the claim using the given text. Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: 25 US$/kWh supplied through diesel generators [2]. The total investment for a geothermal power plant mainly includes the following: (1) cost of drilling, (2) cost of exploitation, (3) operating and maintenance costs, and (4) cost of power plant (capital cost of design and construction) [1, 2]. The first two types are referred to as subsurface costs, whereas the other two are referred to as surface costs. For small-scale geothermal power plants (<5 MWe) utilizing LEGE resources, the subsurface cost typically accounts for approximately 30% of the total investment costs, whereas the surface cost accounts for the remaining 70%. For larger geothermal power plants (>5 MWe) utilizing high-enthalpy resources, the surface cost is a small part of the total cost of the project. The major cost involved in larger plants seems to be the subsurface cost. Generating electricity using LEGE-ORC technology is very reliable due to its advanced technological aspects [2]. However, the maintenance costs and shutdowns could be reduced when the technical complexity of the plant is on a level that is accessible to local technical personnel or to experts who are readily available [2]. Also, reducing operations and maintenance costs can be achieved through advanced methods to control the geo-fluid (brine) chemistry and the use of more robust instruments for tighter controls [1, 2]. Geothermal ORC power generation plants are normally constructed and installed in small modular power generation units. These units can then be linked up to create power plants with larger power production rates. Their cost depends on a number of factors, but mainly on the temperature of the geothermal fluid produced, which influences the size of the ORC turbine, heat exchangers, and cooling system. The total size of the plant has little effect on the specific cost, as a series of standard modular units is linked together to obtain larger power capacities [2]. The modular units have a satisfying economic efficiency, because modular construction reduces installation time and costs. Ultimately, the economic viability of the geothermal power plant depends on its ability to generate revenue in the long term. This is because of relatively lower unit cost of electricity generated by geothermal plants and the future power-sale factor. [SEP] Justify:

Output: The text corroborates the claim by discussing the various costs associated with geothermal power plants, specifically mentioning geothermal ORC (Organic Rankine Cycle) technology, which is a method of electricity generation utilizing heat from geothermal sources. The text elaborates on the economic factors of geothermal power generation, highlighting its reliability and the relationship between subsurface costs and power generation capacity, which aligns with the claim that geothermal energy harnesses heat from beneath the Earth's surface for power generation.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: This project has three main goals:First, to observe and characterize trends in the effects of Soylent use on the microbiome. Second, to provide a rich, hands on microbiome research experience to a group of undergraduates. Third, to establish a student organization to regularly discuss microbiome related topics and document the course of the study online. Experimental Design:Participants will log their diets and bowel movements electronically, and sample their microbiome on each indicated day. Day 1 - 2: Participants eat regularly. Day 3 - 5: Split into two groups. 7 participants will switch to a Soylent diet. 3 participants will retain a regular diet. Day 6: All participants return to regular diets. Day 10: Last sample to observe long term recovery. wait. . . Thanks to a generous partnership with uBiome, the budget will now fund ~15 participants. Each participant's microbiome will be sampled 7 times, for a total of 105 uBiome sampling kits. Participants will consume Soylent 2. 0, the leading meal-alternative. Nutritional information and composition can be found here. Recruitment funds will be used to raise awareness and advertise the project to attract candidates for participation. Candidates will learn as part of our student organization in regular meetings about the study and microbiome research overall. Funds raised past our goal will be used to increase the sample size and fund more participants. Endorsed byhas been an excellent undergraduate in my laboratory working on the quantitative dynamics of human microbiome assemblies for over a year. This project expands his focus to the impact of constructed diets on the human gut microbiome. There have been other studies on diet-microbiome interaction revealing startling impacts on the host, but this one focuses on an emerging very restrictive diet that is gaining in popularity rapidly. This project will throw needed light how this diet affects this health-critical bacterial community. Adam ArkinProfessor, University of California, BerkeleyWe are so excited to collaborate on this project to test how Soylent changes the gut microbiome. We hope to find some interesting data and further scientific curiosity in this field! CEO uthe & jr. of are a group of motivated undergraduate researchers at UC Berkeley. Together, we are forming a student organization that will educate students on current microbiome advances and conduct this student-run study. We aim to both contribute to the field of microbiome research and inspire our study's participants. Ryan HsuI'm a Microbiology and student at UC Berkeley. [SEP] Justify:

Output: The text contrasts the claim by discussing how the project is designed to observe and characterize trends in the effects of Soylent use on the microbiome, implying that individual microbiome compositions can vary based on diet. It mentions sampling microbiomes multiple times and highlights the variability of the microbiome in response to different diets, which suggests that microbiome compositions are not identical across individuals.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: (a) Knockdown D2R in mushroom body neurons (MBNs) impairs larval aversive and appetitive learning. (b-d) Activation of MBNs during training impairs both larval aversive and appetitive learning. Unconditioned stimuli used were quinine (b) and sucrose (d). No learning behaviors are observed in the control distilled water (DW) groups (c). Third-instar larvae with ChR2 expression in MBNs (201Y-GAL4) are used. ATR, all-trans-retinal. Data are shown as mean ± SEM. Two-way multiple comparisons test, *p < 0. 05, **p < 0. 01, ***p < 0. 001, ****p < 0. 0001. numbers, see Table S3 and S4. DiscussionThe dopaminergic system plays an important role in Drosophila olfactory associative learning, but the roles of D2R in this process have not been fully explored. In this study, we systematically investigated the expression pattern of D2R in the third-instar larval brain as well as its role in larval aversive and appetitive learning. One driver strain singly identifying the DAN-c1 neuron was discovered and learning assays with thermogenetic tools (Shibirets1, dTRPA1) determined that the blockade of dopamine release from DAN-c1 impeded aversive learning, while its activation during training led to repulsion toward the odor in the absence of unconditioned stimulus (i. e. , QUI). These results revealed that DAN-c1 activation (i. e. , presumably leading to the release of synaptic dopamine) is both necessary and sufficient for larval aversive learning to occur. Subsequently, the expression pattern of D2R was explored by using a GFP-tagged D2R strain, including distinct dopaminergic and mushroom body neurons. D2Rs were expressed in DAN-c1, and the knockdown of these receptors resulted in aversive learning deficiency. These data suggest that presynaptic D2Rs in a single dopaminergic neuron, DAN-c1, regulate dopamine release during excitation whereas knockdown of these same receptors leads to excessive dopamine release, causing deficits in aversive learning to occur. [SEP] Justify:

Output: The text discusses the role of dopamine receptors in Drosophila olfactory associative learning but does not mention quantum effects or olfactory receptors functioning through such mechanisms. Hence, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: is Open Access? is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers. are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn 10. 5772/intechopen. 101577 this 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation Intechby Basel I. From the Edited by Basel I. purchase hard copies of this book, please contact the representative in India: CBS Publishers & Distributors Pvt. Ltd. www. cbspd. com | customercare@cbspd. com 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation Impact of this 1. IntroductionThe term “Geothermal Energy” refers to the Earth’s natural heat energy. The continual heat energy flux coming from the Earth’s core to the surface is the source of geothermal energy. The Earth’s geothermal resources are huge; for example, the portion of geothermal energy stored at a depth of 3 km is estimated to be 1,194,444,444 TWh, which is substantially more than the total energy equivalent of all fossil fuel resources combined, which is estimated to be 1,010,361 TWh [1]. Geothermal energy is regarded as an environmentally beneficial clean energy source that, when used to generate electrical power, has the potential to considerably reduce GHG emissions. It is reported that global net electrical power demand will rise by nearly 85% between 2004 and 2030, increasing to 30,364 TWh in 2030 from 16,424 TWh in 2004, making the use of geothermal energy for electricity generation an appealing solution, especially with advances being made in innovative technological methods of drilling and power generation schemes. [SEP] Justify:

Output: The text corroborates the claim by stating, 'The term “Geothermal Energy” refers to the Earth’s natural heat energy.' It further explains that the heat energy flux from the Earth’s core is the source of geothermal energy, which directly supports the idea of harnessing heat from beneath the Earth’s surface for power generation. The text also emphasizes the potential of geothermal energy in electricity generation and reducing greenhouse gas emissions, reinforcing the concept that geothermal energy can be used for power generation.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: I have also spent over a year conducting research on synthetic human microbiomes as part of the at National Laboratory. I'm excited to see what we can learn about the microbiome together! Thanks for your support! Dylan McCormickI am a Molecular and student at UC Berkeley. I have previously worked in UC Berkeley's Museum of Vertebrate Zoology, and I am currently researching microbial genetics in the at UC Berkeley. I can't wait to help discover more about the human gut microbiome! Kate SchreinerI am an and English student at UC Berkeley. I have spent over a year working in UC Berkeley's Museum of Vertebrate Zoology. I plan on attending medical school in two years, so I am excited to learn more about the human microbiome! jr. I'm an student at UC Berkeley. I have spent over two years working in the UC of Vertebrate Zoology, and I plan on attending medical school in the future. I look forward to learning about the human gut microbiome! Thank you for your support! Lab Notes10 PostedLong awaited updates (results and more)! October 19, 201711201113Data! March 10, 20179101819A super late update! February 10, 201755363Additional InformationAny additional funds raised past our goal will be used to increase the sample size and increase the number of microbiome measurements we will take. FAQ Q: Is 3 days of Soylent long enough to observe a change? Is a sample size of 10 large enough for this study? A: In our revised study design, participants consume Soylent for 4 days. The protocol we propose is designed similarly to the study here. In this paper, a similar sample size of about 10 participants were given meat-rich or vegetable rich diets for five days, and microbiome compositions were rapidly and reproducibly changed within 2 days. Any funding we raise past our goal will be used to increase our sample size. Citation: a, et al. (2014) Diet rapidly and reproducibly alters the human gut microbiome. Nature 505(7484):559–63. Q: How can you control for Soylent/conditions if everybody's microbiome starts off differently? A: Everybody's microbiome does start off differently. As such, it is impossible to do a real soylent + - control without somehow duplicating the person and their microbiome. However, out method has two controlled aspects. [SEP] Justify:

Output: The text contrasts the claim by stating that 'Everybody's microbiome does start off differently,' which directly refutes the idea that everyone's microbiome composition is identical. It emphasizes the uniqueness of each individual's microbiome, highlighting the complexity and variability among different people's microbiomes.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: Furthermore, the activation of DAN-c1 with optogenetic tools during training, resulting in excessive dopamine release, impaired aversive learning, as well. Finally, it was demonstrated that either the knockdown of postsynaptic D2R or activation of mushroom body neurons led to learning deficits. These data demonstrate that D2Rs in distinct brain locations are critically involved in associative learning. Insights into the neuronal circuits underlying larval olfactory associative learningMushroom body and dopaminergic neurons play important roles in Drosophila associative learning66–69. In larvae, olfactory information (odors, Conditioned Stimulus, CS) is received by olfactory sensory neurons (OSN), then transmitted to the mushroom body via projection neurons (PN)70. The mushroom body (MB) is the learning center of Drosophila and composed of Kenyon cells (KC, or mushroom body neurons, MBN)71–73. Their dendrites form the calyx, receiving olfactory information from projection neurons. The axons converge into peduncles, then branch into the vertical and medial lobes. Distinct gustatory cues (taste, Unconditioned Stimuli, US) are sensed by gustatory sensory neurons (GSN) and transferred to dopaminergic neurons in different clusters (Figure 7A). DAN-c1 in the DL1 cluster mediates aversive cues, projects to the lower peduncle (LP) in the mushroom body. The plasticity of synapses from MBNs to MB output neurons (MBON) is negatively modulated by dopamine67,74,75. The MBN-MBON synapses in the vertical lobe and peduncle are responsible for attraction, while those in the medial lobe are for repulsion67,75. Figure 7. Roles of D2Rs in dopaminergic and mushroom body neurons during larval olfactory learning. (a) A schematic diagram shows the roles of D2R in dopaminergic neurons (DAN) and mushroom body neurons (MBN) in larval olfactory associative learning. During learning, olfactory stimuli (conditioned stimulus, CS) are received by olfactory sensory neurons (OSN) and transmitted to MBNs (green) via projection neurons (PN). [SEP] Justify:

Output: The text does not address the claim about olfactory receptors functioning through quantum effects. Instead, it discusses the role of D2 receptors and dopaminergic neurons in associative learning processes in Drosophila larvae, specifically related to olfactory information processing. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: Geothermal energy resources differ regionally based on the temperature and depth of the resource, the availability of ground water, and the chemical composition of the rock [2]. It is distinct from other conventional and renewable energy sources in that it is always accessible, steady throughout the year, regardless of weather conditions, and has an inherent storage potential. The temperature of geothermal energy resources typically ranges approximately from 50 to 350°C. Geothermal resources near volcanic regions and island chains tend to have a high resource temperature with temperature typically greater than 200°C. Medium-temperature, ranging from 150 to 200°C, and low-temperature geothermal resources of less than 150°C are typically found widely in most continental regions and considered to be the most commonly available geothermal energy resources [3]. The geothermal binary cycle technology known as the Cycle (ORC) technology can successfully generate power from medium- and low-temperature geothermal energy (LTGE) resources. LTGE-ORC technology emits almost no greenhouse gases into the environment and is an appealing technology because of its simplicity and small number of components, all of which are common and commercially accessible. More information related to a number of past and existing successful ORC binary power plants can be found in Refs. [1, 2, 3, 4]. In this introductory 2. Fundamental concept of a binary fluid ORC system using LTGE resourcesFigure 1 depicts a schematic representation of an ORC binary fluid system utilized for electric power generation using LTGE resource. Figure 1. A schematic showing the fundamental concept of an ORC binary fluid technology utilizing LTGE resource for power generation. The initial (main) fluid collected from the LTGE resource via the production well is the geo-fluid. The geo-fluid transports heat from the liquid-dominated LTGE resource (being the heat carrier) and effectively transfers this heat to the low-boiling point organic-based working fluid (the secondary fluid) through an efficient heat exchanger. Typical ORC organic fluids may include pure hydrocarbons (e. g. , pentane, butane, propane), refrigerants (e. g. , R134a, R218, R123, R113, R125), or organic mixtures. More details about the selection criteria of these ORC organic fluids for optimal performance can be found in Refs. [1, 2, 3, 4]. [SEP] Justify:

Output: The text corroborates the claim by explaining how geothermal energy resources are used for power generation. It describes the process of heat transfer from liquid-dominated low-temperature geothermal energy (LTGE) resources via a binary fluid organic Rankine cycle (ORC) system, emphasizing that this system effectively utilizes the heat from beneath the Earth's surface for generating power. It provides specific details about the operational mechanics of the system, thus supporting the claim about geothermal energy's function.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: Firstly, the two days prior to Soylent consumption will provide a baseline microbiome of each participant's unique microbiome, acting as an internal control. The individuals that do not consume Soylent will act as a control for day-to-day variance of microbiome fluctuations. Q: What about human testing approval? A: --Update 10/2016-- We passed our IRB review! We have an IRB (human experimentation ethics review) underway and will also require approval from each participant before we begin testing. The study is considered minimally invasive since Soylent is regulated as food by the FDA and the uBiome sequencing is done from an external sample. of Bacteria to Microbiomes: How did so many bacteria get inside of you? From the moment you were born, your body has been collecting bacteria from the environment, which populate both your outside and inside. The main source of bacterial diversity comes from your food. Your body replenishes bacterial diversity from the varieties of food that you eat. Interestingly, Soylent is sterile, which means there are no living bacteria in the drink. This may have interesting effects on the replenishment of bacteria for Soylent drinkers. Sugars/Carbohydrates: Each species of bacteria has a unique metabolism that determines how well it can utilize the nutrition it is fed. Sugars, or carbohydrates, are the main source of energy for bacteria, and most people have diets that include a wide range of sugars. Soylent is designed with one main sugar, isomaltulose, which may preferentially select for certain bacteria, while negatively affecting other bacteria. Special thanks to for his great work on the promotional video. Richard Farman: LinkedIn, farmanrl@whitman. eduProject Backers84Backers106%Funded$6,405Total Donations$76. 25Please wait. . . [SEP] Justify:

Output: The text contrasts the claim by explaining that individuals have unique microbiomes influenced by various factors such as their food sources and external environments. It mentions that 'the main source of bacterial diversity comes from your food' and discusses how different diets, like that including Soylent, can lead to varying effects on microbiome composition.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: Distinct gustatory stimuli (unconditioned stimulus, US) are received by gustatory sensory neurons (GSN) and transferred to different dopaminergic neurons. Aversive stimuli are sent to DAN-c1 (red) in the DL1 cluster which connects the lower peduncle compartment (LP, square in dash line), while appetitive stimuli are received by pPAM neurons (blue) innervating the medial lobe (ML). D2Rs (yellow square) are expressed in DAN-c1 and pPAM as autoreceptors, regulating dopamine release. Both D2R and dDA1 (magenta circle) are expressed in the MBNs. (b) A hypothetical curve showing the relationship between learning ability and cAMP level in the mushroom body. Insufficient cAMP (rutabaga mutant) cannot induce learning, while excessive cAMP (dunce mutant) also impairs learning. Only the appropriate level of cAMP regulated by the opposing actions of D1R (dDA1) and D2R leads to successful learning in wild type larvae (WT). Knockdown of D2R in DAN-c1 causes excessive dopamine release, elevating cAMP and resulting in impaired learning. D2R knockdown in MBNs relieves the inhibition effect of D2R, resulting in excessive intracellular cAMP and learning failure. (c-d) Potential molecular mechanisms underlying Drosophila olfactory learning in the square region shown in (a). (d) During aversive learning, olfactory stimuli induce depolarization of MBNs, which activates voltage-gated calcium channels and induces calcium influx. Gustatory stimuli, such as quinine, activates dopaminergic neurons and elevates dopamine (DA) release. D1 receptor (dDA1) activates adenylyl cyclase (AC) and elevates cAMP via Gαs, while D2 receptor (D2R) inhibits AC and suppresses cAMP via Gαi/o. In Drosophila, the coincidence detector rutabaga (AC) is activated by the existence of both calcium and Gαs, converging the olfactory and gustatory stimuli. cAMP activates the PKA signaling pathway, elevating the neuronal excitability. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the mechanisms of gustatory stimuli and dopamine regulation in Drosophila, without mentioning olfactory receptors or any potential quantum effects. Therefore, it does not provide any support or rebuttal to the idea that olfactory receptors may function through quantum effects.


Input: Task: corroborate the claim using the given text. Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: The ORC is a thermodynamic Rankine cycle that uses the organic working fluid instead of steam (water). In this binary fluid system, the low-boiling point organic liquid absorbs the heat which is transferred by the geo-fluid and boils at a relatively much lower temperature (compared to water) and as a result develops significant vapor pressure sufficient to drive the axial-flow or radial-inflow turbine. The turbine is coupled to an electric generator, which converts the turbine mechanical shaft power into electrical power. The organic working fluid expands across the turbine and then is cooled and condensed in the condenser before it is pumped back as a saturated liquid to the heat exchanger using a condensate pump to be re-evaporated, and the power cycle repeats itself. Advertisement 3. Thermodynamic analysis of an ORC system utilizing a low-temperature geothermal energy resource: A case study with numerical illustrationA small-scale modular binary fluid ORC geothermal power generation system is proposed for design and installation in Vancouver, BC Canada, near a site characterized by a low-temperature geothermal energy resource. The production well in this resource is capable of supplying hot geo-fluid (mainly liquid water) at a temperature of 90°C (see state a, Figure 1). The proposed ORC system utilizes R-134a as a working fluid. In this ORC system shown in Figure 1, it is required to provide R-134a at a mass flow rate ṁRof 6. 25 kg/s as saturated vapor at 85°C to an inflow radial turbine (state 3). The ORC condenser operates at constant pressure with a constant phase-change temperature of 40°C. R-134 then enters an ideal ORC-pump as a saturated liquid (state 1, Figure 1). The density of the geo-fluid is assumed to be constant at approximately 1000 kg/m3. The geo-fluid liquid exits the ORC-evaporator (state b,Figure 1) to be re-injected into the geothermal resource at 35°C with constant specific heat capacity Cp,Geo = 4. 185 kJ/kg. °C. [SEP] Justify:

Output: The text corroborates the claim by describing the process of an Organic Rankine Cycle (ORC) that utilizes geothermal energy. It explains how the system absorbs heat from a low-temperature geothermal resource to generate power through the use of an organic working fluid, thus demonstrating that geothermal energy is indeed harnessed for power generation.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: Diet may influence the oral microbiome composition in cats access Published: 09 June 2016 Diet may influence the oral microbiome composition in cats Christina J. Adler1,2, Richard Malik3, Gina V. Browne1,2 & …Jacqueline M. Norris4 Show authors Microbiome 0. 001). Amongst this higher diversity, cats on dry-food diets had a higher abundance of Porphyromonas spp. (p < 0. 01) and Treponema spp. (p < 0. 01). ConclusionsWhile we observed differences in the oral microbiome between cats on the two diets assessed, the relationship between these differences and gingival health was unclear. Our preliminary results indicate that further analysis of the influence of dietary constituents and texture on the feline oral microbiome is required to reveal the relationship between diet, the oral microbiome and gingival health in cats. BackgroundThe microbiome of the gingival cleft is of great interest in human dentistry because the two most important diseases of the teeth and periodontium in human patients, dental caries and periodontitis [1], are related to changes in the relative contribution of various potentially pathogenic bacteria in the complex biofilm referred to as dental plaque [2]. As a result, there is considerable information on the oral microbiome of human patients, how this is associated with different disease conditions, and how this is influenced by diet [3, 4]. This includes studies using ancient deoxyribonucleic acid (DNA) which have shown that systematic changes in the microbiome are correlated with changes in diet, in both a contemporary and an evolutionary sense [5]. Cats, like humans, are very commonly affected by periodontal disease, with a consensus that it is the most common disease of feline patients in developed nations [6]. While periodontal disease is seen in cats of all ages, it is generally considered to progress with age, although its extent and severity are impacted on by such factors as diet and co-morbid disease (especially kidney disease and infection with feline immunodeficiency virus and/or feline calicivirus). Indeed, some feline diets are specifically formulated to prevent and/or ameliorate the severity of feline periodontal disease. [SEP] Justify:

Output: The text contrasts the claim by indicating that there are observable differences in microbiome composition based on diet, specifically in cats. It mentions that the oral microbiome changes with different diet types and that systematic changes in microbiomes correlate with dietary changes, suggesting that microbiomes are not identical across individuals.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: D1 and D2 receptors can also form heteromeric receptors and activate the PLC-PKC and CaMKII signaling pathways via Gαq. These pathways inhibit acetylcholine (ACh) release from MBNs to MB output neurons (MBON), which leads to avoidance of the learned odor. Abbreviations: AC, adenylyl cyclase; ACh, acetylcholine; ATP, adenosine triphosphate; CaM, calmodulin; 2+/calmodulin-dependent protein kinase II; cAMP, cyclic adenosine monophosphate; CS, conditioned stimulus; DA, dopamine; DAN, dopaminergic neurons; DL, dorsolateral; ER, endoplasmic reticulum; Gαi/o, Gi/o protein α subunit; Gαq, Gq protein α subunit; Gαs, Gs protein α subunit; GSN, gustatory sensory neurons; LP, lower peduncle; MBN, mushroom body neurons; MBON, mushroom body output neurons; ML, medial lobe; nAChR, nicotinic acetylcholine receptor; OSN, olfactory sensory neurons; PKA, protein kinase A; PKC, protein kinase C; PLC, phospholipase C; PN, projection neurons; pPAM, primary protocerebral anterior medial; RyR, ryanodine receptor; US, unconditioned stimulus; VGCC, voltage gated calcium channel. When only the odorant appears, the subset of Kenyon cells representing this odor are depolarized, inducing calcium influx73. As a balance exists between compartments across the mushroom body lobes, the response to the odor depends on the naïve olfactory circuits from projection neurons to the lateral horn (LH). In aversive learning, in addition to olfaction induced calcium influx, gustatory stimuli also lead to dopamine release from DAN-c1 and subsequently, activation of Gαs in LP54. The co-existence of calcium and Gαs activates a Ca2+-dependent adenylate cyclase (AC), rutabaga (rut)76–79. Rutabaga converges information from both olfaction and gustation, working as the coincidence detector of associative learning. [SEP] Justify:

Output: The text is irrelevant to the claim as it provides detailed information about various receptors and their signaling pathways without mentioning olfactory receptors functioning through quantum effects. It discusses mechanisms in aversive learning related to olfaction and gustation but does not support or refute the specific claim regarding quantum effects in olfactory receptors.


Input: Task: corroborate the claim using the given text. Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: Thermal design constraints: The ORC-evaporator (counter flow-HEx) effectiveness εEvap,ORC= 90%; the turbine isentropic efficiency ηT = 100%; negligible pressure drops in the ORC-piping systems. Electric generator specification: ηEG = 92%. Electricity-driven pump efficiency = 100%. Required: For this conceptual ORC power generation system, the following thermodynamic performance indicators are determined:The R-134a-pumping power ẆPump (kW) requirement for the ORC system. The heat transfer rate input Q̇Evap,ORC (kW) to the ORC system. The turbine supplied power ẆTurb (kW). The electric generator power output ẆEG (kW). The net power Ẇnet,ORC (kW) delivered by the ORC system. The thermal efficiency ηth,ORC (%) of the ORC system. The specific heat transfer qGeo (kJ/kg) supplied by the low-temperature geothermal resource to the ORC system. The heat transfer rate Q̇Geo(kW) supplied by the LTGE resource to the ORC system. The mass flow rate of the geo-fluid ṁGeo(kg/s) to be extracted from the low-temperature geothermal resource. The volumetric flow rate of the geo-fluid V̇Geo(L/min) to be extracted from the geothermal resource. The overall energy conversion efficiency ηGeo (%) of the low-temperature geothermal ORC power generation system. Advertisement 4. Methodology, analysis, and resultsApplication of the steady-state energy balance, mass balance, and other thermodynamic relationships over the ORC pump (stream 1–2), turbine (stream 3–4), evaporator (stream 2–3), electric generator (EG), and the geothermal resource (stream a-b), yields the following set of model equations (Tables 1 and 2):State #T (°C)P (kPa)v (m3/kg)h (kJ/kg)s (kJ/kg. °C)x (%)1401017. 00. 000873256. 540. 022926. 2258. 213852926. 2428. 101004409. [SEP] Justify:

Output: The text corroborates the claim by discussing the performance indicators and thermodynamics of a system that utilizes low-temperature geothermal resources for power generation. Specifically, it mentions the 'specific heat transfer qGeo' and 'heat transfer rate Q̇Geo' supplied by the 'geothermal resource', which indicates that geothermal energy is indeed harnessed for power generation.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: To date, there have only been a handful of in-depth genetic studies of the feline oral microbiome [14–16], and these have not considered the contribution of diet to the observed findings. The present work evaluates the composition of the feline oral microbiome using next-generation sequencing (NGS) and, in a preliminary fashion, the contribution of diet to the composition of the microbiome. The two mutually exclusive dietary categories fed to cats for months to years were (i) dry extruded kibbles (highly refined, cereal-based, dehydrated rations) and (ii) wet (canned [any type] and/or fresh meat [uncooked chicken, or lamb or beef; off or on the bone] combinations, high in water content, protein and fat, but low in carbohydrate). Our findings are in agreement with recent published work on the composition of the feline oral microbiome [14–18] and show a tangible effect of diet on the overall diversity and relative frequency of occurrence of known periodontal pathogens. ResultsWe used in-depth genetic sequencing to analyse the composition of the oral microbiome in 10 owned cats. The phylogenetically informative 16S gene (hypervariable region V1-V3) was amplified from supragingival dental plaque samples from cats in the study cohort. Illumina sequencing of the 16S PCR products produced a total of 2,421,096 sequences with an average of 186,238 sequences per sample, post-quality filtering. These sequences had an average length of 487 base pairs. Feline oral microbiome compositionThe cat oral microbiome contained a diverse array of bacteria from 14 phyla (Fig. 1). For each cat and diet group, the relative frequency of operational taxonomic units (OTUs) is presented in Additional file 1. Irrespective of diet, three phyla accounted for 76 % of sequences. These included Bacteriodetes (31 %), Firmicutes (24 %) and Proteobacteria (21 %). Amongst the phyla, we found a total of 411 bacterial OTUs across all the cats (Fig. 2). The majority (67 %) of these OTUs belonged to genera that occurred at abundances below 1 %. The remaining 33 % of OTUs had abundances above 1 % and accounted for the majority (76 %) of sequences. The most dominant OTUs across all the cats belonged to the Porphyromonas genus (14. [SEP] Justify:

Output: The text contrasts the claim by detailing the diversity of the feline oral microbiome, showing that there are multiple bacterial OTUs present among cats, and emphasizing the individuality of microbiome compositions influenced by dietary variations. The mention of 411 bacterial OTUs across all cats, along with different abundances among them, supports the argument that microbiome composition is not identical across individuals.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: Its downstream signaling inhibits attractive MBN-MBON synapses in the LP, breaking the balance between distinct compartments. As a result, after learning, the larvae will exhibit repulsion when exposed to the odor again. In contrast, dopaminergic neurons in the pPAM convey appetitive cues, which project to compartments in the medial lobe. The co-existence of olfactory and appetitive gustatory stimuli leads to inhibition of these repulsive MBN-MBON synapses, inducing attraction. The conserved role of DAN-c1 in aversive learning throughout Drosophila developmentshare similar neuronal circuits of learning with larvae54,66–69. In adult brains, dopaminergic neurons are classified into 13 clusters, named as PAM (protocerebral anterior medial), PAL (protocerebral anterior lateral), PPM (protocerebral posterior medial), PPL (protocerebral lateral), and PPD (protocerebral posterior dorsal) clusters32. DANs in the PAM cluster innervate the medial lobe, while those in PPL1 project to the vertical lobe69. R76F02-AD;R55C10-DBD identifies two dopaminergic neurons in adult brains, MB-MP1 in PPL1 and ALT-PLPC in PPL2ab 61. MB-MP1 is also named PPL1-γ1pedc, innervating both γ1 and the peduncle of the β lobe69. Activation of this neuron induced aversive learning62, and activation of its corresponding MBON-γ1pedc>α/β led to approach80. During metamorphosis, dopaminergic neurons in DL1 develop into the PPL1 cluster, DL2a neurons develop into PPL2ab, and those in pPAM will develop into the PAM cluster81. This driver strain only identifies DAN-c1 from DL1 in larvae, which innervates the lower peduncle of the mushroom body. Interestingly, previous reports revealed that memory can be transferred from larvae to adults82, indicating the maintenance of neuronal circuitry architecture during metamorphosis. This evidence supports that DAN-c1 is the corresponding neuron of PPL1-γ1pedc in larvae, which performs similar functions in aversive learning. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on the role of dopaminergic neurons and aversive learning in Drosophila, with no mention of olfactory receptors or any suggestion of quantum effects. It discusses synaptic inhibition and the development of neural circuits rather than the mechanisms by which olfactory receptors might operate.


Input: Task: corroborate the claim using the given text. Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: : August 7, 2019 Harnessing the Our Feet: Authors and reviewers. Susan G. Hamm is the Director for at the U. S. Department of Energy. She leads efforts to lower costs and risks so more geothermal energy can be used for electricity and heat. Dr. Hamm has also worked at the Foundation and the Department of Homeland Security, and she has been a legislative assistant in the U. S. Congress. She holds a B. A. in Geology from as well as a M. S. in Geophysics and a Ph. D. in from the University of Minnesota. *susan. hamm@ee. doe. gov is a Scientist in the Office at the U. S. Department of Energy, Office of. In this role, Elisabet is involved in project oversight and execution of cutting-edge EGS RD&D, leads the annual student competition, and develops the communications strategy for geothermal initiatives. Prior to joining DOE, Elisabet worked within FEMA’s in Washington, DC. Elisabet holds an M. S. in from the University of Maine-Orono and a B. S. in Geology and Geophysics from University of Wisconsin-Madison. Age: 9 I am in the fourth grade, I love to play soccer and basketball. I also like music a lot. grow up, I want to be a computer scientist! I like learning about science and I really like math. Age: 8 My name is Vardhan and I am 8 years old. I enjoy cricket and reading books on outer space. My favorite subjects are Math and English. you know that, deep down, the Earth is really hot and that this heat can generate energy? This energy, called geothermal energy, can create electricity all over the world! It all starts with water. Water is found inside rocks nearly everywhere, even very deep beneath the Earth’s surface where it is very hot. The water inside hot rocks is very hot as well, and when this fluid comes to the Earth’s surface, it can generate electricity that can turn on a lightbulb, charge your phone, and even power your car! Geothermal energy is green energy! It does not release carbon dioxide into the atmosphere, is nearly inexhaustible, and available 24/7, because the interior of the Earth is always hot. [SEP] Justify:

Output: The text corroborates the claim by explaining that geothermal energy is derived from the heat located deep beneath the Earth's surface. It details how this heat can be harnessed by using water found in hot rocks to generate electricity, supporting the assertion that geothermal energy is a method of power generation based on underground heat.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: 9 %) followed by the Treponema (5. 1 %) and Fusibacter (4. 5 %) genera. Fig. 1Relative abundance of phyla in the oral microbiome of cats on dry- and wet-food diets. The abundance of phyla was calculated from sequences classified as bacteria. These sequences were taxonomically assigned using the Greengenes database following operational taxonomic unit (OTU) classification with uclust (QIIME version 1. 8. 0). Relative abundances were calculated using normalised sequence data to account for varying sequence depth between samples (Phyloseq version 1. 10. 0)Full size image Fig. 2Phylogeny of sequences in the oral microbiome of cats on dry and wet diets. The phylogeny was generated using maximum likelihood (RAxML version 730) from representative sequences of the operational taxonomic units (OTUs) and graphically displayed in the of Life (version 2. 1). Sequences with less than 0. 2 patristic distance between OTUs were collapsed (Phyloseq version 1. 10. 0). The abundance of genera in the dry- and wet-food diet groups displayed around the phylogeny was calculated from normalised OTU counts (Phyloseq version 1. 10. 0). Abbreviation: NA non-assignedFull size image Impact of diet on the feline oral microbiomeThe overall oral bacterial diversity was estimated for each cat (see Additional file 2). We used the alpha (α) diversity metric, Estimator (ACE), to assess species richness and the to assess species evenness. We assessed whether the overall bacterial diversity differed between cats eating highly refined dry (higher carbohydrate content [both on a dry matter, total energy and absolute percentage basis], dehydrated, lower percentage of protein and fat on a percentage basis as fed) versus wet (lower carbohydrate content, high in water [approx. 70 %], variably higher in protein and fat) diets (see Additional file 3). Many of the dry extruded kibbles are sprayed with phosphoric acid to improve palatability, and such diets were the ones that tended to be fed to cats in the dry-food cohort. An analysis of covariance () was conducted to determine if there was a statistically significant effect of diet on the cat’s oral microbiome ACE and values, when controlling for age of the cats. [SEP] Justify:

Output: The text contrasts the claim by discussing the variability in the oral microbiome of cats based on their diet, emphasizing differences in microbial composition. It details how factors such as diet impact the bacterial diversity within the oral microbiome, asserting that microbiome composition is influenced by external factors, thereby refuting the idea that everyone's microbiome is identical.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: Pre- and post-synaptic D2Rs regulate cAMP in the mushroom body during aversive learningThe molecular mechanisms underlying Drosophila learning have not been fully determined. In a traditional view, gustatory cues elevate dopamine release, which binds to D1 receptors and then activates Gαs. The co-existence of Gαs and calcium elicited by olfactory cues activates rutabaga in axons of MBNs. Rutabaga transforms ATP into cAMP, activating PKA signaling pathway. Mutant flies with either insufficient (rutabaga) or excessive cAMP (dunce) showed aversive learning deficiency, indicating that the level of cAMP should be kept in an optimal range to achieve aversive learning21,25 (Figure 7B). Our results have shown that D2Rs in dopaminergic neurons and the mushroom body are important for larval aversive learning, suggesting a “dual brake” role in regulating cAMP levels in the MBNs through both pre- and postsynaptic components. On the presynaptic side, D2R in DAN-c1 decreases the release of dopamine under gustatory stimuli, reducing the probability of postsynaptic D1R activation in MBNs. On the postsynaptic side, D2R in MBNs inhibits the coincidence detector rutabaga (AC) both via activation of Gαi/o and inhibition of voltage-gated calcium channels2, indicating postsynaptic D2R functioning as a “brake of the coincidence detector”. Combining these, “dual brake” D2R ultimately regulates the mushroom body cAMP level within a physiologically optimal range during aversive learning. In addition, D2R fine tunes the functional concentration spectrum of dopamine with higher resolutions, as its dopamine affinity is 10- to 100-fold greater than D1 receptors. Overall, D2Rs work in a “dual brake” system both expanding the representation of a dynamic range of gustatory signal intensity with high signal to noise ratio and preventing postsynaptic overexcitation, which increases the reliability of DAN-c1 and MBN circuits for the larval aversive learning (Figure 7C & D). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the molecular mechanisms involved in Drosophila aversive learning, particularly focusing on the roles of D2 receptors and cAMP regulation. There is no mention or implication of olfactory receptors or quantum effects, which makes the text unrelated to the claim about olfactory receptors functioning through quantum effects.


Input: Task: corroborate the claim using the given text. Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: In the future, scientists hope to expand geothermal energy use with technology improvements, bringing this powerful, fascinating energy into even more cities and towns. Geothermal Energy? Geothermal energy is renewable energy that is harnessed from the heat inside the Earth. We can use this energy not only for electricity, but also for heating and cooling buildings and for spas and hot springs. But where does this heat come from? The heat at the center of the Earth is a byproduct of chemical and nuclear reactions happening deep in the Earth’s core—reactions that have been occurring for billions of years. A common byproduct of these reactions is heat, which then slowly migrates up through the Earth until we can reach it by drilling into the ground. Because these reactions deep inside the earth will continue to happen, any heat we use will be replaced, or renewed. Geothermal energy, along with solar energy, wind energy, and hydropower, is considered a renewable energy source because we can use it forever. ? Although heat from the center of the Earth is migrating to the surface everywhere, the heat is concentrated at the edges of tectonic plates. Tectonic plates are pieces of the Earth’s surface that fit together like a jigsaw puzzle and move around slowly, about as fast as your fingernails grow. These plates can grind up against each other, or slip under each other, so their edges are very hot and are considered dynamic places. In fact, the edges of tectonic plates are marked by lots of earthquakes, like in California, and volcanoes, like in Japan. For example, the edge of the is called the Ring of Fire, because of the especially high number of earthquakes and volcanoes that occur there (see Figure 1). The western is a part of the Ring of Fire, so more heat is brought to the Earth’s surface in states like Nevada and California. That is where you will find all of the current geothermal power plants in the U. S. Figure 1 - A world map showing tectonic plates and plate boundaries. of Fire runs along the perimeter of the (Service). Convergent boundaries, shown in red, are where plates are moving toward each other. Divergent boundaries, shown in white, are where plates are moving away from each other. Transform boundaries, shown in gold, are where plates are sliding past each other. [SEP] Justify:

Output: The text corroborates the claim by explaining that geothermal energy is derived from the heat inside the Earth, stating directly that geothermal energy is harnessed from heat, which is a result of ongoing chemical and nuclear reactions within the Earth’s core. It notes that this heat can be accessed through drilling, which supports the idea that geothermal energy harnesses heat from beneath the Earth’s surface.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: We controlled for age because OTU diversity has previously been found to rise with increasing age [19]. We observed a significant effect of diet when controlling for age on species richness (ACE), F(2, 7) = 39. 26, p = 0. 0002 (Fig. 3), and evenness (Simpsons Index), F(2, 7) = 85. 67, p = 0. 00001. Cats on a dry-food compared to a wet-food diet had a higher diversity of OTUs in their oral microbiomes (ACE p = 0. 0001, Simpsons p = 0. 0001). Additionally, the overall bacterial diversity increased with increasing age (ACE p = 0. 0002, Simpsons p = 0. 0001). Fig. 3Analysis of covariance on the effect of diet type and age on the feline oral microbiome’s α diversity. Analysis of covariance () was used to test the effect of dry and wet diets on α diversity, while controlling for age of the cats in R (version 3. 1. 2). Alpha diversity was calculated using the Abundance-based (ACE) metric, from operational taxonomic unit (OTU) abundance data (Phyloseq version 1. 10. 0). was performed on the log of ACEFull size image To determine what was causing the higher bacterial diversity in cats consuming dry food, we assessed the effect of diet on the abundance of specific bacteria. A differential abundance test, DESeq2 [20, 21], was used to compare the OTUs present in dental plaque samples from cats on dry-food to those on a wet-food diet. Of the 411 OTUs identified in the cat’s oral microbiomes, 23 were differentially abundant between the two diets (Fig. 4). Of these 23, 65 % of OTUs were significantly more abundant in plaque from cat’s consuming a dry diet. The most enriched bacteria in cats eating dry diets exclusively included OTUs from the following genera: Actinobacillus (p = 1. 74 × 10−8), Acholeplasma (p = 0. 0002), Treponema (p = 0. 0087) and Porphyromonas (p = 0. 0028). The remaining 35 % of OTUs were significantly more abundant in dental plaque from cats consuming a wet diet. [SEP] Justify:

Output: The text contrasts the claim by providing evidence that there is significant variation in microbiome composition, specifically related to diet and age. The study mentions that 'dry-food compared to a wet-food diet had a higher diversity of OTUs in their oral microbiomes,' which implies that microbiome composition is influenced by environmental factors and is not identical across individuals.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: Recent studies showed that the approach/repulsion in learning is achieved via inhibition of the repulsive/attractive representing compartments67,74,75, which indicates dopamine inhibits acetylcholine release from MBN to MBON83. However, the PKA signaling pathway usually elevates neuronal excitability and increases neurotransmitter release2,3, which is contradictory to the recent findings. In addition to the “dual brake” role of D2R, our results suggest a third role of D2R in aversive learning. D1 and D2 receptors can form heteromeric receptors, whose downstream Gαq activates PKC and CaMKII signaling pathways. The activation of these signaling pathways may reduce acetylcholine release84 (Figure 7C & D). Explaining the results of thermogenetic and optogenetic experimentsActivation of DAN-c1 with dTRPA1 induced aversive learning, while the repulsion disappeared when DAN-c1 was activated in the quinine group (Figure 2K). Our explanation is that quinine stimulation and temperature activation led to over-excitation of DAN-c1, which impaired aversive learning. This is consistent with the learning deficiency in larvae with D2R knockdown in DAN-c1 (Figure 4E). Results from optogenetic activation of DAN-c1 during aversive learning also support this (Figure 5B). However, in contrast to results with thermo-activation, larvae with optogenetic activation of DAN-c1 showed neither repulsion after being trained with distilled water (Figure 5C), nor did they show reduced attraction in the sucrose group (Figure 5D). One possible explanation is that the thermo-activation is relatively mild compared to the optogenetic activation. Based on this, thermo-activation of DAN-c1 is still in the physiological range of cAMP under the upper cutoff (Figure 7B), resulting in repulsion in the distilled water (DW) group, neutralized attraction in SUC group, and impaired repulsion in QUI group. In contrast, optogenetic activation of DAN-c1 overwhelmed the physiological conditions, leading to failure of repulsion in DW group (Figure 5C). This repulsive failure did not affect appetitive learning (Figure 5D), and a stronger over-excitation in QUI group induced similar failure (Figure 5B). [SEP] Justify:

Output: The text does not address the claim regarding olfactory receptors and quantum effects. Instead, it discusses various signaling pathways and their roles in aversive learning, with no mention of olfactory receptors or quantum mechanics. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: The yellow dots represent earthquake activity, which you can see is heaviest at the edges of the plates. Map of by the Service. Check out the tectonic plate map above and look at the countries through which plate boundaries cross. Do you notice a pattern? Countries with a lot of geothermal energy, like Iceland and Indonesia, also sit right on top of a plate boundary. Think of all the geothermal energy potential across the entire globe! A lot of gigawatts! A gigawatt, usually abbreviated as GW, can power about 1,000,000 homes. There is about 12. 8 GW of geothermal-based electricity produced worldwide and about 3. 5 GW in the U. S. alone. 3. 5 GW can power about 3,500,000 homes, which is equivalent to almost all the households in the entire state of Georgia! That might seem like a lot, but it is about to get bigger: there is about 1. 3 GW of additional electricity from geothermal energy under development right now! Up a Geothermal Reservoir? To be able to harness the geothermal energy deep in the Earth, we need three ingredients: (1) heat, (2) fluid, and (3) permeability, or pathways through the rock. As we have already discussed, heat from the center of Earth migrates to the Earth’s surface naturally, and is found everywhere underground, although the amount of heat varies slightly from place to place. We also need fluid inside underground rocks to absorb heat and bring it to the surface for us. This fluid is not your average water; it is not like water that comes out of your tap, or ocean water, or agricultural water, or anything that you have ever seen before. This fluid has weird colors, strange smells, and flows differently. Lastly, we need to have pathways for that water to flow underground. These pathways are usually made up of fractures, or cracks in the rocks, which are very tiny and connected to each other. These fractures allow the fluid to circulate slowly and increase in temperature. When these three things occur together, they create a geothermal reservoir. We can pump the hot water out of the reservoir, send it through a power plant, use it to spin a turbine and generate electricity. Then, that electricity can be sent out to power homes via transmission lines. [SEP] Justify:

Output: The text corroborates the claim by explaining how geothermal energy is generated from heat found beneath the Earth's surface. It details the requirements for harnessing geothermal energy, stating that we need heat, fluid, and permeability to create a geothermal reservoir, which can then be used to generate electricity. The explanation of pumping hot water out of the reservoir and sending it through a power plant to spin a turbine for electricity generation supports the claim directly.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: These bacteria were primarily Proteobacteria, from the Neisseriaceae family (38 %), including Conchiformibius kuhniae (p = 0. 0002). The differential abundance test indicated that diet alters the abundance of specific bacteria in the feline oral microbiome. Fig. 4Differentially abundant genera between the oral microbiomes of cats on dry and wet diets. DESeq was used to test for the presence of differentially abundant operational taxonomic units (OTUs) in the oral microbiome of the cats on a dry- compared to wet-food diet (Phyloseq version 1. 10. 0). The DESeq test was applied to OTUs that had been filtered to remove singletons and species with an abundance below 0. 005 %. The figure presents those OTUs that were found to be significantly different (p < 0. 01) between the two diets. Positive log2-fold change values indicate enriched OTUs in the dry diet, and negative log2-fold change values indicate enriched OTUs in the cats eating a wet diet. Abbreviation: NA non-assignedFull size image DiscussionOur analysis of the oral microbiome is only the fourth NGS study in cats [14–16] and for the first time highlights the potential impact of diet on bacterial composition. Cats consuming dry kibble (dehydrated, highly refined, cereal-based, sprayed with oil and phosphoric acid) compared to a higher protein/lower carbohydrate wet diet (approx. 70 % water by weight) have a more diverse oral microbiome, with an enrichment of bacteria associated with both gingival health and periodontal disease. Our findings indicate that diets commonly fed to domestic cats in Australia [22] influence the feline oral microbiome make-up, although exactly how these composition changes relate to gingival health requires resolution. Current understanding of the feline oral microbiome and what impacts its composition is limited by a lack of culture-independent studies. At a broad scale, we found a similar bacterial profile to published NGS [14–16] and Sanger sequencing [17, 18] studies of the feline oral microbiome, in terms of abundance of phyla and detection of genera. [SEP] Justify:

Output: The text contrasts the claim by indicating that the composition of the feline oral microbiome varies based on diet, highlighting differences in bacterial profiles between cats on different diets. The analysis demonstrates that diet alters the abundance of specific bacteria, implying that microbiome compositions are not identical across individuals.