Input: Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: This is primarily because a geothermal energy source is contained subsurface and need not to be exposed in the atmosphere and the surface energy conversion system (e. g. ORC unit) is relatively compact, thus making the overall size of the entire system attractively small. EGS power plants also provide environmental benefits by having minimal GHGand other emissions (zero emissions for the case of using ORC technology). Distinct from the conventional fossil fuels, there are minimal discharges of CO2, nitrogen or sulphur oxides or particulate matter resulting from its use, and there is no need to dispose radioactive materials. However, still there are impacts that must be considered and managed if enhanced geothermal energy resource is to be developed as part of a more environmentally sound, sustainable energy source for the future. The major environmental challenges for EGS are associated with ground water use and contamination, with related concerns about induced seismicity or subsidence as a result of water injection and production. Induced seismicity is a phenomenon in which a change in fluid pressure within a stressed rock formation leads to movement of the fractured rocks; the energy released is transmitted through the subsurface rock and may reach the surface with enough intensity to be heard or felt by people in the surrounding region (DiPippo, 2008; Majer et al. , 2007). To mitigate risks related to induced seismicity, strategies and procedures are needed to set requirements for seismic monitoring and for prolonged EGS field operation. Technologies for imaging fluid pathways induced/injected by hydraulic stimulation in EGS fields would constitute a key improvement of the EGS concept. Issues of noise, safety and land use associated with drilling and production operations are also important but can be fully manageable (Huenges, 2010; Majorowicz& Grasby, 2010; Azim et al. , 2010). EGS technology has impacts on the global and local environment (Huenges, 2010; DiPippo, 2008). Therefore, it is important to identify and evaluate any impact which results from the implementation of an EGS plant at the beginning of a project. The goal must be to avoid or minimize adverse impacts on the environment during all stages of an EGS project (e. g. , construction, operation, and deconstruction) and to meet the objectives and requirements of environment protection and preservation of finite resources (Huenges, 2010; Stephens & Jiusto, 2010).

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: 3The relative abundances of the 10 most common bacterial taxa in hyphosphere soils (A and B) and AMF mycelia (C and D) are depicted in terms of Phylum (A and C) and Genus (B and D), with all remaining taxa grouped under “Others”Full size imageBacterial diversity and composition of AMF mycelia and soil communitiesMiSeq sequencing, a total of 10,207,763 bacterial 16 S rRNA gene reads were generated. Through the bioinformatic pipeline, 3,813,362 non-chimeric reads were retrieved from 72 samples after filtering, trimming, denoising, and merging. The reads ranged from 31,424 to 84,914 per sample. These reads were assigned to 8819 bacterial ASVs and further classified into 49 phyla, 119 classes, 297 orders, 386 families, and 965 genera. The rarefaction curves demonstrated that all samples had reached saturation (Figure S4). The bacterial community of AMF mycelia was predominantly comprised of the phylum Planctomycetota, representing 42% of the total ASVs, followed by Pseudomonadota (13%), Chloroflexota (11%), Actinomycetota (9%), and Verrucomicrobiota (7%). Similarly, Planctomycetota constituted the majority of the bacterial soil community, accounting for 41% of the total ASVs, followed by Chloroflexota (13%), Pseudomonadota (12%), Bacillota (7%), and Acidobacteriota (6%) (Fig. 3). Among the mycelia, the predominant genera were Pirellula (7%), Gemmata (4%), Pir4 lineage (4%), and Roseimicrobium (2%). Conversely, common genera in the soil were Pirellula (6%), Gemmata (4%), and Bacillus (2%). A substantial proportion of bacteria (47% in the mycelia and 58% in the soil) belonged to unknown genera. Furthermore, 2191 ASVs were exclusively found in the mycelia, 4145 were unique to the soil, and 2051 were shared by both biotopes (Figure S5).

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: Zhang F. Bamberg E. Nagel G. Deisseroth K2005Millisecond-timescale, genetically targeted optical control of neural activity8:1263–1268 P. Felsenberg J. Waddell S2018Do the right thing: neural network mechanisms of memory formation, expression and update in DrosophilaNeurobiol 49:51–58 D. Waddell S2015Olfactory learning skews mushroom body output pathways to steer behavioral choice in DrosophilaNeurobiol 35:178–184 R. L2005Olfactory memory formation in Drosophila: from molecular to systems neuroscienceNeurosci 28:275–302 Y. et al. 2014The neuronal architecture of the mushroom body provides a logic for associative learningElife 3 G. S. Marin E. C. Watts R. J. Luo L2002Development of neuronal connectivity in Drosophila antennal lobes and mushroom bodiesNeurobiol 12:80–86 M. Borst A. Wagner S. Byers D1985Drosophila mushroom body mutants are deficient in olfactory learningJ Neurogenet 2:1–30 Belle J. S. Heisenberg M1994Associative odor learning in Drosophila abolished by chemical ablation of mushroom bodiesScience 263:692–695 R. L2011Traces of Drosophila memoryNeuron 70:8–19 J. et al. 2011Mushroom body efferent neurons responsible for aversive olfactory memory retrieval in Drosophila14:903–910 D. et al. 2015Activity of defined mushroom body output neurons underlies learned olfactory behavior in DrosophilaNeuron 86:417–427 T. Fischer M. Schulz R. Heisenberg M2000Localization of a short-term memory in DrosophilaScience 288:672–675 S. M. Davis R. L2009Dynamics of learning-related cAMP signaling and stimulus integration in the Drosophila olfactory pathwayNeuron 64:510–521 L. R. et al. 1992learning and memory gene rutabaga encodes a Ca2+/Calmodulin-responsive adenylyl cyclaseCell 68:479–489 T. Louis T. Jindachomthong K.

Output: pass


Input: Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: Typical EGS drilling costs as a function of well depth (Azim et al. , 2010). 3. 3. EGS using CO2 as the working fluid for green power generation and simultaneous carbon sequestration It was reported (Pruess, 2006) that previous attempts to develop EGS in Japan, USA, Europe and Australia have all employedwater as a heat transmission fluid. Although, water has many properties that make it a favorable medium for this purpose, it also has serious shortcomings. An unfavorable property of water is that it is astrong solvent for many rock minerals, especially at elevated temperatures. In this case, injecting water at high pressure intohot rock fractures, as part of an EGS resource operation & utilization, results in strong dissolution and precipitation effects that change fracture permeabilityand make it very difficult to operate an EGS reservoir in a stable manner. In 2000, Brown, D. (Pruess, 2006) proposed a novel EGS concept that would utilize supercritical CO2 instead of water as heat exchange (carrier) fluid, and would simultaneously achieve CO2 geologic sequestration as an additional benefit. There are only very few investigations that characterized the performance of CO2 as working fluid in EGS applications. For example, Pruess (Pruess, 2006) performed numerical simulations and evaluated thermophysical properties in order to explore the heat transfer and fluid dynamics characteristics in an EGS reservoir that would be operated with CO2. It was found that CO2 is superior to water in its ability to exchange heat from hot fractured rock. Carbon dioxide also offers certain advantages with respect to wellbore hydraulics, in that its larger compressibility and expansivity as compared to water would increase buoyancy forces and would decrease the parasitic power consumption (thus reduce pumping cost) of the EGS fluid circulation system. This is because the larger expansivity of CO2 would generate large density differences between the cold CO2 in the injection well and the hot CO2 in the production well, and therefore provide buoyancy force that would reduce the power consumption of the fluid circulation system. Another interesting feature of CO2 is that its lower viscosity, tend to yield larger flow velocities for a given pressure gradient. In addition, CO2 would be much less effective as a solvent for rock minerals, which would reduce or eliminate scaling problems, such as silica dissolution and precipitation in water-based systems (Pruess, 2006).

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Interestingly, at the genus level, the majority of genera were shared between the mycelia and the soil, with 281 genera common to both, 109 found only in the soil, and 44 exclusive to the mycelia (Figure S5). Fig. 4Alpha diversity, assessed using the Shannon (A) and Simpson (B) indices across various biotopes of AMF mycelia and hyphosphere soils and treatments: microbial amendment (B1), no microbial amendment (B0), R. irregularis (M1), R. clarus (M2), and R. cerebriforme (M3)Full size imageInfluence of AMF identity and microbial inoculation on bacterial communities in mycelia and soilAccording to the Kruskal–Wallis test, there was a significantly higher alpha diversity in the soil compared to the mycelia (p < 0. 001). No significant difference in alpha diversity was observed between the B0 and B1 treatments (Table 1; Fig. 4). In both the B0 and B1 treatments, the species of inoculated AMF did not have an impact on the alpha diversity in either the mycelia or the soil. Overall, the alpha diversity remained consistent throughout the experiment when calculated using either the Shannon or Simpson indices. Table 1 Factors significantly influencing the alpha diversity assessed by Shannon and Simpson indices across different biotopesFull size tableWe observed significant differences in the bacterial community between the mycelia and the soil (p < 0. 001). The factors influencing the beta diversity varied between these two environments. In the soil, there were no significant changes in the bacterial community structure between the B0 and B1 treatments or between the AMF species (Table 2; Fig. 5). However, in the mycelia, both the B0/B1 treatments and the inoculated AMF species had a significant effect on the bacterial community structure (p = 0. 025 and p = 0. 005, respectively); however, the interaction between microbial treatment and AMF species inoculation was not significant (Table 2; Fig. 5). The “betadisperse” function indicated higher dispersion among the communities in the mycelia (0. 42) compared to those in the soil (0. 34). Fig.

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: AbstractThe intricate relationship between the dopaminergic system and olfactory associative learning in Drosophila has been an intense scientific inquiry. Leveraging the formidable genetic tools, we conducted a screening of 57 dopaminergic drivers, leading to the discovery of DAN-c1 driver, uniquely targeting the single dopaminergic neuron (DAN) in each brain hemisphere. While the involvement of excitatory D1-like receptors is well-established, the role of D2-like receptors (D2Rs) remains underexplored. Our investigation reveals the expression of D2Rs in both DANs and the mushroom body (MB) of third instar larval brains. Silencing D2Rs in DAN-c1 via microRNA disrupts aversive learning, further supported by optogenetic activation of DAN-c1 during training, affirming the inhibitory role of D2R autoreceptor. Intriguingly, D2R knockdown in the MB impairs both appetitive and aversive learning. These findings elucidate the distinct contributions of D2Rs in diverse brain structures, providing novel insights into the molecular mechanisms governing associative learning in Drosophila larvae. eLife assessment This study presents a valuable finding on the role of dopamine receptor D2R in dopaminergic neurons DAN-c1 and mushroom body neurons (Y201-GAL4 pattern) on aversive and appetitive conditioning. The evidence supporting the claims of the authors is solid and promotes the investigation using fly larvae, which have interesting advantages in the time required for obtaining experimental animals and the use of optogenetics. The work will be of interest to researchers studying neuronal control of behaviour and learning and memory in general. of findingsvaluable: Findings that have theoretical or practical implications for a subfieldlandmarkfundamentalimportantvaluableusefulStrength of evidencesolid: Methods, data and analyses broadly support the claims with only minor weaknessesexceptionalcompellingconvincingsolidincompleteinadequateDuring the peer-review process the editor and reviewers write an eLife assessment that summarises the significance of the findings reported in the 1. On the cellular and physiological level, learning and memory is achieved by neuroplastic changes in circuits, including neuronal excitability and synaptic plasticity. Usually distinct types of neurotransmitters, such as dopamine, modulate these changes.

Output: pass


Input: Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: It was also reported (Pruess, 2006) that while the thermal and hydraulic aspects of aCO2-based EGS system look promising, major uncertainties remain with regard to geochemical interactions betweenfluids and rocks. It was concluded in (Pruess, 2006) that an EGS system running on CO2 has sufficiently attractive features to warrant furtherinvestigation. It was suggested that an EGS using CO2 as heat transport and exchange fluid could have favorable geochemical properties, as CO2 uptake and sequestration by rock minerals would be quite rapid. Supercritical CO2 can also be used as the working fluid of the power cycle before it is sent back to the EGS reservoir. For example, ina study by (Gurgenic et al. , 2008), it was reported that there is a significant potential to use supercritical CO2 as working fluid in the power loop as illustrated (Gurgenic et al. , 2008) in Figure 5. Significantly higher energy conversion efficiencies were predicted using a single-loop system with the CO2 being both the heat exchange and the power cycle working fluid. It was reported (Gurgenic et al. , 2008; Atrens et al. , 2011) that the loops in either of the two cycles (i. e. subsurface loop and surface power loop) do not have to be closed. For example, if there is ready access to CO2 (e. g. , at a geothermal installation situated close to a coal-fired power plant), the captured CO2 from the plant can be run through the geothermal reservoir first and then sequestered in a geologic sequestration site of choice. CO2-based EGS has been examined in (Atrens et al. , 2011) from a reservoir oriented perspective, and as a result thermodynamic performance was investigated. It was reported (Atrens et al. , 2011) that economics of the system are still not well understood, however. In their study, the economics of the CO2–based EGS technology was explored for an optimized power plant design and best-available cost estimation data. It was demonstrated in (Atrens et al. , 2011) that near-optimum turbine exhaust pressure can be estimated from surface temperature. It was found that achievable cooling temperature is an important economic site consideration alongside EGS resource temperature.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: 5PCoA plots displaying the bacterial communities categorized by their respective biotopes (hyphosphere soils and AMF mycelia), based on the inoculation of AMF species and microbial suspension amendmentFull size imageTable 2 Factors significantly influencing the beta diversity of bacterial communities including the inoculated mycorrhizal species, represented by “AMF”, and the B0/B1 treatment of bacterial community inoculation, denoted by “MI”. “*” denotes statistical significance, “**” represents high significance, and “•” indicates a tendencyFull size tableIndicator species profilesThe analysis of indicator species revealed 126 ASVs across all treatments and sample types (Tables S2–S5). Of these, 77 ASVs (81%) were associated with AMF species treatments (Tables S2 and S4), with 53 ASVs enriched in the mycelia and 24 in the soil. Furthermore, 13 ASVs were linked to the mycelia of R. irregularis, 19 to R. clarus, and 21 to R. cerebriforme. Conversely, six ASVs were enriched in the soil of R. irregularis and nine each in the soil of R. clarus and R. cerebriforme. In contrast, 18 ASVs (19%) were identified as indicator species under microbial suspension amendment, with 10 ASVs enriched in the mycelia and 8 in the soil. Notably, Herpetosiphon sp. , Pirellula sp. , and one ASV belonging to the CPla-3 termite group were the most abundant ASVs in the mycelia under microbial amendment (Table S2). However, two ASVs belonging to the phylum Verrucomicrobiota (Ellina 516 and Candidatus Xiphinematobacter) and one ASV belonging to Acidobacteriota were identified in the soil (Table S4). Additionally, Sulfurifustis sp. , Pirellula sp. , Candidatus Omnitrophus, and Noviherbaspirillum sp. were the most dominant ASVs associated with R. irregularis mycelia (Table S2). The most abundant ASVs associated with R. clarus were Herpetosiphon sp. , Fimbriiglobus sp.

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: Dopamine (DA) plays an important role in many mammalian brain functions, including motor functions, motivation, reinforcement, addiction, and learning and memory2–4. Dopaminergic neurons (DAN) are mainly located in the mesencephalon: DANs in the substantia nigra (SN) are responsible for motor functions, while those in the ventral tegmental area (VTA) are important in reward, addiction, learning and memory3,5. Dopamine achieves its functions via two families of G protein-coupled receptors (GPCR): excitatory D1-like and inhibitory D2-like receptors4. All D1-like receptors are located post-synaptically; in contrast, D2-like receptors both function post-synaptically and play an important presynaptic role, regulating dopamine release through negative feedback4. All these receptors are important in mammalian associative learning6,7. D1-like receptors elevate intracellular cAMP by activating adenylyl cyclase (AC) via Gαs, while D2-like receptors repress cAMP by inhibiting AC via Gαi/o. cAMP activates protein kinase A (PKA), leading to the phosphorylation of DARPP-32 (dopamine and cyclic AMP-regulated phosphoprotein, 32kDa), ion channels, and CREB (cAMP response element-binding protein). In addition, dopamine receptors also activate the PLC-and CaMKII pathways2–4,8–10. Although mammalian studies reveal mechanisms more relevant to human beings, the complexity of the nervous system impedes our understanding about the basic or more universal principles of learning and memory applicable generally to all nervous systems11. With a simple central nervous system (CNS) and powerful genetic tools, the fruit fly Drosophila melanogaster has become a popular model organism in learning and memory research12,13. With conserved genes in dopamine metabolism and signaling14, as well as fundamental similarities in the olfactory circuitry compared to mammals15,16, Drosophila can perform olfactory associative learning in both larvae17–19 and adults20–24. Olfactory associative learning is a type of classical conditioning in which flies are trained under positive or negative reinforcement paired with an odorant.

Output: pass


Input: Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: The role of sequestration as part of CO2–based EGS was also examined in (Atrens et al. , 2011), and it was concluded that if fluid losses occur, the economic viability of the concept depends strongly on the price associated with CO2 (Atrens et al. , 2011). Potential barriers to implementation of CO2–based EGS technology include access to CO2 at an acceptable cost, proximity of the EGS to the electricity grid, and access to cooling water. Similar issues related to long-term responsibility for the resultant reservoir, including the liability for future CO2 leakage from the geologic sequestration site. In another study by (Randolph & Saar, 2011), it was suggested that using CO2 as the working fluid in geothermal power systems may permit utilization of lower temperature geologic formations than those that are currently deemed economically viable,l eading to more widespread utilization of geothermal energy. However, additional exploration of economics regarding the opportunities and issues for CO2–based EGS technology for combined carbon sequestration and power generation is needed. Figure 5. A conceptual model showing a single-loop system with CO2 used for combined heat exchange and power cycle (Gurgenic et al. , 2008). Advertisement 4. increasing concern of environmental issues of emissions & pollution, in particular global warmingand the constraints on consuming conventional energy sources has recently resulted in extensive research into innovative renewable and green technologies of generating electrical power. One of these innovative emerging technologies includes renewable low-temperature (low-enthalpy) geothermal energy source for clean electrical power generation. This promising technology offers potential applications in generation of electric power which can be produced using the vast renewable low-temperature geothermal energy resources available worldwide. In this 1. Atrens A. D. Gurgenic H. Rudolph V. (2011 2011 Economic optimization of a CO2-based EGS plant. Energy & Fuels, 25 3765 3775 ACS Publications. 2. Azim M. R. Amin M. S. Shoeb A. 2010 Prospects of enhanced geothermal system in baseload power generation. IEEE, 10 176 180 0000-9781 3. Cengel Y. A. Boles M. A. 2008 Thermodynamics: an engineering approach (6th ed. ), McGraw-Hill press, 978-0-07352-921-4 New York. 4.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: , two ASVs of Haliangium sp. , Rivibacter sp. , and unidentified Burkholderiales (Table S2). With the R. cerebriforme mycelia, the most predominant ASVs were an unidentified ASV belonging to Planctomycetota, another belonging to Xanthomonadales, KCM-B-112, which belongs to Acidithiobacillaceae, two Roseimicrobium sp. , and one belonging to the Burkholderia–Caballeronia–Paraburkholderia group (Table S2). In the R. irregularis soil, an unidentified ASV belonging to the Gemmataceae family was the most abundant (Table S5). In addition, one ASV of Rhodopirellula was identified in the soil of R. clarus (Table S5). However, two ASVs belonging to the Hydrogenispora order and Blastocatellaceae family and one to Nitrosomonadaceae Ellin6067 stood out in the soil of R. cerebriforme (Table S5). Common bacterial core in the mycelia of different AMF species and soilA total of 29 bacterial core taxa were shared among all biotopes and treatments. Interestingly, the majority of these bacterial core taxa (29) were found in the mycelia of both the B0 and B1 treatments, with more core taxa present in the B0 treatment compared to the B1 treatment (Fig. 6). Among the various mycorrhizal species, R. cerebriforme (M3) exhibited more core taxa in its mycelia than the other AMF species. However, in the soil, R. irregularis (M1) had more core taxa (Figure S6). Most of the core taxa were common to all three AMF species in both the mycelia and the soil, with 78 taxa in the mycelia and 111 in the soil. Interestingly, the bacterial core taxa of R. cerebriforme (M3) were greatly increased by B1 treatment, rising from 1 to 138. Fig.

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: Different from the naïve reaction to the odorant, flies approach the odorant after being trained with rewards (e. g. , sucrose; appetitive)21, but avoid the odorant when trained with punishments (e. g. , electric shock, bitter taste chemicals; aversive)20. Several genes related to the cAMP-PKA signaling pathway, including dunce (dnc)25 and rutabaga (rut)21, are expressed in the mushroom body (the center for Drosophila learning and memory)26,27. Mutations of these genes lead to learning deficiencies, indicating their roles in olfactory learning21,25. Drosophila larvae offer several advantages for studying olfactory learning compared to adults. Notably, compared to neural circuits underlying olfactory learning, their simpler neural circuitry28, characterized by fewer olfactory receptor neurons (ORNs)29,30, projection neurons (PNs) 31, mushroom body neurons (MBNs)32, and dopaminergic neurons (DANs)33,34, facilitates the elucidation of underlying mechanisms. Additionally, larvae exhibit simpler behavioral patterns, facilitating experimental manipulations and observations. Furthermore, their translucent cuticles enable convenient application of techniques such as optogenetics35, further enhancing the experimental versatility of larval studies. Like in mammalian brains, dopamine achieves its functions via four dopamine receptors in flies, two D1-like receptors dDA136 (or Dop1R1) and DAMB37 (or Dop1R2), one D2-like receptor D2R38 (or Dop2R), and one non-canonical receptor DopEcR14,39. dDA1 is mainly found in the mushroom body40,41 and is necessary for appetitive and aversive olfactory learning in larvae and adults41,42. D2R in the mushroom body is necessary for anesthesia-resistant memory43. In addition, D2R in GABAergic anterior paired lateral (APL) neurons is known to secure aversive conditioning in adult flies44. Although D2R expression has been reported in the ventral nerve cord45, neither its expression in larval brains, nor its functions in larval olfactory learning have been investigated.

Output: pass


Input: Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: Chandrasekharam D. Bundschuh J. 2008 Low-enthalpy geothermal resources for power generation CRC & Francis Group, 978-0-41540-168-5 New York. 10. 1201/9780203894552 5. Cui J. Zhao J. Dai C. Yang B. 2009 Exergetic performance investigation of medium-low enthalpy geothermal power generation. 636 639 0000-9780 6. Dickson M. H. Fanelli M. (2005 2005 Geothermal energy: utilization and technology, Earthscan, an imprint of James & James (Science Publishers) Ltd. in Association with the for Environment and Development, 1-84407-184-7 7. R. 2008 Geothermal power plants: principles, applications, case studies and environmental impact 2nded, Elsevier, 978-0-75068-620-4 New York. 8. R. 2007 Ideal thermal efficiency for geothermal binary plants. Geothermics, 36 276 285 0375-6505 9. Energyinformative. How enhanced geothermal systems (EGS) work. Available from: 10. Frick S. Kaltschmitt M. Schröder G. (2010 2010 Life cycle assessment of geothermal binary power plants using enhanced low-temperature reservoirs Energy 35 2281 2294 0360-5442 11. Galanis N. Cayer E. Roy P. Denis E. S. Desilets M. 2009 Electricity generation from low temperature sources. Journal of Mechanics, 2 2 55 67 1735-3645 12. 2012 Geothermal: International market overview report. Available from: 13. Grasby S. E. Allen D. M. Bell S. Chen Z. Ferguson G. Jessop A. Kelman M. Ko M. Majorowicz J. Moore M. Raymond J. Therrien R. 2011 Geothermal energy resource potential of Survey of Canada, 6914. Available from: 10. 4095/288745 14. Gupta H. Roy S. 2007 Geothermal energy: an alternative resource for the 21st century, Elsevier, 978-0-44452-875-9 New York. 15. Gurgenic H. Rudolph V. Saha T. Lu M. 2008 Challenges for geothermal energy utilisation.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: 6Venn diagram illustrating the core taxa shared between the two biotopes (AMF mycelia and hyphosphere soils) and the two treatments, B1 (microbial inoculation) and B0 (control)Full size imageDiscussionThe microcosm used in this experiment was designed to investigate the impact of AMF identity on the structure of soil bacterial communities in both enriched and non-enriched microbial environments. Additionally, this provided an opportunity to assess whether the mycelia from different AMF species, which were closely related and cultivated under identical in vitro conditions, would lead to the selection of distinct bacterial communities. This system also facilitated the assessment of the impact of microbial bioaugmentation on plant growth via AMF mycelia. The predominant bacterial taxa identified in the soil affected by AMF mycelia in the root-free compartment differed from those reported in other studies conducted on the hyphosphere [47, 48] or mycelia [49] of AMF without experimental designs separating them from the roots. Furthermore, the distribution of their abundance differed from that reported in a meta-analysis by Basiru et al. [31] on microbial communities associated with AMF. Interestingly, the dominant bacterial taxa exhibited greater similarity to the bacterial community found in the hyphosphere fungal mats of Basidiomycota species [50], characterized by a prevalence of the phyla Planctomycetota, Chloroflexota, and Pseudomonadota, and at the genus level, an abundance of Pirelulla, Pir4 lineage, Gemmata, and Massilia. However, the most abundant genera differed between these studies and ours. Consequently, our findings for the soil influenced by the hyphosphere more closely resemble those found in fungal mats compared to those in mycorrhizal fungal communities associated with plant roots. The absence of plant influence due to the microcosm units, separated by two nylon membranes with an opening size of 44 μm, altered the bacterial community. The soil community in our study appeared to consist of taxa belonging to the “mycophilic guild” [50]. Notably, the phylum Planctomycetota, which predominated in our study, appeared to be enhanced by the absence of plant roots.

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: By using a GFP-tagged D2R strain, we detected the expression pattern of D2R in the third- instar larval brain, specifically in dopaminergic and mushroom body neurons. Knockdown of D2Rs in DAN-c1 impaired aversive learning, while knockdown of D2R in mushroom body neurons led to deficits in both aversive and appetitive learning. These results revealed that D2Rs in distinct brain structures mediate different learning tasks. The newly discovered roles of D2R in the larval brain provides new insights into the mechanisms underlying larval associative learning. ResultsDistinct dopaminergic neurons innervate different compartments of the mushroom bodyThe connectome of larval learning has been investigated in both first- and third-instar larvae28,46. The mushroom body works as the learning center in Drosophila, which is composed by αβ, α’β’, and γ neurons in adult brains47. In larvae, axons from γ neurons bifurcate and form the vertical and medial lobes, as αβ and α’β’ neurons are not mature33,48,49. These lobes are divided into 11 compartments (refer to Figure 1) based on the coverage of neurites from both mushroom body extrinsic neurons (MBEN) and mushroom body output neurons (MBON)28. Around 21 dopaminergic neurons are found in each brain hemisphere, and categorized into 4 clusters: DM1 (dorsomedial), pPAM (primary protocerebral anterior medial, or DM2)50, DL1 (dorsolateral), and DL234. DL1 neurons project to the vertical lobe41, while pPAM neurons innervate the medial lobe50 (refer to Figure 1a-c). Figure 1. Identification of driver strains for single dopaminergic neurons in the Drosophila larval brain. (a) A schematic diagram shows the dopaminergic neuron (DAN) clusters and the mushroom body (MB) in the third-instar larval brain. (b) & (c) Schematic diagrams show the innervation patterns from distinct DANs to different compartments in the MB. All 11 MB compartments are shown, note that there is no synapse formed from dopaminergic neurons to the calyx and intermediate peduncle (IP).

Output: pass


Input: Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: Proceedings, 33rdWorkshop on 16. Hammons T. J. 2004 Geothermal power generation worldwide: global perspective, technology, field experience, and research and development. Components and Systems, 32 529 553 1532-5008 17. Hettiarachchi H. D. M. Golubovic M. Worek W. M. Ikegami Y. 2007 Optimum design criteria for an organic Rankine cycle using low-temperature geothermal heat sources Energy, 32 1698 1706 0360-5442 18. Holdmann G. 2007 ORC technology for waste heat applications. Presented at the Recovery and 2007. Available from: 19. Holm A. Blodgett L. Jennejohn D. Gawell K. Geothermal Energy. Association G. E. A. 2010 Geothermal energy: International market update. Available from: 20. Huenges E. (2010 2010 Geothermal energy systems, Wiley-& Co. 978-3-52740-831-3 KGaA, Weinheim, Germany. 10. 1002/9783527630479 21. Hung T. -C. 2001 Waste heat recovery of organic Rankine cycle using dry fluids and Management 42 539 553 0196-8904 22. Ismail B. I. 2011a Advanced electrical power generation technology using renewable & clean low-enthalpy geothermal energy sources. on Mechanical Engineering, 4 2 168 179 0187-4477 X 23. Ismail B. I. 2011b Power generation using nonconventional renewable geothermal and alternative clean energy technologies, Chap. 18, in: Planet earth 2011 Global warming challenge and opportunities for policy and practice, Edited by Carayannis, E. G. , 978-9-53307-733-8 Croatia. 24. Kalra C. Becquin G. Jackson J. Laursen A. L. Chen H. Hardy A. Klockow H. Zia J. 2012 High-potential working fluids and cycle concepts for next generation binary Cycle for enhanced geothermal systems. Proceedings, 37th Workshop on 25. Maizza V. Maizza A. 2001 Unconventional working fluids in organic Rankine-cycles for waste energy recovery systems Engineering 21 381 390 1359-4311 26. Majer E. Baria R. Stark M.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Surprisingly, bioaugmentation using the microbial suspension made from forest soil did not significantly alter the soil bacterial community structure in the root-free compartment. This result does not corroborate the first hypothesis, which proposed that amending the microbial suspension from natural soil would enhance bacterial diversity and modify the community structure. Overall, the diversity of the soil community appeared to be reduced, as there was also no evidence of AMF species influencing the soil. Another study utilizing a similar compartmented rhizobox system, albeit with a sterilized soil substrate [51], demonstrated very significant differences between the hyphosphere soil communities of different AMF species, contrary to our findings; the overall bacterial community also differed greatly in this study compared to ours. Consequently, AMF seemed to exhibit reduced host specificity in the hyphosphere soil within a soil-based substrate already containing an indigenous microbial community compared to a sterilized one. However, this does not necessarily mean that the AMF had no effect on the overall soil, as demonstrated by the impact of AMF on a forest soil bacterial community [51]. Rather, the hyphosphere soil community appeared to be highly conservative, as evidenced by the fact that microbial inoculation did not alter its composition in our study. The unexpected variability observed in the mycelial bacterial community when compared to the hyphosphere soil community is another intriguing finding. This result partially supports our second hypothesis. It is possible that AMF mycelia acted as a bacterial highway, facilitating the transportation of microbial communities from the root compartment to the root-free compartment through the nylon membranes. This is supported by de Novais et al. [52], who demonstrated that AMF hyphae facilitated the movement of bacteria between two interconnected plants via the same mycorrhizal network. Another potential mechanism is that AMF mycelia recruited part of their bacterial community from the amendment made by the microbial suspension. These recruited communities did not disperse in the hyphosphere soils but remained attached to the surfaces of the hyphae and spores, which likely explains the shifts observed in the bacterial communities between the mycelia and hyphosphere soils. Moreover, the taxonomic composition of the bacterial community observed in the mycelia differed significantly from that reported in an AMF inoculum of R. irregularis by Agnolucci et al. [53], as well as in another study by the same authors focusing on the spores of other AMF species [53].

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: DANs in DL1 (b) and in pPAM (c). (d-m) Drivers covering DANs in the DL1 cluster (d-h). Drivers covering DANs in the pPAM cluster (i-m). The first column shows drivers covering distinct dopaminergic neurons. Neurons under the drivers are labeled by GFP, the mushroom body (MB) is labeled by RFP, and dopaminergic neurons (DANs) are marked by tyrosine hydroxylase (TH) antibody. The second column (d’-m’) shows the GRASP signals from DANs under the driver to the corresponding compartments in the MB. The green channel represents the GRASP signals, and RFP marks the morphology of the MB. In the first column, white arrows mark the DANs under the driver strains. Yellow arrows in (d) show the pPAM neurons not labelled by TH-GAL4 driver strain. The yellow arrowhead in (k) showed the DL1 neuron not innervating the MB. Scale bars: 50 µm for the first column, and 20 µm for the second column. Abbreviations: DL, dorsolateral; DM, dorsomedial; IP, intermediate peduncle; IT intermediate toe; IVL, intermediate vertical lobe; LA, lateral 1. In this study, we wanted to functionally identify individual DANs that mediate larval olfactory learning. The first step was to search for DAN-specific driver strains that mark single dopaminergic neurons, which subsequently can be used to target genetic manipulations of corresponding neurons. A total of 56 driver strains identifying dopaminergic neurons were screened in this study (Table 1). These strains were chosen based on previous studies, either identifying a single dopaminergic neuron in larvae, or identifying only several in adult brains and indicating the potential of identifying single DAN in larvae. TH-GAL4, a traditional dopaminergic neuronal driver51, identifies all dopaminergic neurons except those in pPAM (Figure 1d, Figure S1a). Split-GFP reconstitution across synaptic partners (GRASP) technique was used to investigate the “direct” synaptic connections from DANs to the mushroom body (MB), in which portions of GFP were specifically expressed in corresponding neurons (Figure S2d).

Output: pass


Input: Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: Oates S. Bommer J. Smith B. Asanuma H. (2007 2007 Induced seismicity associated with Systems. Geothermics, 36 185 222 0375-6505 27. Majorowicz J. Grasby S. E. 2010 Heat flow, depth-temperature variations and stored thermal energy for enhanced geothermal systems in Canada. J. Geophys. Eng. , 7 232 241 1742-2132 28. Panea C. Rosca G. M. Blaga C. A. 2010 Power generation from low-enthalpy geothermal resources. Journal of Sustainable Energy, 1 2 1 5 2067-5538 29. Pruess K. 2006 Enhanced geothermal systems (EGS) using CO2 as working fluid- A novel approach for generating renewable energy with simultaneous sequestration of carbon. Geothermics, 35 351 367 0375-6505 30. Randolph J. B. Saar M. O. 2011 Coupling carbon dioxide sequestration with geothermal energy capture in naturally permeable, porous geologic formations: Implications for CO2 sequestration. Energy Procedia, 4 2206 2213 31. Rosca M. G. Karytsas K. Mendrinos D. 2010 Low enthalpy geothermal power generation in Romania. 2010. 32. Ruggero-Bertani E. 2007 World geothermal generation in 2007, GHC Bulletin, 1 12 33. Saleh B. Koglbauer G. Wendland M. Fischer J. 2007 Working fluids for low-temperature organic Rankine cycles Energy, 32 1210 1221 0360-5442 34. Schuster A. Karellas S. Kakaras E. Spliethoff H. 2009 Energetic and economic investigation of Cycle applications. Engineering, 29 1809 1817 1359-4311 35. Stephens J. C. Jiusto. S. 2010 Assessing innovation in emerging energy technologies: Socio-technical dynamics of carbon capture and storage (CCS) and enhanced geothermal systems (EGS) in the USA. Energy Policy, 38 2020 2031 0301-4215 36. Wei D. Lu X. Lu Z. Gu J.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Therefore, it can be inferred that substrate composition has an impact on the bacterial communities associated with plant roots [54], which in turn could influences AMF mycelial bacteriome. Certain bacterial taxa, such as Chloroflexota, which were abundant in the mycelia in our study, were not present in the spores in these two studies, suggesting a context-specific association with mycelia development in the soil. Despite the substantial beta diversity difference observed between the hyphosphere soils and mycelia, a significant number of ASVs, as well as the majority of bacterial genera, were shared between the two biotopes, indicating a certain degree of connectivity between them. Consequently, our findings suggest a higher level of host selectivity within the mycelia compared to the hyphosphere soil, and/or a lack of consistency in the mycelia bacterial community, which may result in some stochastic effects, consistent with the increased dispersion observed in mycelia communities. The bacterial community profiles associated with different AMF species varied. In particular, the species R. cerebriforme exhibited a strong correlation with three bacterial genera, including Glomeribacter, known for forming symbiotic associations with AMF species [55]. Additionally, R. clarus and R. irregularis each showed a correlation with one bacterial genus. These data support the third hypothesis, suggesting that while all AMF species establish some level of association with certain bacterial taxa, the primary selection process predominantly occurs within the mycelia and is less pronounced in hyphosphere soils. The observed variation in the bacterial community selection by AMF aligns with previous findings, indicating that some AMF taxa possess a higher enrichment of secreted protein genes compared to other species [10], which may lead to a more selective association with the bacterial community. Although the three AMF species belong to the same genus Rhizophagus, with R. irregularis and R. clarus being closely related compared to R. cerebriforme, we observed variations in the bacterial communities associated with their mycelia, suggesting the organismal phylogeny influence of bacterial communities associated with AMF mycelia. Notably, the mycelial bacterial community of R. cerebriforme contained 60 bacterial core taxa exclusive to it, while R. clarus and R. irregularis had 25 and 9 exclusive taxa, respectively.

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: GRASP results showed neurons under TH-GAL4 formed synapses in the vertical lobe and lower peduncle (white dash lines in Figure 1d’), consistent with previous electron microscopy data46. We found three DAN driver strains that identify single dopaminergic neurons in the third-instar larval brain hemisphere. DAN driver R76F02-AD;R55C10-DBD identifies a dopaminergic neuron innervating the lower peduncle (LP), which would be DAN-c1 based on previous published nomenclature28 (Figure 1f). MB296B driver identifies the dopaminergic neuron (DAN-d1) projecting to the lateral 1. Driver strains screened for dopaminergic neurons in the third-instar larval brain. Strains are listed with their published names and names used in this work. The numbers of dopaminergic and non-dopaminergic neurons in the third-instar larval brain are described. The identities of dopaminergic neurons from distinct clusters are also listed, especially for those in DL1 and pPAM. The analogs column lists the labelled neurons in previous publications. Source/Gift column shows the original papers in which these strains were described, as well as the laboratories these strains were obtained from. Several, 2-5 neurons; some, 6-10 neurons; lots, >10 neurons. In pPAM, R58E02 driver identifies DAN-h1, i1, and j1, innervating the shaft (SHA), intermediate toe (IT) and upper toe (UT) (Figure 1i), and R30G08 driver identifies DAN-h1 and i1 (Figure 1j). As described in a previous report28, SS864 driver identifies DAN-i1, innervating the upper toe (Figure 1l), and SS1757 driver identifies DAN-k1 which innervates the lower toe (LT) (Figure 1m). In contrast, SS1696 driver identifies not only DAN-h1, but also i1 and one DL1 neuron not innervating the mushroom body (Figure 1K). In summary, our results show that five DL1 and four pinnervate nine distinct mushroom body compartments in a one-to-one pattern (Figure 1b and c).

Output: pass


Input: Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: Thermodynamic pre-determination of power generation potential in geothermal low-temperature applications access Published: 26 April 2014 Thermodynamic pre-determination of power generation potential in geothermal low-temperature applications Henrik Öhman1 & Per Lundqvist2 0. 5-MWe plant is considered. Obviously, the relative cost will then rise significantly for small-scale power plants and could prohibit the use of such. In order to simplify and increase the confidence of second law analysis output, Öhman and Lundqvist ([2012]) suggested a simplified analysis method using a finite Carnot cycle analogy determining the Carnot Efficiency, ηc, Il, as the potential ratio of output work, W ˙ NPO to input heat, Q ˙ 1 a. η c Il = 1 n ∑ i = 1 n η c l i (1) where i is a very small, finite Carnot process in a series of n and ηc, l is the Carnot efficiency of i. Note that the small, finite and constant size step is taken in the dimension of heat flux from the heat source and that ‘finiteness’ or apparent specific heat capacity of both source and sink is required to determine ηc, l. The reason for using this summation is, as described in ( Öhman and Lundqvist [2012]), the lack of possibility to solve the equivalent integral analytically. One may bear in mind that is possible to derive by exergy analysis or entropy analysis, though an iterative procedure is required. The function of η c ˙ 1 is determined by ending the summation in Equation 1 at Q ˙ 1. This also gives a relation of ηc, Il(ψ U ), where ψ U is the utilization of the available heat transport from source to sink by a reversible power cycle. Though the calculations of is relatively simple and straight-forward (see Equation 1), it would still require the local engineer to set up a model in a suitable software by himself or purchase a commercial software, in order to perform his investigation. Making software is prone to cause errors and purchasing commercial software is likely to be non-attractive from a cost perspective. Small-scale power plants, using low-temperature heat, LTPCs, have entered the market in a number of formats.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: This supports the hypothesis that R. cerebriforme exhibits a highly selective association with its bacteriome, whereas R. irregularis demonstrates a comparatively weaker selection. Further research employing a variety of AMF species from different genera and families, coupled with both taxonomic and functional analyses, is required to elucidate the impacts of organismal phylogeny and life history strategies on the recruitment of bacterial communities by AMF. ConclusionsOur study employed a meticulously designed microcosm to explore how the identity of AMF influences the soil bacterial community structure in both enriched and non-enriched microbial environments. We also delved into whether mycelia from closely related AMF species cultivated under similar conditions would selectively shape distinct bacterial communities. Additionally, we evaluated the impact of AMF species, microbial bioaugmentation and their interactions on plant growth through AMF mycelia. Our findings unveiled notable disparities in the predominant bacterial taxa between soil influenced by AMF mycelia and those reported in previous studies on AMF hyphospheres or mycelia without experimental designs separating them from the roots. Intriguingly, our study’s bacterial communities closely resembled those found in hyphosphere fungal mats compared to those in mycorrhizal fungal communities associated with plant roots, indicating a significant alteration due to the absence of plant roots in our experimental setup. Unexpectedly, microbial bioaugmentation using a forest soil-derived microbial suspension did not significantly alter the soil bacterial community structure in the RFC. Furthermore, we observed a reduction in overall soil community diversity, suggesting no apparent influence of AMF species on the soil. The unexpected variability observed in the mycelial bacterial community compared to the hyphosphere soil community suggests that AMF mycelia may serve as a conduit for microbial transportation between compartments. Additionally, our study highlighted the impact of substrate composition on the bacterial communities associated with AMF mycelia. Furthermore, we observed a higher level of host selectivity within mycelia compared to hyphosphere soil, suggesting a more tailored interaction between AMF and the bacterial communities within mycelia. The distinct bacterial community profiles associated with different AMF species further support the hypothesis that selective association processes occur primarily within mycelia. This selective process appears to be influenced by organismal phylogeny, as evidenced by the variations observed in the bacterial communities associated with different AMF species.

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: DL1 neurons innervate the vertical lobe and peduncle, while pPAM neurons project to the medial lobe. The single neuronal driver strains screened can be used to investigate the roles of individually identified DANs in larval olfactory learning. Dopamine release from DAN-c1 is both necessary and sufficient for larval aversive learningDopamine plays an important role during olfactory associative learning in both adults and larvae52,53. In adults, dopaminergic neurons in PPL1 regulate aversive learning54–57, while those in PAM mediate reward signals in appetitive learning58–60. In larvae, DL1 neurons innervating the vertical lobe and the peduncle are required for aversive learning18,41, while those in pPAM projecting to the medial lobe are involved in appetitive learning50. 1 and Table 1, three driver strains identifying single dopaminergic neurons in DL1 were discovered, which could be candidates to investigate their roles in larval aversive learning. The R76F02-AD;R55C10-DBD strain identifies MB-MP1 in the adult brain61, which is a dopaminergic neuron involved in adult aversive learning62. This driver strain identifies one neuron per hemisphere in the third-instar larval brain (Figure 2a-c, Figure S1c). Using a UAS- DenMark;UAS-sytGFP strain, its dendrites were labeled with RFP and axonal terminals were marked by GFP. Its dendrites were localized in the dorsomedial protocerebrum (dml), and its axonal terminals located in the lower peduncle of the mushroom body (Figure 2a and d), with GRASP results supporting the existence of synapses in this compartment (Figure 2E). All these characteristics are consistent with the previously published nomenclature28, indicating that this neuron is DAN-c1 (Figure 2f), thus, this strain will now be referred to simply as DAN-c1. Figure 2. DAN-c1 is both necessary and sufficient for larval aversive learning. (a-f) R76F02-AD;R55C10-DBD driver is used as it covers one dopaminergic neuron, DAN-c1, in each brain hemisphere.

Output: pass


Input: Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: Öhman and Lundqvist ([2013]) investigated a large sample of operating plants as well as commercially available, standardized LTPCs. It showed that performance of all plants is a function of the ratio of utilized to potential heat transport from heat source to heat sink. With this knowledge, the need to simulate a particular thermal process, such as cycles (ORC; Schuster et al. [2009]), Kalina cycle (Walraven et al. [2013]), cycle (TFC; Fischer [2011]) or any other cycle becomes unnecessary for pre-optimizing plants and business cases. In order to offer a correct and cost-efficient tool, a graphic model was therefore developed in order to allow the simplest possible access to Equation 1. Combined with a calculated absolute maximum rate of heat exchange from the heat source Q ˙ CA any practitioner is able to present a scientific best estimate of the maximum expected power output and the corresponding required utilization of available heat exchange between source and sink. This allows for estimating size and cost of the major cost items as well as the expected sales volume of electric power. The authors' intention is to enhance the use of small-scale LTPCs by offering this simplified analysis procedure. model is dependent on a number of parameters making a detailed graphic version multidimensional and too complicated. Therefore, a reduction of dependent parameters was performed by analysing the model sensitivity to each parameter. Intuitively, we understand that the entry temperatures of source and sink, T1 and T2 and the thermal capacity of the fluid streams are parameters affecting the potential power generation of a power cycle. More often overlooked is the dependence on the utilization as defined in Equation 2. ψ U = Q ˙ 1 Q ˙ CA (2) where Q ˙ CA corresponds to the heat transfer rate from the source when exit temperature of source and sink coincide and work is produced entirely reversibly, see Equations 4 and 5. Q ˙ 1 represents the particular rate of heat transferred from the heat source to the process. Figure 1 shows the thermal entities of a general power cycle in an application with relatively low temperature difference between source and sink. As heat transfer from the source is used as the abscissa; the temperature gradient of the source flow is shown linearly. Please note that the graphic model developed in this 2. Full size imageEfficiency is also included in Figure 1.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Intestinal anastomotic injury alters spatially defined microbiome composition and function access Published: 15 September 2014 Intestinal anastomotic injury alters spatially defined microbiome composition and function Shogan1, Smith2 nAff4, Scott Christley1, Gilbert2,3, Olga Zaborina1 & …Alverdy1 Show authors Microbiome 0. Point sizes represent the higher point-in-time average abundance of the group relative to the entire microbial community. Groups comprising less than 1% of the total community on POD0 and POD6 are excluded. Full size imageFigure 3Comparative analysis of bacterial abundance in anastomotic tissues at POD0 and POD6. The analysis is presented at phylum, order, class, family, and genus levels. Full size imagePredicted functional profiling suggests the potential of virulence-associated pathways in post-anastomotic tissuesThe functional potential of the microbiota associated with the intraluminal contents (stool) and tissue before (POD0) and after anastomosis (POD6) was predicted using [6]. There were no statistically significant functional differences for the intraluminal contents between POD0 and POD6, while the microbiota associated with intestinal tissue revealed multiple significantly altered functional attributes after anastomosis (Table 1). The upregulated functions in tissue-associated microbiota can be traits of Escherichia and Enterococcus. The virulence-associated functions such as pore formation by hemolysin E [7], bacterial attachment to mammalian cells via AIDA-I adhesin-like protein [8], cytotoxicity to eukaryotic cells by cytolethal toxins [9] and cytotoxic necrotizing factor 1 [10], increased motility (outer membrane usher protein, fimbrial-like protein, type 1 fimbrial protein, fimbrial chaperone protein) [11], and invasion of epithelial cells via invasion B [12] can be attributed to Enterobacteriaceae, particularly, Escherichia. The proteolytic cleavage by coccolysin also known as gelatinase can be attributed to Enterococcus[13, 14]. Importantly, gelatinase GelE of Enterococcus faecalis has been shown to degrade collagen, a critical component for anastomotic tissue healing [15, 16].

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: (a) Dendrites and axons of DAN-c1 are labeled by DenMark and sytGFP, correspondingly. (b-e) The innervation patterns of DAN-c1. DAN-c1 is labeled with GFP, the MB with RFP, and DANs with TH antibody (blue color). Only one DAN soma is identified (c), axons from DAN-c1 innervate the lower peduncle (LP) of the MB (d), and synapses from DAN-c1 to the LP of the MB are shown by GRASP (e). A schematic diagram (f) shows the innervation patterns of DAN-c1. Modified from Eichler et al. 46, and Saumweber et. al. 28. (g) A schematic paradigm for larval olfactory learning (top) and two different training paradigms for thermogenetics (bottom). (h-j) Blocking dopamine release from DAN-c1 during learning using shibirets1 strain under 34°C impairs larval aversive learning. (k-l) Activation of DAN-c1 with dTRTPA1 under 34°C induces aversive learning. QUI, quinine. DW, distill water. SUC, sucrose. 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 S2. Scale bars: 50 µm (a and b), 20 µm (c, d and e). To reveal the role of DAN-c1 in larval olfactory learning, a single odor learning paradigm and thermogenetic tools were applied17,18,63. Compared to those trained with distilled water (DW), control larvae exhibited repulsion to the odorant pentyl acetate (PA) after being trained with quinine (QUI) paired with PA, reflecting aversive learning. In contrast, larvae were attracted to PA after being trained with sucrose (SUC) paired with PA, reflecting appetitive learning. The extent of repulsion or attraction was represented with a response index (R. I. ) that is compared to the DW group (Figure 2g).

Output: pass


Input: 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. 84390 this 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation by Basel I. From the 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. natural heat energy produced from the Earth is called “geothermal heat energy. ” It is proved to be an environmentally friendly clean energy source that could significantly help to mitigate GHG emissions when used for power generation [1]. The source of geothermal energy is the continuous heat energy flux flowing from the interior of the Earth toward its ground surface. The geothermal resources of the Earth are vast. For instance, the amount of geothermal energy stored at a depth of approximately 3 km is estimated to be 1,194,444,444 TWh which is much larger compared to all fossil fuel resources combined, whose energy equivalent is estimated to be 1,010,361 TWh [1, 2]. Unlike other renewable energy resources, geothermal energy has distinct characteristics; namely, it is available and stable at all times throughout the year, has an inherent storage capability, and is independent of weather conditions [1].

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: We further performed enrichment analysis for KEGG metabolic pathways to determine what metabolic changes occur at post-anastomotic tissues versus stool. The analysis indicates there are substantially more metabolic changes for tissue-associated microbiota than for the microbiota in the luminal contents, as expected based on changes in microbial community structure (Figure 4). Furthermore, the analysis of post-anastomotic tissues predicts virulence-associated metabolic pathways such as lipopolysaccharide biosynthesis, bacterial secretion system, and shigellosis, which are not represented in the stool microbiota. The metabolic pathways were linked to the OTUs responsible for the functions (Figure 5). The analysis demonstrated a shift in the primary contributors to the metabolic functions such as genus Lactobacillus, order Clostridiales, and genus Ruminococcus for pre-anastomotic tissue versus family Enterobacteriaceae, genus Enterococcus, and genus Bacteroides for post-anastomotic tissue (Figure 5). Table 1 Predicted upregulated functions in tissue-associated microbiota after anastomosis Full size tableFigure 4Enriched metabolic pathways for pre- and post-anastomic tissue and stool. Pathway enrichment for KEGG metabolic pathways using HUMAnN followed by statistical comparative analysis using LEfSe was performed to determine differential enrichment between POD0 and POD6 for tissue and stool. Red color indicates that the KEGG metabolic pathway is enriched in comparison to the corresponding POD for either tissue or stool. Tissue and stool were not directly compared with each other, so common metabolic pathways do not indicate equivalent levels of enrichment for both. Full size imageFigure 5Contribution of taxonomic groups to the enriched metabolic pathways for pre- and post-anastomotic tissue. There is a shift in the primary contributors to the metabolic functions from taxa such as genus Lactobacillus, order Clostridiales, and genus Ruminococcus for pre-anastomotic tissue to taxa such as family Enterobacteriaceae, genus Enterococcus, and genus Bacteroides for post-anastomotic tissue. Values are scaled to a 0–1 range for each KEGG metabolic pathway (ko) individually, with 1 indicating the highest contributing OTU and 0 indicating no contribution by the OTU.

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: To examine the role of DAN-c1 in aversive learning, we used a Shibirets1 strain, which encodes a thermosensitive mutant of dynamin blocking neurotransmitter release when the ambient temperature is higher than 30°C by repressing endocytosis and vesicle recycling functions17. When trained at 34°C, the complete inactivation of dopamine release from DAN-c1 with Shibirets1 impaired aversive learning (Figure 2h), while Shibirets1 did not affect learning when trained at room temperature (22°C). These data indicated that dopamine release from DAN-c1 is necessary for larval aversive learning to occur. In the next experiments, a fly carrying temperature-sensitive cation channel, dTRPA1, was used to excite the DAN-c1 neuron because it can be activated at temperatures higher than 30°C58. Activation of DAN-c1 with dTRPA1 at 34°C during training induced repulsion to PA in the distilled water group (Figure 2k). These data suggested that DAN-c1 excitation and presumably increased dopamine release is sufficient for larval aversive learning in the absence of gustatory pairing. Combining the blockade results with Shibirets1, these data revealed that dopamine released from DAN-c1 activation is both necessary and sufficient in larval aversive learning. However, when paired with a gustatory stimulus (QUI or SUC), activation of DAN-c1 during training impairs both aversive and appetitive learning (Figure 2k). We suggest that these data indicate a critical role for the amount of DAN-c1 dopamine release in larval associative learning, as dTRPA1 stimulation may result in excessive dopamine release. The expression pattern of D2R in the third-instar larval brainAlthough dopamine D1-like receptors have been proven important for learning42, the role of D2-like receptors has not been fully investigated. In addition, the expression pattern of D2R in fly brains was not reported. A fly strain expressing GFP-tagged D2R (BDSC#60276) was used to reveal the expression pattern of D2R in the third-instar larval brain (Figure 3a). D2Rs were found in dopaminergic neurons and the mushroom body.

Output: pass


Input: Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: It was estimated that the world net electricity demand is going to increase by approximately 85% from 2004 to 2030, increasing from 16,424 TWh (in 2004) to 30,364 TWh (in 2030), so that the utilization of geothermal energy for electric power production continues to be a promising solution, more particularly with the new discoveries of innovative technological methods of power generation cycles and drilling technologies. The utilization of geothermal energy resources can also be used for direct heating applications [1]. In general, the utilization of geothermal energy is divided into two parts: (a) electricity generation and (b) direct heating (non-electrical) applications. Recently, this form of renewable energy source has grown in 25 countries, with installed geothermal-electric capacity up to 11 GWe in 2010 [1, 2], and is increasingly contributing to the electric power supply worldwide [1, 2, 3]. Geothermal energy resources vary from one geographic location to another, depending on the depth, temperature, and pressure of the geothermal resource, the abundance of ground water, and the underground chemical composition. Geothermal energy resources typically differ in temperature from about 50 to 350°C. The high-temperature geothermal resources (with temperature >200°C) are typically found in volcanic regions and island chains, whereas the medium-temperature (150–200°C) and low-temperature geothermal resources (<150°C) are usually found widely in most continental regions and by far the most commonly available geothermal resource [2, 3]. The increase in temperature with depth in the Earth’s crust can be expressed in terms of what is known as the geothermal temperature gradient. Down to the depths accessible by drilling with state-of-the art technology (over 10 km), the average geothermal gradient is about 2. 5–3. 0°C/100 m [2]. For example, at depth around 3 km below ground surface, the estimated temperature is approximately 90°C. There are, however, regions in which the geothermal temperature gradient is far from the average value. For instance, in some geothermal areas, the gradient is 10 times the average value due to composition of these areas and geothermal structure [2]. It was also reported that the emissions of GHG from geothermal power plants, in general, constitute less than 2% of the emission of these gases by fossil-fuelled power plants [2].

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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].

Output: contrast


Input: 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).

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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).

Output: pass


Input: 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].

Output: corroborate


Input: 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.

Output: contrast


Input: 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).

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast