Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: While there have been several poultry studies demonstrating unique age-driven shifts in microbial composition across different intestinal sites [50,51,52], the role of age in the poultry respiratory microbiota remains relatively understudied. The first study to provide insight into the role of age on the poultry respiratory microbiota was conducted by Shabbir et al. [5], which characterized the lower respiratory microbiome of birds from different age groups (36, 40 and 55 weeks) from three different farms in the Punjab province of Pakistan. While the study reported unique differences in microbiota composition and diversity across the evaluated samples, it is difficult to attribute the observed effects solely to the age of the flocks. Multiple confounding variables from experimental design, including differences in sex, bird type (layers vs breeders), production system (open-house vs. free-range vs. controlled-house), and small sample size could have contributed to the observed outcome. The comparative study of Glendinning et al. [24] offered better perspective on the effect of age on the respiratory microbiota. The study reported different nasal microbiota composition from birds of different age groups, and it was found that two-day old and three-week-old birds recorded a high abundance of Lactobacilli. However, 30-month-old birds recorded a lower abundance of Lactobacilli, while Jeotgalicoccus and Staphylococcus were the most abundant genera in the 30-month-old birds. Additionally, microbial richness and diversity increased in buccal and nasal samples with increasing age, except for BAL samples. Another study by Ngunjiri et al. [25] reported a significant shift in nasal microbial communities in layers at 16–25 weeks of age compared to brooding (0–5 weeks of age) and grow-out (6–16 weeks of age) stages. This finding is also supported by the results from Kursa et al. [9] study, which showed increased numbers of unique Variant (ASV) in tracheal samples as turkeys aged, suggesting increased microbial richness with increasing age. Interestingly, a similar observation was also noted for chickens in the same study. While these findings help to justify that a direct linear relationship exists between bird age and microbial composition and diversity in the respiratory tract, this may not be the case. Our speculation is that during the first weeks of life, microbial colonization and diversity increases until an age-independent microbial stabilization plateau is reached. [SEP] Justify:

Output: The text contrasts the claim by providing evidence that microbiome composition varies significantly with age and among different species of birds. It discusses various studies that demonstrate unique differences in microbiota composition and diversity across evaluated samples, highlighting that the microbiome is not identical across individuals but is influenced by multiple factors including age and environmental conditions.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: This is because firstly, there might be shibire/ts-insensitive ways of dopamine release, and secondly because shibire/ts may affect co-transmitter release from DANs. Shibirets1 gene encodes a thermosensitive mutant of dynamin, expressing this mutant version in target neurons will block neurotransmitter release at the ambient temperature higher than 30C, as it represses vesicle recycling1. It is a widely used tool to examine whether the target neuron is involved in a specific physiological function. We cannot rule out that there might be Shibirets1 insensitive ways of dopamine release exist. However, blocking dopamine release from DAN-c1 with Shibirets1 has already led to learning responses changing (Figure 2h). This result indicated that the dopamine release from DAN-c1 during training is important for larval aversive learning, which has already supported our hypothesis. For the second question about the potential co-transmitter release, we think it is a great question. et al. reported co-neurotransmitters in dopaminergic system modulate adult olfactory memories in _Drosophila_11, and we cannot rule out the roles of co-released neurotransmitters/neuropeptides in larval learning. Ideally, if we could observe the real time changes of dopamine release from DAN-c1 in wild type and TH knockdown larvae would answer this question. However, live imaging of dopamine release from one dopaminergic neuron is not practical for us at this time. On the other hand, the roles of dopamine receptors in olfactory associative learning support that dopamine is important for Drosophila learning. D1 receptor, dDA1, has been proven to be involved in both adult and larval appetitive and aversive learning12,13. In our work, D2R in the mushroom body showed important roles in both larval appetitive and aversive learning (Figure 6a). All this evidence reveals the importance of dopamine in Drosophila olfactory associative learning. In addition, there is too much unknow information about the co-release neurotransmitter/neuropeptides, as well as their potential complex ‘interaction/crosstalk’ relations. We believe that investigation of co-released neurotransmitter/neuropeptides is beyond the scope of this study at this time. [SEP] Justify:

Output: The text does not address or provide any evidence about the role of quantum effects in the function of olfactory receptors. Instead, it discusses the roles of dopamine and co-transmitters in larval learning in _Drosophila_, focusing on neurotransmitter release and receptor involvement. Therefore, it is irrelevant to the claim regarding olfactory receptors functioning through quantum effects.


Input: Task: corroborate the claim using the given text. Claim: 3D printing can create patient-specific surgical models for preoperative planning. [SEP] Text: The planned cut angle was 45° from the piriformis fossa to the anatomical axis of the femur to match the etched mark on the femoral stem (also 45° to the long axis of the stem). The femoral canal was prepared by the surgeon using the instructions for use provided by the implant manufacturer. The canal was opened using a starter reamer and femoral stem rasps, with sequentially increasing sizes, so that the etched stem marker was level with the cut surface of the femur and the rasp was secure when tested by twisting. The stem was then press-fitted and tested by twisting the implant within the femur and confirming that this did not cause movement between the stem and the bone. Post-operative radiological evaluationAll patients underwent conventional standing anteroposterior (AP) radiographs of the pelvis and hip post-operatively. The surgeon assessed size and position of cup and stem as well as restoration of leg length. Number of dislocations and revisions were recorded. Statistical analysisStatistical analysis was performed using Prism 7 (). Paired t-test test was used to determine the difference between the two groups. The reproducibility of planned and implanted cup and stem sizes was determined using the Pearson’s correlation coefficient. The level of significance for all statistical analyses was p < 0. 05. ResultsFeasibility studyStem and cup components used were within 1 size of the planned. This enabled us to safely optimize the size of the inventory for the pilot study. The feasibility study created the rules for a safe reduction in implant inventory: 1) the planned size of stem, plus one size above and below, and duplicated for all neck options; 2) the planned size, plus one size above and below, of cup, cup liners (both polyethylene and polyethylene hooded), and femoral heads (all 4 neck lengths for both 32 and 36 mm because cups cross boundary between 32 and 36 mm). Pilot studyThe pilot study showed: 1) 3D surgical planning correctly predicted sizes in 93% (42/45) of the femoral components and 89% (40/45) of the acetabular components; 2) a reduction in the implant inventory size from 101 to 39 components (a 61% reduction in inventory size); 3) good surgical outcomes with no difference between control and experimental groups and importantly, all patients in the group with a reduced inventory size had appropriately sized implants. [SEP] Justify:

Output: The text corroborates the claim by stating that '3D surgical planning correctly predicted sizes in 93% (42/45) of the femoral components and 89% (40/45) of the acetabular components.' This indicates that 3D printing technology was utilized for preoperative planning, confirming its role in creating patient-specific surgical models.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: Several factors, including the environment, stressors, and nutrition could potentially contribute to when the microbial plateau is achieved. A good buttress of this hypothesis is the observed significant taxonomic successional difference in the microbiota of old and young layer chickens [25]. As old layers are more exposed to several physiological and management stressors, they undergo more microbial plasticity compared to younger hens, implying they take a longer time (age) to reach a microbial stabilization plateau. Furthermore, the study by Taylor et al. [18] suggested that there might be a respiratory-site specific effect of age on poultry respiratory microbiota. The study reported significant differences in species richness and evenness in the nasal cavity compared to the trachea. Anatomically, the nasal cavity's constant interaction with the external environment may explain the observed difference. Summarily, while age plays a significant role in shaping the poultry respiratory microbiota, other factors may also contribute to the observed microbial composition and diversity. Further studies are thus needed to provide more insight on the complete role of age on poultry respiratory microbiota. Extrinsic factorsIt is common knowledge that a bird’s phenotype is largely influenced by its genotype and environmental influence. Hence it is not surprising that the environmental conditions of a bird affect its respiratory microbiota composition and diversity. For instance, the immediate environment of a poultry bird includes litter, feces, and moisture. All of which can potentially serve as a viable ground for pathogenic bacteria proliferation, depending on the stocking density, management conditions, and other factors. et al. [53] revealed that litter management could influence the respiratory microbiota of broiler chickens via an increase in atmospheric bacteria load. The authors conducted a longitudinal study to evaluate the effect of ambient air in a tunnel-ventilated broiler house on the tracheal microbiota. The tracheal microbiota was dominated by Escherichia coli/Shigella at week 3 of their study, which was correlated with litter aeration by tumbling. While litter aeration increases aerosols in the bird-rearing facility, it also prevents litter compaction brought on by moisture buildup from bird excrement, drinker leakage, and other factors [54]. Ivulic et al. [53] found that air tumbling as a management strategy reduced microbial diversity in the same period across the broiler rearing facilities in their study. [SEP] Justify:

Output: The text contrasts the claim by discussing how various factors, such as age, environment, and management conditions, significantly influence the composition and diversity of microbiota. It emphasizes that microbial plasticity varies among individuals and suggests that differences in microbiota exist due to environmental interactions and physiological factors, implying that microbiome compositions are not identical for everyone.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: Weakness #5: It is not clear whether the genetic controls when using the Gal4/ UAS system are the homozygous, parental strains (XY-Gal4/ XY-Gal4 and UAS-effector/ UAS-effector), or as is standard in the field the heterozygous driver (XY-Gal4/ wildtype) and effector controls (UAS-effector/ wildtype) (in some cases effector controls appear to be missing, e. g. Figure 4d, Figure S4e, Figure S5c). Almost all controls we used were homozygous parental strains. They did not show abnormal behaviors in either learnings or naïve sensory or locomotion assays. The only exception is the control for DAN-c1, the larvae from homozygous R76F02AD; R55C10DBD strain showed much reduced locomotion speed (Figure S6). To prevent this reduced locomotion speed affecting the learning ability, we used heterozygous R76F02AD; R55C10DBD/wildtype as control, which showed normal learning, naïve sensory and locomotion abilities (Figure 4e to i). 4d, it is a column graph to quantify the efficiency of D2R knockdown with miR. Because we need to induce and quantify the knockdown effect in specific DANs (DM1), only TH-GAL4 can be used as the control group, rather than UAS-D2R-miR. For the missing control groups in Figure S4e and S5c, we have shown them in other Figures (Figure 4e). We will re-organize the figures to make them easier to understand. Weakness #6: As recently suggested by Yamada et al 2024, bioRxiv, high cAMP can lead to synaptic depression (sic). That would call into question the interpretation of low-Dop2R leading to high-cAMP, leading to high-dopamine release, and thus the authors interpretation of the matching effects of low-Dop2R and driving DANs. We will read through this paper and try to add it as possible explanations for the learning mechanisms. As we introduced in the Discussion section, the learning mechanism is quite complex, mixing both non-linear neuronal circuits and multiple signaling pathways, in responding to complex environmental learning contexts. [SEP] Justify:

Output: The text does not address the claim regarding olfactory receptors and quantum effects; instead, it discusses genetic controls and experimental observations related to dopamine receptors and learning mechanisms. Thus, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: 3D printing can create patient-specific surgical models for preoperative planning. [SEP] Text: Accuracy of 3D-CT planningAll 45 patients had a cup or stem size within one size of planned. The achieved stem size corresponded to the preoperatively planned size in 93% (42/45) of cases. There was a high correlation between the planned and achieved stem sizes (r = 0. 99, p < 0. 0001). CT planning correctly predicted size in 89% (40/45) of the acetabular cup components. The 5 discrepancies had cups that were undersized by one size when compared to the planned size. There was a high correlation between the planned and achieved cup sizes (r = 0. 98, p < 0. 0001), Fig. 5. Fig. 5Correlation between the planned values and those achieved for the size of the femoral stem components (n = 45); (r = 0. 99, p < 0. 0001) (left image). Correlation between the planned values and those achieved for the size of the acetabular cups (n = 45) (r = 0. 98, p < 0. 0001) (right image)Full size imageAccuracy of pre-operative CT planning increased over time, Fig. 6. Fig. 6Correlation between the planned values and those achieved for the size of the femoral stem and acetabular cup component for the control (n = 25) and experimental (n = 20) groupsFull size imageHead diameter was accurately determined in the majority of the cases (80 to 90%), Fig. 7. Fig. 7Cumulative frequency of planned size being within ±1 size of target value for the femoral stems, − 1 size of target value for the acetabular cups and ± 2 sizes for the femoral heads in both control and experimental groupsFull size imageInventory assessment outcomeNo femoral fracture occurred during surgery and there were no errors attributed to a missing implant. The full inventory contained 101 implants for the Quadra stem and system. This was reduced to 39 implants following the feasibility study: a two third reduction (61%), Fig. 8. Fig. 8Column plot showing the difference in number of components available for one conventional hip replacement and the sizes needed following 3D-CT planning (comparison between the control and the experimental group)Full size imageSurgical outcomeThe post-operative course was uneventful for all patients (n = 45). [SEP] Justify:

Output: The text corroborates the claim by detailing the accuracy of preoperative CT planning, which is integral to the creation of patient-specific models. It cites high correlations between the planned and achieved sizes of implants, indicating that individualized surgical planning is effectively supported by advanced imaging techniques, which can be associated with 3D printing applications.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: High atmospheric bacterial load and temporal increase in ammonia levels in the barn facilitated by the litter aeration by tumbling are deemed responsible for the unique tracheal microbiota composition observed in their study. Poor barn ventilation, which results in high ammonia and dust concentrations, can be predisposing factors for pathogenic E. coli, such as Escherichia coli (APEC) colonization of the bird respiratory tract [55]. In light of the implications of APEC, on bird health and welfare, it is imperative to closely examine innovative strategies for managing litter and barn environmental air. Furthermore, Ivulic et al. [53] reported a succession of tracheal microbiota from Escherichia coli/Shigella to Gallibacterium sp. at week 6, this reinforces the transient nature of E. coli observed at week 3. Additionally, given that microbiota dysbiosis often leads to inflammatory reactions [56, 57], it is plausible that the immuno-mucociliary clearance in the trachea contributed to the observed microbial succession. As highlighted above, ammonia pollution in poultry barns may promote pathogenic microbial colonization in the respiratory tract, which in turn disrupts respiratory health [58, 59]. The avian trachea is thought to be affected by ammonia stimulation, resulting in a concurrent shift in the microbial composition. Zhou et al. [13] confirmed this in their study which evaluated the effects of varying levels (0–35 ppm) of ammonia on broiler chicken tracheal microbiota. Exposure to 15–35 ppm levels of ammonia disrupted microbial homeostasis in the trachea, as evidenced by reduced alpha and beta diversity. Increasing levels of ammonia exposure also increased the communities of Blautia and Streptococcus. Species from both genera have been linked to multiple respiratory pathologies and are capable of preventing the colonization of symbiotic bacteria [60, 61]. This result emphasizes the necessity of implementing innovative litter management and anti-pollution strategies. Environmental stress, such as heat stress, is another factor that can induce a shift in respiratory microbiota. Oxidative stress, immunosuppression, inflammatory conditions, and microbial dysbiosis are a few of the negative impacts that heat stress can have on poultry [62, 63]. Relative to the intestine, the impact of heat stress (HS) on the respiratory microbiota is understudied. [SEP] Justify:

Output: The text contrasts the claim by stating that the unique tracheal microbiota composition observed in their study is influenced by factors like atmospheric bacterial load and ammonia levels, indicating variability in microbiome composition. Furthermore, it discusses how environmental stressors, such as ammonia pollution and heat stress, can lead to shifts in respiratory microbiota, further emphasizing that microbiomes are not identical but instead influenced by various environmental factors.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: We will try to develop a better hypothesis with the best compatibility to accommodate our results with published data. Reference (1) Honjo, K. & Furukubo-Tokunaga, K. Induction of cAMP response element-binding protein-dependent medium-term memory by appetitive gustatory reinforcement in Drosophila larvae. J Neurosci 25, 7905-7913 (2005). (2) Honjo, K. & Furukubo-Tokunaga, K. Distinctive neuronal networks and biochemical pathways for appetitive and aversive memory in Drosophila larvae. J Neurosci 29, 852-862 (2009). (3) Neve, K. A. , Seamans, J. K. & Trantham-Davidson, H. Dopamine receptor signaling. J 24, 165-205 (2004). (4) Saumweber, T. et al. Functional architecture of reward learning in mushroom body extrinsic neurons of larval Drosophila. 9, 1104 (2018). (5) Aso, Y. & Rubin, G. M. Dopaminergic neurons write and update memories with cell-type-specific rules. Elife 5 (2016). (6) Xie, T. et al. A for in Drosophila. 23, 652-665 (2018). (7) Hartenstein, V. , Cruz, L. , Lovick, J. K. & Guo, M. Developmental analysis of the dopamine-containing neurons of the Drosophila brain. J 525, 363-379 (2017). (8) Aso, Y. et al. The neuronal architecture of the mushroom body provides a logic for associative learning. Elife 3, e04577 (2014). (9) Eschbach, C. et al. Recurrent architecture for adaptive regulation of learning in the insect brain. 23, 544-555 (2020). (10) Draper, I. , Kurshan, P. T. , McBride, E. , Jackson, F. R. & Kopin, A. S. Locomotor activity is regulated by D2-like receptors in Drosophila: an anatomic and functional analysis. 67, 378-393 (2007). (11) Yamazaki, D. , Maeyama, Y. [SEP] Justify:

Output: The text does not address the claim regarding olfactory receptors and quantum effects. It discusses various studies related to memory and neuronal pathways in Drosophila without any mention of olfactory receptors or quantum mechanics. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: 3D printing can create patient-specific surgical models for preoperative planning. [SEP] Text: Standard post-op 2D radiography showed satisfactory restoration of leg length and femoral offsets Fig. 9. Fig. 9a Pre-operative AP radiograph and b, c post-operative radiographic AP and lateral views of the patient in standing and sitting position showing a satisfactory alignment of the prosthesisFull size imageThere was one revision operation. This was for cup loosening following a previous acetabular fracture in a patient with protrusio and was not related to the implant size. There were no other revision operations for any cause. There were two dislocations, which occurred as a result of excessive range of motion: deep hip flexion at 5 weeks post operative; and a deep “child’s pose” during yoga at 12 months post operative. Both of these cases were treated with one closed reduction procedure for each, post reduction CT showed a satisfactory position, and clinical outcomes were good (both had an OHS of 48 out 48 with no further dislocations at last review, which was 12 months and 4 months post dislocation). DiscussionTHA is one of the most successful and cost effective medical advances of the last 50 years [22]; the implant is the most expensive supply item for joint replacement [1, 5]. The current increase in demand is driving the rationale for cost savings [1, 4]. The modern approach to THA involves a more targeted treatment relying on the use of advanced image modalities for both diagnosis and treatment. Preoperative 3D surgical planning helps determine the correct implant size [18, 23], component orientation and fixation, achieve restoration of femoral offset and limb length [9, 10]. Moreover it enables the use of customised PSI which is sometimes an incorporated step in specialized planning software [24]. Accurate prediction of implant size during preoperative planning is an important factor for successful reconstruction in THA, to avoid intraoperative or postoperative complications. In addition, the accuracy of size prediction may facilitate inventory cost savings and reduce the risk of opening (and thus wasting) the packaging of an implant of the wrong size [25]. Aim of this study was to better understand the impact of the use of 3D surgical planning and PSI on hip implant inventory. This is the first study to investigate the use of CT-based planning software to optimise implant inventory in THA. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of preoperative 3D surgical planning, which helps determine the correct implant size and orientation. It mentions the use of 'customised PSI' (Patient Specific Instrumentation), indicating that 3D printing is involved in creating specific surgical models tailored for individual patients. This aligns with the claim's assertion about the capability of 3D printing in surgical planning.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: Several poultry studies [64,65,66,67] have recorded unique shift in gut microbiota in response to heat stress. In terms of the respiratory tract, Wang et al. [10] reported reduced bacterial richness and diversity in the lung microbiota of cyclic heat-stressed (30 °C, 10 weeks) layer-type pullets compared to their thermoneutral counterpart (24 °C). In terms of microbiota composition, significant reduction in members of the genus Brevibacillus was also recorded in the heat-stressed birds. This is a notable observation given that Brevibacillus species are often considered for probiotic use due to their ability to secrete antimicrobial peptides that can hinder the growth of pathogenic microorganisms [68]. In conformity with Wang et al. [10] result, Sohail et al. [12] also recorded reduced tracheal microbial diversity in broiler chickens subjected to chronic HS (35 ± 2 °C, 42 days). Additionally, their study provided insights into the limitations of popular methods utilized in microbiome research, particularly related to taxonomic resolution, such as PCR-DGGE and 16S rRNA throughput sequencing. Results obtained using PCR-DGGE showed that the impact of HS on the ceca was more pronounced compared to trachea. Moreover, dietary supplementation with mannooligosaccharides was found to restore tracheal microbial homeostasis in heat-stressed birds. In addition to identifying differences in microbial composition between HS and thermoneutral groups, the use of 16S rRNA high throughput sequencing provided distinct bacterial clades and relative abundances between treatment groups at a much lower taxonomic resolution. Taken together, these results demonstrate that HS induces respiratory microbiota community disruption and microbial diversity reduction. The extent of this microbial dysbiosis resulting from HS may vary based on factors such as the type, intensity, and duration of the stress [69]. Therefore, it is crucial to continue studying heat stress intervention strategies for the poultry industry, as the documented effect of reduced microbial diversity on decreased resistance to opportunistic infections has been well-established [70, 71]. Although yet to be fully explored, antibiotic use is another factor that can influence the poultry respiratory tract microbiota community structure. For over six decades, antibiotics have been used at sub-therapeutic doses to enhance the growth performance of poultry flocks [72]. [SEP] Justify:

Output: The text contrasts the claim by illustrating that there are significant variations in microbiome composition, particularly in response to factors such as heat stress. It presents specific research findings that show unique shifts in gut and respiratory microbiota in poultry, indicating that microbiome composition is not identical across individuals or conditions. For example, it notes 'unique shift in gut microbiota in response to heat stress' and 'reduced bacterial richness and diversity in the lung microbiota,' which supports the idea that microbiomes can differ considerably based on environmental factors. This directly contradicts the claim that suggests uniformity in microbiome composition.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: & Tabata, T. of Co-transmitters in. J Neurosci 43, 8294-8305 (2023). (12) Selcho, M. , Pauls, D. , Han, K. A. , Stocker, R. F. & Thum, A. S. The role of dopamine in Drosophila larval classical olfactory conditioning. PLoS One 4, e5897 (2009). (13) Kim, Y. C. , Lee, H. G. & Han, K. A. D1 dopamine receptor dDA1 is required in the mushroom body neurons for aversive and appetitive learning in Drosophila. J Neurosci 27, 7640-7647 (2007). [SEP] Justify:

Output: The text does not address the claim about olfactory receptors functioning through quantum effects. Instead, it references various studies that discuss dopamine and olfactory conditioning in Drosophila without mentioning quantum effects or olfactory receptors specifically.


Input: Task: corroborate the claim using the given text. Claim: 3D printing can create patient-specific surgical models for preoperative planning. [SEP] Text: We found that implant size prediction by 3D planning was accurate in up to 95% of the cases, which enabled us to safely reduce the inventory size by up to 61%. There were no complications relative to implant size. 3D planning correctly predicted implant size in 93% (42/45) of the femoral components and 89% (40/45) of the acetabular components. Stem and cup variability was within one-size in the feasibility study, which gave us the confidence to optimize the inventory for the pilot study. The ability of 3D planning to predict implant size was up to 2. 5-fold superior to that reported when 2D templating is used (accurate in about a third of cases) [8, 26, 27]. A number of studies on cementless THAs have evaluated the accuracy of the preoperative planning by 2D or 3D templating. The agreement with cup size prediction was found to be between 20 and 42. 2% with 2D planning and 86–96% with 3D planning, while the agreement with stem size prediction was 19–68. 8% with 2D planning, 52–100% with 3D planning [25, 27]. Our results are in accordance with the existing literature, Table 2. Table 2 Rate of prediction for cup and stem size when using 3D planningFull size table2D templating is still the most commonly used planning method, despite its lower accuracy when compared to CT planning [15, 16], particularly when using cementless components [31, 32] over cemented implants [9, 33] and the magnifications issues associated with conventional 2D radiographs. A number of barriers hindrance the widespread use of new technology. The use of CT data introduces new issues related to how best to display and use all information in order to provide surgeons with a user-friendly interface [34]. Another barrier to large adoption of CT planning is the learning curve associated with new technology and systems. Moreover, CT-based planning platforms that are not tied to an implant manufacturer, have recently been introduced to the European and US marketplace, but their uptake has been limited due to software and imaging costs, radiation dose and a lack of standardized optimized imaging protocols. The use of 3D surgical planning has been largely limited due to the associated ionizing radiation exposure to the patient, limited availability of generic software planning solutions, high cost and limited availability of pre-operative CT imaging protocols. [SEP] Justify:

Output: The text corroborates the claim by indicating that 3D planning has superior accuracy in predicting implant sizes compared to 2D templating, specifically noting that it predicts implant size in 93% of cases. This supports the idea that 3D printing and planning can be effectively utilized in a preoperative context to tailor surgical models to individual patient needs.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: However, their continued use has been linked to public health concerns related to antibiotic resistance and residues, leading to calls for their reduction and ultimate discontinuation by governments and the general public [73]. Several studies [71, 74, 75] have affirmed that antibiotics are capable of inducing changes in microbial structure in the poultry gut. This antibiotics-induced shift in microbial structure has also been associated with decreased adaptive immune responses in poultry, particularly in a dose-dependent manner [76,77,78]. Conversely, only one study [18] investigated the influence of antibiotics on the poultry respiratory microbiota. In this study, commercial turkey flocks treated with chlortetracycline recorded a lower abundance of Mycoplasma in the trachea. The abundance of Mycoplasma was negatively correlated with the average weight of birds in the same study, which is notable considering a link between gut microbiota composition and bird weight has previously been established [79]. Vancomycin-colistin induced gut microbiota dysbiosis with concomitant impairment of lung immunity has also been reported in mice [80]. Despite these findings, further studies are required to fully understand the role of antibiotics in dysbiosis of the poultry respiratory tract and its effects on immunity and growth performance. Pathogens, especially viruses, have also been shown to cause respiratory microbiota disruption in humans [81,82,83]. This has recently been demonstrated in the poultry respiratory tract in a longitudinal study in which birds were challenged with low pathogenic avian influenza (LPAI) H5N2 virus [84]. Viral infection increased the total bacterial content in nasal microbiota samples; however, this increase had an inverse relationship with bacterial species richness. Samples retrieved from the nasal cavity also showed significant virus-induced change in beta diversity. Results from this study also suggest that virus-induced microbiota disruption is respiratory-site specific. Compared to the control treatment in this study, virus-induced microbial disruption across several taxa reduced linearly from the URT to the LRT. The number of microbial taxa disrupted by the LPAI H5N2 virus were 16, 5, and 1 across the nasal cavity, trachea, and lungs, respectively. [SEP] Justify:

Output: The text contrasts the claim by presenting evidence that shows the variability in microbial composition. It discusses how antibiotics and viral infections can induce significant changes in microbial structure across different species and organs, such as the gut and respiratory microbiota, indicating that microbiome compositions are not identical. The reference to studies on the impact of antibiotics and pathogens highlights that there are distinct differences in microbiome responses depending on various factors.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text does not address the claim regarding olfactory receptors and their potential function through quantum effects. Instead, it focuses on dopamine receptors and their role in associative learning in Drosophila, which is unrelated to the mechanisms of olfactory receptors.


Input: Task: corroborate the claim using the given text. Claim: 3D printing can create patient-specific surgical models for preoperative planning. [SEP] Text: Recent advances in low-dose CT technology tailored for the specific purpose of planning orthopaedic surgery have made justifying the radiation exposure to the patient considerably easier [21, 28]. The new imaging protocols include imaging of the hip, knee and ankle regions at a comparatively lower effective radiation dose without a significant trade off in image quality [21, 28]. These protocols have seen a four-fold reduction in radiation and can be as low as equivalent to 3 pelvic radiographs [35], enhanced further by the introduction of orthopaedic metal artefact reduction (MARS) sequences on many modern CT scanners, allowing suppression of noise artefacts produced by pre-existing metal implants. Our results show a reduction in the size (up to 61%) of implant inventory based on the use that is conventionally adopted when the cases are not 3D planned for our single surgeon series. 3D surgical planning aids surgical planning and implant sizing with greater accuracy, crucial for good functional hip reconstruction; it therefore can help minimizing the number of surgical trays used during the operation [15, 36, 37]. 3D planning enables a reduction in intraoperative guesswork, and allows for an optimised implant inventory with the potential to reduce the costs to the both manufacturer and hospital without additional risk [23]. However, it is currently unknown what effect CT planning has on the size of hip implant inventory in hospitals. As the technological advancements in the field of orthopaedics have led to the introduction and development of sophisticated tools, 3D surgical planning is a promising field in which many more developments can be expected providing a quicker and more accurate surgery and the development of an optimal inventory of implants with benefits for all the parts involved in the chain. We acknowledge limitations. First, we evaluated only one design of implant for both acetabular and femoral components, therefore our findings relative to the reduction in inventory size cannot be generalized to every 3D planning platform and implant type. They can be used as a baseline comparison for future studies aimed at quantifying inventory size reduction. Secondly, this study was based on a series of cases performed by one experienced consultant orthopaedic surgeon, therefore it is unknown whether similar results can be expected for surgeons with different level of experience and using different surgical approaches. ConclusionPlanning of hip arthroplasty surgery on a 3D virtual CT-based model is useful to surgeons to help predict the size of the implants to be used in the operating room. [SEP] Justify:

Output: The text corroborates the claim by discussing the benefits of 3D surgical planning, which aids in predicting the size of implants during hip arthroplasty surgery. It emphasizes how 3D planning improves accuracy, reduces intraoperative guesswork, and helps in optimizing the implant inventory. These points align with the notion that 3D printing can facilitate the creation of tailored surgical models for effective preoperative planning.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: Additionally, genera Clostridium, Enterococcus, Escherichia-Shigella, and Pseudomonas were positively correlated with viral titers in the nasal cavity, while genera Enterococcus, unclassified Erysipelotrichaceae, Escherichia-Shigella, Staphylococcus, and Pseudomonas were positively correlated with viral titers in trachea. Conversely, the Lactobacillus genus was negatively correlated with viral titers in the trachea. In conformation with our respiratory site-specific microbiota disruption hypothesis, Yildiz et al. [83] have also reported marginal effects of virus infection on lung microbiota (LRT) in a murine model. Furthermore, considering that active virus replication occurred between days 5–7 post infection [83], another important finding from this study is that microbiota disruption may persist even after virus clearance. The reduction in bacterial species richness was still observed in samples retrieved from the nasal cavity even at 14 days post infection. Innate immune responses, particularly via cytokine production related pathways, is a possible mechanism for the virus-mediated microbiota dysbiosis. This has been pointed out in several in vivo mouse and poultry studies [71, 85,86,87]. Therefore, virus-induced dysbiosis could potentially support the proliferation of pathobionts and opportunistic infections in poultry flocks [88, 89]. It is yet to be seen if probiotic formulations can restore microbiota homeostasis in the poultry respiratory tract, following viral challenge. Despite the very limited number of studies that have explored the effects of various intrinsic and extrinsic factors on the poultry respiratory microbiota, it is important to state that in the absence of well-designed a priori experiments, it is difficult to determine which of these factors exert the greatest effects. Much of the available data in the literature are confounded by several variables in their experimental design, hence the interpretation of these results needs to be treated with caution. For instance, multivariate regression models revealed that experimental factors including bird type, age, body site, and flock in the study by Taylor et al. [18] were only able to explain about 54% of the total sample variance. This suggests the existence of additional sources of microbial heterogeneity beyond those accounted for. [SEP] Justify:

Output: The text contrasts the claim by highlighting the variability and heterogeneity in microbiome composition, noting that factors such as bird type, age, body site, and flock contribute to significant differences in microbial diversity. Specifically, it mentions that multivariate regression models were only able to explain about 54% of the total sample variance, suggesting that there are additional sources of microbial heterogeneity, which implies that microbiomes are not identical across individuals.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: 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. [SEP] Justify:

Output: The text does not provide any information regarding the functioning of olfactory receptors or their potential quantum effects. Instead, it discusses the role of dopamine and its receptors in mammalian brain functions, learning, and memory, as well as the use of Drosophila as a model organism for studying olfactory associative learning. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Some hospitals use 3D printing to produce medical tools and devices on-site. [SEP] Text: Οι μέθοδοι και τα πρωτόκολλα που αναπτύχθηκαν στα πλαίσια της παρούσας διατριβής αποτελούν νέες προσθήκες στην έρευνα για τον σχεδιασμό βιοκαταλυτών και αναμένεται να θέσουν τις βάσεις για την ανάπτυξη πιο προηγμένων βιοκαταλυτικών εργαλείων με αυξημένες δυνατότητες για ποικίλες βιο-σχετιζόμενες εφαρμογές. περισσότερα Περίληψη σε άλλη γλώσσα In the present thesis, they were developed methods and protocols under the scope to facilitate biocatalytic processes, with the use of Enzyme immobilization technology, Microfluidic technology, and 3D printing technology. The methods and protocols followed aimed at developing processes and tools that belong to the general area of biotechnology, a wide research field that utilizes biological systems or parts of them and their derivatives to develop useful products and processes for human health, food, energy, and environmental preservation. [SEP] Justify:

Output: The text corroborates the claim by mentioning that '3D printing technology' is part of the methods and protocols developed for biocatalytic processes. This implies that 3D printing is being utilized in the context of biotechnology, which can include the production of medical tools and devices.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: The information provided here on select intrinsic and extrinsic factors that potentially influence the respiratory microbiota would be valuable for the design and execution of poultry microbiota studies with reproducible outcomes. Role of poultry respiratory microbiota in pathobiology and immune responsesThe avian mucosal surfaces are occupied by an abundant number of microbial communities that are in constant interaction with the host, other members of the community and the environment. A healthy balance of this tripartite relationship, especially in terms of compositional stability and diversity, ensures a healthy microbiota in humans [90]. Any compositional or functional alteration of this relationship is referred to as microbial dysbiosis, which is generally characterized by increased abundance of pathobionts [91]. Microbial dysbiotic conditions are implicated in several pathological conditions in avian species, including pathogen colonization, excessive inflammation, dysregulated immune responses, and severe disease conditions [33, 92]. The potential of the respiratory microbiota to influence viral infections, vaccine responses, secondary bacterial infections, and disease severity has also been reported [12, 93]. Consequently, the role of resident microbiota in the poultry respiratory tract on the host immune system and disease outcomes continues to be the subject of recent research [13, 94]. Information from studies of this nature could offer a better understanding of economically important avian diseases and zoonoses. Economically important avian respiratory diseases that are microbiota-exacerbated or linked to bacterial etiologies include but are not limited to Mycoplasma infection, infectious coryza, Colibacillosis and [10, 28, 71, 95,96,97,98,99] (Table 2). Under homeostatic conditions in the respiratory tract, commensal microbiota contribute to host immunity by inhibiting pathogen persistence and transmission via several means, including competitive exclusion, bacteriostatic activities, biofilm formation, quorum sensing, and others [100]. Conversely, under dysbiotic conditions, potential respiratory pathogens include Escherichia coli, Ornithobacterium rhinotracheale, Mycoplasma synoviae, Mycoplasma gallisepticum, Gallibacterium anatis, Avibacterium species, and Staphylococcus aureus [9, 33, 101]. [SEP] Justify:

Output: The text contrasts the claim by discussing the diversity of microbial communities in avian species and how alterations in this balance can lead to conditions like microbial dysbiosis. It emphasizes that a healthy microbiota depends on compositional stability and diversity, which implies that not everyone's microbiome can be identical but rather, individuals have different microbial compositions.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text does not address the claim regarding olfactory receptors functioning through quantum effects. Instead, it discusses olfactory learning in Drosophila, focusing on training, gene expression, and the role of dopamine receptors in behavior. There is no mention of quantum effects related to olfactory receptors, making the text irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Some hospitals use 3D printing to produce medical tools and devices on-site. [SEP] Text: Biocatalysis utilizes one or more enzymes to convert substrates into higher-value compounds. Nowadays, the trends in biocatalysis follow three main directives: integration of novel technologies, intelligent manufacturing, and enzymatic total synthesis of complex natural products and drugs. As a sub-sector of microfluidic technology, flow biocatalysis, plays a decisive role in the concept of upgrading enzymatic processes, offering process intensificat. . In the present thesis, they were developed methods and protocols under the scope to facilitate biocatalytic processes, with the use of Enzyme immobilization technology, Microfluidic technology, and 3D printing technology. The methods and protocols followed aimed at developing processes and tools that belong to the general area of biotechnology, a wide research field that utilizes biological systems or parts of them and their derivatives to develop useful products and processes for human health, food, energy, and environmental preservation. Biocatalysis utilizes one or more enzymes to convert substrates into higher-value compounds. Nowadays, the trends in biocatalysis follow three main directives: integration of novel technologies, intelligent manufacturing, and enzymatic total synthesis of complex natural products and drugs. As a sub-sector of microfluidic technology, flow biocatalysis, plays a decisive role in the concept of upgrading enzymatic processes, offering process intensification and production yields that could never be realized with conventional technology. The research conducted so far has showed that, when growing out of the beaker, biocatalysis can be highly productive, controlled, and sustainable. Fundamental contribution to these features makes the fusion of enzyme immobilization technology with microreactor systems. The prevailing advantage of immobilization in microfluidic bioreactors is that the catalyst remains in the interior of the microreactor, while reagents and products are continuously pumped in and out of the reaction system, giving the opportunity to develop systems with high operational stability. With extremely low costs and almost infinite design possibilities, 3D printing has been considered to offer completely new ways of thinking about medicine and bioengineering. Publications for bio-based applications of 3D printed prototypes are growing exponentially in the last 5 years, while the first attempts to integrate this technology to the biocatalysis field have been lately witnessed. [SEP] Justify:

Output: The text corroborates the claim by discussing how 3D printing offers new possibilities in medicine and bioengineering, specifically mentioning that '3D printing has been considered to offer completely new ways of thinking about medicine and bioengineering.' This indicates that 3D printing technology is being integrated into medical applications, aligning with the claim about hospitals using it to produce tools and devices.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: In addition to modifying the structural composition of the microbiota, another way in which AIV infection influences antiviral immune response in the host is by influencing microbiota diversity. There are conflicting reports on the effect of AIV infection on microbiota diversity in the literature. Kaul et al. [97] reported significantly higher microbial diversity (Beta diversity) in AIV-infected ferrets compared to uninfected ferrets. These microbial perturbations are facilitated by host antiviral responses, such as the induction of interferon [97]. In contrast, Chrzastek et al. [111] reported reduction in microbial richness and phylogenetic diversity in the colon microbiota of H9N2 AIV-infected birds. Similar reduction in species richness have been reported in the fecal microbiota of H5N1 AIV-infected swans [114]. In spite of this inconsistency related to the effect of AIV infection on microbial diversity in the literature, a simplistic definition of “low” or “high” levels of diversity and their relation to host antiviral immune responses may not be entirely correct. A closer look at the specific colonizers of the microbial community and the ecological niche occupied may afford better insight. For example, microbiota dominated by and Veillonella may signify specie richness. However, these taxa have been associated with increased AIV replication [71, 98]. It is also possible that the activities of this taxa differ depending on the ecological niche occupied. Also, they may increase AIV virulence in respiratory sites compared to the gut. Nonetheless, being the main site of infection, the effect of AIV infection on the respiratory microbiome requires more attention. Conversely, certain microbial species have demonstrated positive impacts on adaptive immune responses and antiviral responses. For instance, Alqazlan et al. [119] showcased the ability of specific Lactobacillus strains (such as L. salivarius, L. johnsonii, and L. reuteri) to induce immunostimulatory and antiviral responses against H9N2 AIV. [SEP] Justify:

Output: The text contrasts the claim by discussing the diversity of microbiota as influenced by AIV infection. It mentions that there are 'conflicting reports on the effect of AIV infection on microbiota diversity,' suggesting that microbiomes can vary significantly between individuals, as illustrated by the differences in microbial diversity found between AIV-infected and uninfected animals. This indicates that microbiome compositions are not identical among individuals.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: 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). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the expression pattern and roles of D2 receptors in the larval brain in relation to learning tasks. It does not mention olfactory receptors or quantum effects, making it not applicable to the claim.


Input: Task: corroborate the claim using the given text. Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [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. 5772/54675 this 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation IntechDevelopments in Edited by From the in Edited by and 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. IntroductionWind energy is one of the most promising renewable energy resources for producing electricity due to its cost competitiveness compared to other conventional types of energy resources. It takes a particular place to be the most suitable renewable energy resources for electricity production. It isn't harmful to the environment and it is an abundant resource available in nature. Hence, wind power could be utilized by mechanically converting it to electrical power using wind turbine, WT. Various WT concepts have a quick development of wind power technologies and significant growth of wind power capacity during last two decades. Variable speed operation and direct drive WTs have been the modern developments in the technology of wind energy conversion system, WECS. Variable-speed operation has many advantages over fixed-speed generation such as increased energy capture, operation at MPPT over a wide range of wind speeds, high power quality, reduced mechanical stresses, aerodynamic noise improved system reliability, and it can provide (10-15) % higher output power and has less mechanical stresses in comparison with the operation at a fixed speed [1, 2]. [SEP] Justify:

Output: The text corroborates the claim by stating that wind power can be utilized by 'mechanically converting it to electrical power using wind turbine, WT.' This indicates that wind turbines do indeed convert kinetic energy from the wind into electricity, directly supporting the claim.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: These responses encompass the activation of interferon-stimulated gene (viperin) and the robust expression of cytokines in cells of the ceca tonsils, including T-helper (Th)1 type cytokines (interleukin [IL]-2, IL-12, and IFN-γ), pro-inflammatory cytokines (IL-1β and IL-6), and an immunoregulatory cytokine (IL-10). Moreover, a combination of Lactobacillus species, comprising Lactobacillus salivarius, L. johnsonii, L. reuteri, L. crispatus, and L. gasseri, has also been shown to enhance both cell- and antibody-mediated immune responses against H9N2 AIV [78]. This beneficial effect is particularly pronounced when co-administered with synthetic CpG oligodeoxynucleotides (ODN) 2007. Avian mycoplasmosisis a chronic infectious respiratory disease that affects poultry of all ages. The economic consequence of this disease includes poor hatchability, reduced egg production, poor weight gain and egg production [120]. The etiological agent of this disease is the pathogenic bacterium, Mycoplasma gallisepticum (MG), which is commonly isolated on most poultry farms. This pathogen can be spread both vertically and horizontally and can cause severe inflammatory responses and immune dysregulation in birds [120, 121]. Current management tools available to curb MG infection on farms include sourcing birds from breeding stock that are certified as healthy, implementing rigorous biosecurity practices, administering vaccinations, and delivering antimicrobials to infected birds [122, 123]. Although the use of antibiotics like fluoroquinolones and macrolides is a popular approach, there is a growing concern related to the development of antimicrobial resistance in this pathogen [124, 125]. By combining both 16S rRNA sequencing methods and an antibiotic-induced microbiota depletion protocol, Wang et al. [10] were able to provide insight into the role of the respiratory microbiota on MG infection in layer chickens. The findings of this study revealed that MG infection induces a distinctive shift in the microbiota community. Specifically, there was a significant decrease in the relative abundance of bacteria such as Staphylococcus, Lactobacillus, Weissella, and Butyricicoccus in their study. [SEP] Justify:

Output: The text contrasts the claim by describing how MG infection leads to a 'distinctive shift in the microbiota community,' indicating that microbiome compositions are not identical among individuals, since significant changes occur in response to infections. The mention of a significant decrease in the abundance of specific bacteria highlights further variability in microbiome compositions.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: 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). [SEP] Justify:

Output: The text provides a detailed description of dopaminergic neurons and experimental techniques related to their identification and study in the context of olfactory learning. However, it does not address olfactory receptors or any quantum effects regarding their function, making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [SEP] Text: WTs can be classified according to the type of drive train into direct drive (DD) and gear drive (GD). The GD type uses a gear box, squirrel cage induction generator (SCIG) and classified as stall, active stall and pitch control WT and work in constant speed applications. The variable speed WT uses doubly-fed induction generator, (DFIG) especially in high power WTs. The gearless DD WTs have been used with small and medium size WTs employing permanent-magnet synchronous generator (PMSG) with higher numbers of poles to eliminate the need for gearbox which can be translated to higher efficiency. PMSG appears more and more attractive, because the advantages of permanent magnet, (PM) machines over electrically excited machines such as its higher efficiency, higher energy yield, no additional power supply for the magnet field excitation and higher reliability due to the absence of mechanical components such as slip rings. In addition, the performance of PM materials is improving, and the cost is decreasing in recent years. Therefore, these advantages make direct-drive PM wind turbine systems more attractive in application of small and medium-scale wind turbines [1, 3-4]. Robust controller has been developed in many literatures [5-15] to track the maximum power available in the wind. They include tip speed ratio (TSR) [5, 13], power signal feedback (PSF) [8, 14], and the hill-climb searching (HCS) [11-12] methods. The TSR control method regulates the rotational speed of the generator to maintain an optimal TSR at which power extracted is maximum [13]. For TSR calculation, both the wind speed and turbine speed need to be measured, and the optimal TSR must be given to the controller. The first barrier to implement TSR control is the wind speed measurement, which adds to system cost and presents difficulties in practical implementations. The second barrier is the need to obtain the optimal value of TSR, this value is different from one system to another. This depends on the turbine-generator characteristics results in custom-designed control software tailored for individual wind turbines [14]. In PSF control [8, 14], it is required to have the knowledge of the wind turbine’s maximum power curve, and track this curve through its control mechanisms. [SEP] Justify:

Output: The text corroborates the claim as it discusses various types of wind turbines (WTs) and their mechanisms for generating power. It mentions that robust controllers have been developed to track maximum power available from the wind, indicating that wind turbines are designed to optimize the conversion of kinetic energy (from the wind) into electrical energy. Specifically, the discussion on the tip speed ratio (TSR) control method highlights the importance of measuring wind speed and turbine speed to maximize power extraction, directly supporting the claim about the operational principles of wind turbines.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: Staphylococcus and Lactobacillus genera have been identified as microbiota associated with optimal respiratory health [126]. On the other hand, the relative abundance of potential pathogens that include Bacteroides, Serratia, Enterococcus, Prevotella, Pseudomonas and Acinetobacter was increased by MG infection [10]. Furthermore, to directly associate specific bacteria with MG infection, nine Gram-negative bacterial species (Serratia marcescens, Bacteroides ovatus, Parabacteroides distasonis, Prevotella copri, Subdoligranulum variabile, Bacteroides fragilis, Acinetobacter calcoaceticus, Proteus mirabilis and Pseudomonas aeruginosa) were isolated from chicken BAL samples and cultured. Results showed that of all isolated bacteria, only Serratia marcescens significantly increased MG colonization in the respiratory tract when intranasally delivered to chickens [10]. Similar results of microbial disruption following MG infection have also been reported in the chicken gut [127]. In addition to disrupting the respiratory microbiota community structure, MG infection also affects microbial diversity. Shannon and Chao diversity indices were both significantly reduced by MG infection compared to the control treatment in a study by Wang and colleagues [10]. This decrease in diversity of the respiratory tract following MG infection is consistent with further works from the same group [28]. The disruption of microbial homeostasis in the respiratory tract recorded in these studies triggered a concomitant immunological response and tissue damage. MG-infected chickens with disrupted microbiota exhibited increased levels of levels of pro-inflammatory cytokines (IL-1β, IL-8 and IL-6) in the lungs and severe lung tissue damage [10]. Despite the immunological challenge that microbiota dysbiosis creates, microbiota manipulation could also potentially afford a solution to this challenge. Some studies suggest that the use of Gram-positive bacteria like Lactobacillus and Staphylococcus may facilitate protection against respiratory infection [126, 128]. This use of probiotics or direct-fed microbials requires more research in order to ensure therapeutic effectiveness in poultry. ColibacillosisColibacillosis is another poultry disease that causes significant economic losses, especially in terms of morbidity, mortality, stunted growth performance, and carcass condemnation [122]. [SEP] Justify:

Output: The text contrasts the claim by illustrating the variability in microbiome composition, particularly through the mention of different bacterial genera like Staphylococcus and Lactobacillus associated with health, and the increased presence of potential pathogens due to MG infection. This variety of microbial species and their differing roles in health and disease implies that microbiome compositions are not identical among individuals.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: 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). [SEP] Justify:

Output: The text is irrelevant to the claim regarding olfactory receptors and quantum effects. It discusses the identification and characterization of dopaminergic neurons in the larval brain, without any mention or connection to olfactory receptors, quantum effects, or studies that relate to these topics.


Input: Task: corroborate the claim using the given text. Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [SEP] Text: The presence of a dc-link capacitor in PWM-VSC system provides a decoupling between the two converters, it separates the control between these two converters, allowing compensation of asymmetry of both on the generator side and on the grid side, independently [16]. The second configuration consists of a diode-bridge rectifier, a boost converter and a PWM-VSC connected to the grid. This configuration is, simple, less expensive, robust, and rigid and needs simple control system. But, with this configuration the control of the generator power factor is not possible, which in turn, affects generator efficiency. Also, high harmonic distortion currents are obtained in the generator that reduce efficiency and produce torque oscillations [22]. Figure 1. Wind energy conversion system based on a synchronous generator [26]. Wind turbine converts the wind power to a mechanical power, which in turn, runs a generator to generate electrical power. The mechanical power generated by wind turbine can be expressed as [15]:Pm =12CP(λ,β) ρ A u3E1whereCp: Turbine power coefficient. ρ: Air density (kg/m3). A: Turbine sweeping area (m2). u: wind speed (m/s). λ: tip speed ratio of the wind turbine which is given by the following equation [1];λ =rm ωruE2Where rm is the turbine rotor radius, ωris the angular velocity of turbine (rad/s). The turbine power coefficient, Cp, describes the power extraction efficiency of the wind turbine. It is a nonlinear function of both tip speed ratio, λ and the blade pitch angle, β. While its maximum theoretical value is approximately 0. 59, it is practically between 0. 4 and 0. 45 [15]. There are many different versions of fitted equations for Cp made in the literatures. A generic equation has been used to model Cp(λ, β) and based on the modeling turbine characteristics as shown in the following equation [27]:CP(λ,β) =0. 5176(116*1λi−0. 4β−5)e−21λi+0. 0068λE3With 1λi=1λ+0. 08β−0. [SEP] Justify:

Output: The text corroborates the claim by explaining that a wind turbine converts wind power to mechanical power, which then runs a generator to generate electrical power. This directly supports the claim that wind turbines generate power by converting kinetic energy from wind into electricity.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: non-invasive sampling), storage and processing protocols, DNA extraction kits and procedures, and the use of different bioinformatic approaches. Based on the spatial-anatomical microbial diversity hypothesis (described in Sect. 2), each respiratory site in poultry is thought to host a distinct set of microbial species with varying levels of diversity. Therefore, it is challenging to determine which respiratory site accurately represents the core poultry respiratory microbiota. In a study comparing multiple respiratory site samples (choanal swabs, nasal wash, tracheal wash, and lower respiratory lavage), Abundo et al. [44] were able to confirm that tracheal washes and lower respiratory lavage samples afforded the most consistent and reproducible results regarding respiratory microbiota community compositions compared to other respiratory sites. The study also found that bacterial community compositions obtained from nasal washes were inconsistent between experiments. Considering that the nares are in constant interaction with the bird’s external environment, they may thus be predisposed to environmental microbiota contamination. For this reason, Abundo et al. [44] recommends the inclusion of an environmental control group in studies sampling the nasal microbiota. Preliminary data from our laboratory (unpublished data) agree with the findings of Abundo et al. [44]; tracheal and BAL samples were found to be the most consistent sites of URT and LRT regarding bacterial community composition consistency, respectively. Our group has also previously identified the optimal sample size (n = 5 chicken samples) suitable for poultry microbiota research [138]. Another important consideration is the choice of using invasive or non-invasive sampling techniques. While invasive sampling techniques yield high microbial biomass from euthanized birds, continuous sampling of the same bird is impossible, negating the feasibility of a longitudinal study [9, 25, 33]. In contrast, non-invasive techniques (live bird swabs of various respiratory sites) tend to yield lower bacteria density and are more prone to contamination due to the multiple sample processing steps [44, 139]. In addition to pelleting by centrifugation and other DNA extraction steps common to both invasive and non-invasive samples, live bird swabs (non-invasive samples) involve additional processing steps such as immersing the swabs in a transport/storage media, vortexing to facilitate the elution of organic material, including microbes, and an increased requirement for a higher DNA lysis buffer in the DNA extraction process [44]. [SEP] Justify:

Output: The text contrasts the claim by discussing the unique microbial diversity found in different respiratory sites of poultry, indicating that microbiome compositions can vary significantly. For example, it states that 'each respiratory site in poultry is thought to host a distinct set of microbial species with varying levels of diversity,' which directly refutes the idea of identical microbiome compositions among individuals.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: 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. [SEP] Justify:

Output: The text is unrelated to the claim as it discusses the roles of dopaminergic neurons in larval and adult olfactory learning, focusing on how these neurons regulate aversive and appetitive learning without mentioning olfactory receptors or quantum effects. No reference to olfactory receptor function through quantum effects is present.


Input: Task: corroborate the claim using the given text. Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [SEP] Text: 0351+β3E4The Cp-λ characteristics, for different values of the pitch angle β, are illustrated in Figure 2. The maximum value of Cp is achieved for β = 0 degree and for λopt. The particular value of λ is defined as the optimal value (λopt). Continuous operation of wind turbine at this point guarantees the maximum available power which can be harvested from the available wind at any speed. Figure 2. Aerodynamic power coefficient variation against λ and β. 2. 1. Wind turbine arrangement with back-to-back PWM-VSCsIn this arrangement both the generator and the grid-side converters are PWM-VSCs as shown in Figure 3. The output voltage of the generator is converted into dc voltage through a PWM-VSC. As the previous model the dc-link voltage is converted to constant frequency voltage using grid side PWM-VSC. The dc-link voltage is controlled by the modulation index (ma) and power angle (δ). Controlling the dc-link voltage and the bitch angle of the blades of WT will track the maximum power of WT in the case of variable pitch angle control. In the case of fixed pitch angle control the maximum power extraction is achieved by tracking the optimum shaft speed. The grid side PWM-VSC can be used to enhance the stability of the dc-link voltage and controls the active and reactive power from WT by controlling ma and δ. The generator can be directly controlled by the generator side converter (controller-1) while the grid-side converter (controller-2) maintains the dc-link voltage at the desired value by exporting active power to the grid. Controller-2 also controls the reactive power exchange with the grid [26]. So, the main target of controller-1 is to track the maximum power available from the WTG and the function of controller-2 is to control the dc-link voltage and the reactive power injected to the electric utility. Figure 3. Wind turbine generator with back-to-back PWM-VSCs [26]. 2. 2. Wind turbine arrangement with diode-based rectifierFigure 4 shows the wind turbine with a diode-based rectifier as the generator-side converter. The diode bridge rectifier converts the generator output ac power to dc power and the PWM-VSC converts the dc power from the rectifier output to ac power. [SEP] Justify:

Output: The text corroborates the claim by discussing how wind turbines operate to maximize power extraction from the wind. It mentions the control of blade pitch and optimal shaft speed to achieve maximum available power from the wind. This illustrates the process by which kinetic energy from wind is converted into electrical energy, aligning with the claim.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: In addition to sampling methodology, Abundo et al. [45] showed that the choice of DNA kit could also influence results of respiratory microbiota density and diversity. In order to ensure validity and reproducibility of results, it is recommended that only kits specifically certified for microbiome sample extraction are utilized. Adherence to “Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines” and adapting the “checklist” [140] are other ways of ensuring data reliability across studies. In addition to these guidelines, the field of animal microbiome research is in need of comprehensive and standardized guidelines to facilitate research reproducibility and consistency. Furthermore, Abundo, [141] makes a valid case for the inclusion of positive controls, i. e. , microbial communities with well-defined bacterial composition in microbiome studies. The inclusion of reference standards, whether positive or negative, could help mitigate variations due to sample collection, processing, and sequencing biases. Gut-respiratory microbiota cross talk? Primordially, the gut and the lungs differentiate from the same stem cells [142]. Additionally, microbiota and mucus are essential components of both the gut and respiratory systems, and their interactions with mucosal membranes elicit diverse effects on host health [143,144,145]. The bi-directional communication facilitated by neurochemicals, hormones, immune system cells, and microbiota metabolites between the gut and the respiratory system is now termed the gut-lung axis communication [146, 147]. Gut microbiota dysbiosis affects respiratory sites via mucosal immunostimulation, while respiratory dysbiosis also is understood to influence gut functions via immune regulation [148, 149]. The gut-lung axis communication has been implicated in several human diseases, including inflammatory bowel disease, irritable bowel syndrome, Colitis, and Crohn’s disease [142, 150]. In poultry, evidence of gut-respiratory microbiota cross talk is emerging. In a study involving the microbial evaluation of the URT and gut of 181 layer chickens, Ngunjiri et al. [25] discovered up to 29% of analyzed operational taxonomic unit was shared between these sites. Both sites were dominated by microbes in the order Lactobacillales, Clostridiales, and Enterobacterales. The study reported significant compositional similarity between the URT and the ileum specifically. [SEP] Justify:

Output: The text contrasts the claim by discussing the variability in microbiome composition influenced by different factors such as sampling methodology, the choice of DNA kit, and significant findings from studies demonstrating that microbiota can differ between sites, such as the upper respiratory tract and gut. For instance, the text mentions a study that found up to 29% compositional similarity between microbial communities in the upper respiratory tract and the gut but does not support the notion that microbiome composition is identical for everyone. Instead, it implies that factors such as dysbiosis can lead to differences in microbiome composition across individuals.


Input: Task: pass the claim using the given text. Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: (a) 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). [SEP] Justify:

Output: The text does not discuss olfactory receptors or any connections to quantum effects; instead, it focuses on the neurobiological aspects of dopamine neurons (DAN-c1) and their role in larval olfactory learning. Therefore, it is irrelevant to the claim regarding the potential quantum functioning of olfactory receptors.


Input: Task: corroborate the claim using the given text. Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [SEP] Text: One method to control the operation of the wind turbine with this arrangement (assuming a PMSG) is illustrated in Figure 4. A dc–dc converter is employed to control the dc-link voltage (controller-1), the grid side converter controls the operation of the generator and the power flow to the grid (controller-2). With appropriate control, the generator and turbine speed can be adjusted as wind speed varies so that maximum energy is collected [26]. On the other hand, in most PMSG wind systems, the output voltage of the generator is converted into dc voltage via a full-bridge diode rectifier and this dc voltage is adjusted to control the maximum power of turbine. The grid side converter is controlled by grid injected active and reactive power control method. The ac power output from PMSG is fed to a three-phase diode bridge forward by boost converter to effectively control the dc voltage level through the duty ratio of boost converter. The PWM-VSC is used to interface the WTG with the electrical utility also to track the maximum power available from PMSG. The modulation index of the PWM-VSC is controlled to enhance the stability of the dc link voltage as shown in Figure 4. Figure 4. Wind turbine generator with a diode-based rectifier as the generator-side converter [26]. Advertisement 3. MPPT control strategies for the has been attracting wide attention as a renewable energy source due to depleting fossil fuel reserves and environmental concerns as a direct consequence of using fossil fuel and nuclear energy sources. Wind energy varies continually as wind speed changes throughout the day, even though abundant. of power output from a WECS depends upon the accuracy of tracking the peak power points using the MPPT controller irrespective of the generator type used. The maximum power extraction algorithms can be classified into two categories. The two categories are MPPT algorithms with wind speed sensor and MPPT algorithms without wind speed sensor (sensor-less MPPT controller). These two algorithms have been discussed in the following sections. 3. 1. MPPT algorithms for a WT with wind speed sensor3. 1. 1. Ratio (TSR) techniqueThe TSR control method regulates the rotational speed of the generator to maintain an optimal TSR at which power extracted is maximum [13]. The target optimum power extracted from wind turbine can be written as [14]:Pmax =Kopt *ωopt3E5WhereKopt =0. [SEP] Justify:

Output: The text corroborates the claim by discussing the operation of wind turbines and how they interact with control systems to maximize energy extraction from wind. It describes the conversion processes utilized in a wind turbine system, particularly focusing on the generator output and the control of power flow, which is essential for converting the kinetic energy of wind into electrical energy. Keywords such as 'maximum energy is collected' directly support the claim that wind turbines generate power through kinetic energy conversion.