Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Conversely, the lower respiratory tract (LRT) comprises the syrinx, air sacs, bronchioles, and lungs [16]. Contrary to the prevailing microbial sterility dogma, there is now evidence for microbial colonization of the avian respiratory tracts [17, 18]. In view of this knowledge, the question of the origin of pioneer colonizers of the respiratory tract arises. Microbial colonization of the respiratory tract is a complex process influenced by host-related intrinsic factors and environmental extrinsic factors. Nonetheless, the pioneer colonizers of the poultry respiratory tract are hypothesized to be maternal-dependent. This hypothesis has been demonstrated in humans via a metagenomic neonatal study that showed differences in infant-mother gut microbiota resemblance based on delivery method [19]. Gantois et al. [20] exposition on the pathogenesis of Salmonella contamination in poultry support this hypothesis in poultry. It is reported that microbial seeding could occur via horizontal transmission during or after oviposition in the hen or vertically through the pores on eggshell membranes or eggshells. An association between maternal oviduct microbiota, yolk microbiota, and hatched chick microbiota has also been confirmed [21, 22]. Notwithstanding, well-designed longitudinal studies are needed to provide more insight on the maternal-origin hypothesis on microbial pioneer colonization in poultry, especially taking into cognizance modern poultry production systems that limit maternal hen-egg contact. et al. [23] made the first attempt to enumerate poultry respiratory bacterial communities, the findings of this study have limited practical use as a result of the limitations of the culture-dependent method that was employed. The study by Mulholland and colleagues [17] was the first study to adopt 16S rRNA amplicon sequencing methodology to provide a bacteria census of the poultry respiratory tract, specifically of the trachea at hatch. Data from this study affords the opportunity to speculate on the pioneer colonizers of the poultry respiratory tract. Accordingly, Fig. 1 presents information on the pioneer genera of the avian trachea. The avian trachea is seen to be predominantly (62%) dominated by the genera Pseudomonas, Brevibacterium, Streptococcus, Chryseobacterium, Bacillaceae, and Corynebacteriaceae at hatch. Using two- and seven-day-old chickens, Glendinning et al.

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: (8) We also applied this over-excitation to activate MBNs. As MBN takes over both appetitive and aversive learnings, over-excitation of MBNs led to deficit in both types of learning, which follows our hypothesis (Figure 6). In summary, we hypothesized that DAN-c1 restricts DA release via activation of D2R, which is important for larval aversive learning. D2R knockdown or artificial activation of DAN-c1 during training would induce elevated DA release, leading to over-excitation of MBNs and failure of aversive learning. Weakness #7: The authors should not necessarily expect that D2R enhancer driver strains would reflect D2R endogenous expression, since it is known that TH-GAL4 does not label p(PAM) dopaminergic neurons. Just like the example of TH-GAL4, it is possible that the D2R driver strains may partially reflect the expression pattern of endogenous D2R in larval brains. When we crossed the D2R driver strains with the GFP-tagged D2R strain, however, we observed co-localization in DM1 and DL2b dopaminergic neurons, as well as in mushroom body neurons (Figure S3 c to h). In addition, D2R knockdown with D2R-miR directly supported that the GFP-tagged D2R strain reflected the expression pattern of endogenous D2R (Figure 4b to d, signals were reduced in DM1). In summary, we think the D2R driver strains supported the expression pattern we observed from the GFP-tagged D2R strain, especially in DM1 DANs. Weakness #8: Their observations of GFP-tagged D2R expression could be strengthened with an anti-D2R antibody such as that used by Lam et al. , (1999) or Love et al. , (2023). Love et al. , (2023) used the antibody from Draper et al. 10. We have tried the same antibody, but we were not able to observe clear signals after staining. Maybe it is not specific for the neurons in the fly larval brain, or our staining protocol did not fit with this antibody. Unfortunately, we were not able to find Lam (1999) paper.

Output: pass


Input: Claim: 3D printing is used to create custom prosthetics and implants tailored to individual patients. [SEP] Text: 5Model preparation continued: a) virtual jaw models in occlusion; b) virtual teeth added; c) prospective abutments have been placed in places corresponding to future teeth; d) future abutments – sky blue abutments belong to subperiosteal implant and red one belongs to planned screw type implant; e) a cut-off tool placed in the area of tooth 24 (FDI), where abutment was planned to bury deeper into the bone; f) maxilla with cut-off and placed abutmentsFull size imageIn addition, consider the lateral component of jaw movements—create anatomical and mechanical means of implant fixation. When creating fixed cementable abutments, pay attention to parallelism of prospective abutments with current partnering abutments (teeth or implants) and the relation to opposing dentition (antagonists). The modeling process of a custom dental implant per se is the de novo creation process. This is in contrast to reconstructive surgery modeling, which often uses contra-lateral mirroring to obtain correct anatomical structures. Thus, new objects of intricate shape must be created, which in turn must closely adapt to current anatomical structures. This task requires suitable digital tools. Modeling for medical applications is still a new and emerging field of digital design. There is a range of software built for the dental prosthetic field and numerous programs for implant planning and surgical guide creation. Although some of these programs offer planing osteotomies and bone grafting, the majority are predominantly limited to use of standard screw type implants from different manufacturers. Consequently, they are not useful for modeling custom structures. Numerous software packages have been created for making fixed and removable dental prostheses via CAD/CAM. It appeared that the most straightforward route would be to use applications designed for modeling a partial removable dentures because of the similarity of subperiosteal implants and the framework of partial removable denture. However, after testing several software packages, this route proved to be unusable because the programs do not allow jaw model modification, are limited to preset structures, or require heavy customization to be useful. 3D modeling toolsDepending on software capabilities and designer approach, subperiosteal dental implants can be created using several different tools (techniques). The applicable techniques and corresponding programs are listed below:° Digital clay modeling. ▪ Sculpt clay with brush tool (Meshmixer, Blender, FreeForm).

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: [24] and Ngunjiri et al. [25] have also reported that genera Pseudomonas, Staphylococcus, Actinobacillus, and Lactobacillus species (L. reuteri, L. gasseri, and L. crispatus) are initial colonizers of the chicken nasal, tracheal, and Broncho-(BAL) samples. At the phylum taxa, Mulholland et al. [17] report that the avian trachea is majorly colonized by four phyla at hatch, specifically Gram-positive bacteria Firmicutes (35%) and Actinobacteria (29%), Gram-negative bacteria (Proteobacteria (19%), and Bacteroidetes (9%). Although with varying order of abundance, the same bacterial phyla have been reported as pioneer colonizers of the avian gut [26, 27]. While the effects of the hatchery environment and contaminants on potential colonizers of the respiratory tract should be acknowledged, these results suggest that maternal influence plays a significant role in shaping the pioneer microbial community of the avian respiratory tract. Fig. 1Adapted from Glendinning et al. (2017) and Mulholland et al. (2021)Dynamics of microbiota colonization and succession in the avian trachea and lungs. At hatch, the avian trachea (representative of the upper respiratory tract-URT) was colonized predominantly by Pseudomonas (13%), Brevibacterium (9%), Brachybacterium (8%), Streptococcus (8%), Chryseobacterium (7%), Bacillaceae (6. 5%), and Corynebacteriaceae (6%). By week 3, the tracheal microbiota shifted, with reductions in Brevibacterium (6%), Bacillaceae (4. 2%), and Brachybacterium (5%). emerged dominantly, constituting 34. 1% of the trachea. In contrast, the lung microbiota (representative of the lower respiratory tract-LRT) displayed distinct colonization. Initially, Staphylococcus (25%), Pseudomonas (16%), Achromobacter (15%), and Lactobacillus (12%) were predominant at hatch.

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: Weakness #9: Finally, the authors could consider the possibility other DANs may also mediate aversive learning via D2R. Knockdown of D2R in DAN-g1 appears to cause a defect in aversive quinine learning compared with its genetic control (Figure S4e). It is unclear why the same genetic control has unexpectedly poor aversive quinine learning after training with propionic acid (Figure S5a). The authors could comment on why RNAi knockdown of D2R in DAN-g1 does not similarly impair aversive quinine learning (Figure S5b). We also think that other DANs may be involved in aversive learning. We re-analyzed the learning assay data, seemingly D2R knockdown in DAN-g1 with miR partially affected aversive learning when trained with pentyl acetate (Figure S4e). We are going to build single statistic panels for DAN-g1 and DAN-d1. However, neither larvae with D2R knockdown in DAN-g1 using miR trained with propionic acid (Figure S5a), nor larvae with D2R knockdown in DAN-g1 using RNAi trained with pentyl acetate (Figure S5b) showing aversive learning deficit. We will add paragraphs about this in both Results and Discussion sections. Reviewer #2 (Public Review): Weakness#1: Is not completely clear how the system DAN-c1, MB neurons and Behavioral performance work. We can be quite sure that DAN-c1;Shits1 were reducing dopamine release and impairing aversive memory (Figure 2h). Similarly, DAN-c1;ChR2 were increasing dopamine release and also impaired aversive memory (Figure 5b). However, is not clear what is happening with DAN-c1;TrpA1 (Figure 2K). In this case the thermos-induction appears to impair the behavioral performance of all three conditions (QUI, DW and SUC) and the behavior is quite distinct from the increase and decrease of dopamine tone (Figure 2h and 5b). The study successfully examined the role of D2R in DAN-c1 and MB neurons in olfactory conditioning.

Output: pass


Input: Claim: 3D printing is used to create custom prosthetics and implants tailored to individual patients. [SEP] Text: ▪ Sculpt clay applying profile along surface curve (). ° Selected surface extrusion (Meshmixer, VRMesh). ° Curve fitting with pipe or tube (VRMesh, Freeform, Sculpt). In this case, modeling was conducted via the addition-subtraction method using the mesh brush sculpting technique. Two copies of a jaw model were used. The the first copy was used to model the implant itself by bulging virtual clay, which is similar to the manner in which a dental technician lays wax on the refractory model for partial production of a removable framework. The second copy was used for subtraction from first—to form the inner (bone facing) surface of the implant. Further detailsModels for the maxilla and mandible should be put as close as possible to centric relation, which makes abutment planning easier (Figs. 4b and c). Placing virtual teeth helps with the planning of future occlusion and abutments (Figs. 5b and c). Firstly, abutments are placed according to the current bone anatomy and future prosthetic plan. Bone quality and anatomic form must be considered while designing the implant. Load bearing and retentive elements should be planned only where at least 0. 8 mm of cortical bone is present. Retentive elements (loops, puds) were placed at specified locations and all above-mentioned elements were merged with one copy of the jaw models using a Boolean addition operation (Figs. 6b and c, Figs. 7a and d). Fig. 6modeling principle: a) original model of the jaw; b) pre-made structures (loops and abutments) added on to virtual model; c) pre-made structures and modelled (bulked) implant merged with original model; d) the 2nd copy of original model (yellow) recalled and matched with model onto which implant was modelled (orange); e) implant – result of Boolean operation, subtraction of yellow model form orange model; f) implant (resultant) model superimposed on the original jaw model. (Screenshots from Meshmixer program)Full size imageFig.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: By week 3, the composition shifted to Pseudomonas (35%), Lactobacillus (28%), and Staphylococcus (18%) as the dominant genera in the avian lungs. Full size imageIn terms of community structure, phyla Firmicutes, Proteobacteria, and Bacteroidetes—and to a lesser extent Actinobacteria and Tenericutes—are consistently reported as the major residents of the poultry respiratory tract [12, 25, 28]. and Bacteroidetes have also been reported as the predominant bacteria in the human colon [29], suggesting an evolutionary trend. phylum comprises more than 200 genera, including important probiotics like Lactobacillus, Bacillus, and Ruminicoccus. Conversely, the predominant genera in the phylum Bacteroidetes, including Bacteroides and Prevotella, are known pathobionts [30]. Of note, high Firmicutes to Bacteroidetes ratios are associated with increased inflammation, fat deposition and obesity in humans [31]. A reduction in the abundance of members of the Firmicutes phylum has also been linked with several pathologies in other animals including pigs, cattle, and horses [32]. At lower taxa, as many as 144 bacterial genera have been identified in the respiratory tract of turkeys including 25 shared genera between turkeys and chickens [9, 33], emphasizing the complexity of the poultry respiratory microbiota. A detailed summary of studies presenting the predominant genera in the poultry respiratory tract is presented in Table 1. The trachea and BAL samples are the most studied sections of the URT and LRT in poultry, respectively. The avian respiratory tract comprises more facultative anaerobes than obligate anaerobes, with genera Lactobacillus and Staphylococcus being ubiquitous residents of both the URT and LRT [17, 33]. Although the genus Lactobacillus predominates the poultry respiratory tract [34], its relative abundance varies depending on which part of the respiratory tract is being examined. genus is known to comprise gram-positive facultative anaerobic bacteria that degrade polysaccharides to yield lactic acid [35]. Lactobacillus species are often used in the poultry industry as probiotics as a result of their beneficial properties [36]. Notwithstanding, Johnson et al.

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: The conclusions are well supported by the data, with the exception of the claim that dopamine release from DAN-c1 is sufficient for aversive learning in the absence of unconditional stimulus (Figure 2K). Alternatively, the authors need to provide a better explanation of this point. Please refer to our response to Weakness #6 of #1. Reviewer #3 (Public Review): Weakness #1: It is a strength of the paper that it analyses the function of dopamine neurons (DANs) at the level of single, identified neurons, and uses tools to address specific dopamine receptors (DopRs), exploiting the unique experimental possibilities available in larval Drosophila as a model system. Indeed, the result of their screening for transgenic drivers covering single or small groups of DANs and their histological characterization provides the community with a very valuable resource. In particular the transgenic driver to cover the DANc1 neuron might turn out useful. However, I wonder in which fraction of the preparations an expression pattern as in Figure 1f/ S1c is observed, and how many preparations the authors have analyzed. Also, given the function of DANs throughout the body, in addition to the expression pattern in the mushroom body region (Figure 1f) and in the central nervous system (Figure S1c) maybe attempts can be made to assess expression from this driver throughout the larval body (same for Dop2R distribution). We thank the reviewer for the positive comments and the suggestions. For the strain R76F02AD; R55C10DBD, we examined 22 third instar larval brains expressing GFP or Syt-GFP and Den-mCherry, all of them clearly labeled DAN-c1. Half of them only labeled DAN-c1, the rest have 1 to 5 weak labeled soma without neurites. Barely 1 or 2 strong labeled cells appear. These non-DAN-c1 neurons are seldom dopaminergic neurons. In VNC, 8 out of 12 do not label cells, 3 have 2-4 strong labeled cells. These data supported that R76F02AD;R55C10DBD exclusively labeled DAN-c1 in 3rd instar larval brains.

Output: pass


Input: Claim: 3D printing is used to create custom prosthetics and implants tailored to individual patients. [SEP] Text: 7modeling in Meshmixer program: a) pre-made structures (loops and abutments) added on to original jaw model; b) implant modeling (bulking) by sculpt tool; c) pre-made structures and modelled (bulked) implant merged with original model; d) the 2nd copy of original model (dark grey) recalled and matched with model onto which implant was modelled (light grey); e) implant – result of Boolean operation (with lots of unwanted shells), f) implant (resultant) model superimposed on the original jaw modelFull size imageDuring the third step, interconnecting structures (straps) are modeled into saddle-like web connections using various brushes of the “” tool in Meshmixer (Figs. 7b and c). Abutments, retention loops, and peddles are connected by straps. The overall structure of the subperiosteal implant is usually designed to be 1. 5–1. 8 mm thick, which is an empirically defined value. During modeling, the implant is shaped slightly thicker by 0. 2–0. 3 mm to allow for later reduction and smoothing of its surface. Reduction is necessary because of the nature of the DMLS process, which results in printed parts with rough and oxidized surfaces. Supports also leave rough surface once removed; thus, extra thickness of metal must be included to enable reduction of the material. Boolean subtractionsubtraction operation is an essential part of custom implant additive modeling that results in a final product. The input for this computation consists of two models. The first model is an anatomical model of the bone and the second is the same anatomical model, but with modeled implant structure bulging from it. When the computer calculates the difference between these models - the resultant structure is the implant (Figs. 6, 7). Final stepsImplant contains small residual artifacts that have the appearance of small snowflake-like structures left behind after the Boolean operation. These small objects appear because of inconsistencies accumulated in virtual models during earlier manipulations. Such structures have to be removed manually and errors corrected using the above-mentioned software (Figs. 7e, 8a and b). Fig.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: [34] have reported a negative correlation for increased Lactobacillus abundance in the cecum and ileum with weight of birds. This result emphasizes the need to be considerate of the type of Lactobacillus species, rather than the genus, when formulating probiotics applications. species in the poultry respiratory tract include Lactobacillus salivarius, Lactobacillus crispatus/acidophilus/gallinarum, Lactobacillus johnsonii/gasseri, Lactobacillus aviarius, and Lactobacillus reuteri [34]. Depending on the metabolic property and oxygen requirement of the bacteria, they can be classified as transient or colonizing species. For example, Lactobacillus aviaries is known as a transient species in the trachea because it is an obligate anaerobe found in relatively lower abundance [34, 37]. Experimental studies have demonstrated that Lactobacillus species exhibit immunostimulatory and antiviral properties [38,39,40]. Additionally, Lactobacillus acidophilus, Bifidobacterium bifidum, and Streptococcus faecalis species have been found to induce natural antibodies against various foreign antigens in chickens [41]. Table 1 A comprehensive summary of studies that present the most prevalent genera observed in the respiratory tract of poultryFull size tableIn addition to the complexity of the poultry respiratory microbiota, inconsistency exists regarding the microbial diversity within various sites in the poultry respiratory tract. One theory is that microbial diversity decreases posteriorly along poultry respiratory sites, from the nasal cavity to the lungs, which has been referred to as the island ecological model in human studies [42, 43]. This proposition is grounded in an ecological framework, suggesting that the nasal cavities serve as a microbiota mainland (source), with the highest richness and evenness of microbial species, acting as a source of microbial population for other respiratory sites (sink) [44]. Several poultry studies [18, 24, 45] are in conformation with this proposition. Interestingly, Abundo et al. [44] reported an exception to this model as BAL samples recorded significantly higher microbial diversity compared to the trachea. This exception is herein referred to as the spatial-anatomical model.

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: For the question about the pattern of R76F02AD; R55C10DBD and the expression pattern of D2R in larval body, it is an interesting question. However, our main focus was on the central nervous system and the learning behaviors in fruit fly larvae, we may investigate this question in the future. Weakness #2: A first major weakness is that the main conclusion of the paper, which pertains to associative memory (last sentence of the abstract, and throughout the manuscript), is not justified by their evidence. Why so? Consider the paradigm in Figure 2g, and the data in Figure 2h (22 degrees, the control condition), where the assay and the experimental rationale used throughout the manuscript are introduced. Different groups of larvae are exposed, for 30min, to an odour paired with either i) quinine solution (red bar), ii) distilled water (yellow bar), or iii) sucrose solution (blue bar); in all cases this is followed by a choice test for the odour on one side and a distilled-water blank on the other side of a testing Petri dish. The authors observe that odour preference is low after odour-quinine pairing, intermediate after odour-water pairing and high after odour-sucrose pairing. The differences in odour preference relative to the odour-water case are interpreted as reflecting odour-quinine aversive associations and odour-sucrose appetitive associations, respectively. However, these differences could just as well reflect non-associative effects of the 30-min quinine or sucrose exposure per se (for a classical discussion of such types of issues see Rescorla 1988, Neurosci, or regarding 1988, Behav Genetics, or with some reference to the original paper by Honjo & Furukubo-Tokunaga 2005, J Neurosci that the authors reference, also Gerber & Stocker 2007, Chem Sens). As it stands, therefore, the current 3-group type of comparison does not allow conclusions about associative learning. We adopted this single odor larval learning paradigm from Honjo’s papers1,2. In these works, Honjo et al. first designed and performed this single odor paradigm for larval olfactory associative learning.

Output: pass


Input: Claim: 3D printing can create patient-specific surgical models for preoperative planning. [SEP] Text: Can 3D surgical planning and patient specific instrumentation reduce hip implant inventory? A prospective study Download PDF Download eeOpen access Published: 23 September 2020 Can 3D surgical planning and patient specific instrumentation reduce hip implant inventory? A prospective study : orcid. org/0000-0002-4212-37411,2, Johann Henckel1, Harry Hothi1,2 & …Alister Hart1,2 Show authors 3D Printing in Medicine 16. 020). The patientsWe recruited a consecutive series of 45 patients undergoing primary THA for osteoarthritis. In the initial group selected for the feasibility part of the study there were 14 males and 11 females (mean age 64. 36 years, range 39–81); in the experimental group of the pilot study there were 9 males and 11 females (mean age 71. 65 years, range 54–79). Table 1. Table 1 Patient demographicsFull size tableAccording to the literature, the accuracy for size component prediction with CT planning is around 90%, achieved with a minimum of 25 patients (alpha level 0. 05, power of 80%) [16, 17]. The number of patients in this study was chosen to provide a reasonable estimate of accuracy as well as reproducibility prior to starting a prospective study [19, 20]. CT scanning protocolAll patients underwent pre-operative CT-scanning of the hip region and the knee joint according to a standard protocol. The scanning protocol is specifically designed to minimize radiation dose, while ensuring a good spatial accuracy. Image acquisition consisted of two short spiral axial scans: one including the whole pelvis (starting at least 2 cm above the iliac crests and continue to at least 10 cm below the lesser trochanter) and the proximal femur; the second including the distal part of the femur in the affected side at least 2 cm above the posterior condyles and extend down to the distal condyles towards the tibia [21]. 3D surgical planning(and Communications in Medicine) data was transferred to the MyHip 3D reconstruction planning software to create a patient specific 3D model of the pelvis and femurs of the subject. The engineers at Medacta planned the position and size of the prosthesis according to the surgeon’s p1. Fig. 1MyHip planning software.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: This model is based on the concept that the unique anatomical and physiological functions of each distinct respiratory site dictate the richness and evenness of its microbial residents. This would imply that constant mucociliary clearance occurring in the trachea contributes to its reduced microbial richness in comparison to LRT sites, where microbial deposition contributes to its higher microbial diversity [46]. Indeed, further studies are needed to clarify whether poultry respiratory sites exhibit a source-sink or unique microbial diversity trend. Influencing factors on poultry respiratory microbiota community composition and diversityThe composition and diversity of the respiratory microbiota in poultry are influenced by various factors. These include intrinsic host-related factors such as bird type, breed, age, and genetics, as well as several extrinsic factors. Extrinsic factors include production system, sampling sites and methods, sequencing platforms, and methodologies (choice of PCR primers, regions to be sequenced, number of PCR cycles, DNA extraction protocols), as well as nutrition, antibiotics use, disease, and environmental stressors such as heat and litter quality. The interplay of these factors contributes to the complexity of the poultry respiratory microbiota. A synthesis of available information on the most decisive determinants of the poultry respiratory microbiota (presented in Fig. 2) are described below:Fig. 2Factors shaping community structure and diversity of poultry respiratory microbiota. The interplay between intrinsic factors (highlighted in orange) and extrinsic factors (highlighted in grey) significantly influences the community structure and diversity of the poultry respiratory microbiota. The respiratory site itself, shaped by distinct physiological functions, determines the microbial residents, resulting in unique bacterial communities across various respiratory sites. Additionally, avian species type governs microbiota composition, notably between chickens and turkeys. Age is a critical factor, with microbial colonization increasing until an age-independent plateau is reached. However, this process is impacted by diverse stressors, environmental conditions, and management practices. Factors such as ammonia pollution, heat stress, antibiotic usage, and pathogen presence, especially viruses, can disrupt microbiota balance, potentially leading to dysbiosis. Understanding these intricate relationships is crucial for a comprehensive comprehension of avian respiratory microbiota dynamicsFull size imageIntrinsic factorsAs highlighted in our description of the spatial-anatomical model of microbial diversity in the respiratory tract, the distinct physiological and adaptive functions of each respiratory site determine its specific microbial residents.

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: To address the reviewer’s question about the potential non-associative effects of the 30-min quinine or sucrose exposure, we would like to defend it primarily based on results from Honjo et al. (2005 and 2009). They applied the odorant to the larvae after training, only the ones had paired training with both odor and unconditioned stimulus (quinine or sucrose) showed learning responses. Larvae exposed 30 min in only odorant or unconditioned stimulus did not show different response to the odor compared to the naïve group1,2. To validate this paradigm induces associative learning responses, they also tested the paradigm from three aspects: (1) The odor responses are associative. Honjo et al. showed only when the odorant paired with unconditioned stimulus would induce corresponding attraction or repulsion of larvae to the odor. Neither odorant alone, unconditioned stimulus alone, nor temporal dissociation of odorant and unconditioned stimulus would induce learning responses. (2) The odor responses are odor specific. When applied a second odorant that was not used for training, larvae only showed learning responses to the unconditioned stimulus paired odor. This result ruled out the explanation of a general olfactory suppression and indicates larvae can discriminate and specifically alter the responses to the odor paired with unconditioned stimulus. Although the two-odor reciprocal training is not used, these results can show the association of unconditioned stimulus and the corresponding paired odor. (3) Well known learning deficit mutants did not show learned responses in this learning paradigm. Honjo et al. tested mutants (e. g. , rut and dnc) showing learning deficits in the adult stage with two odor reciprocal learning paradigm. These mutant larvae also failed to show learning responses tested with the single odor larval learning paradigm. (4) In our study, we used two distinct odorants (pentyl acetate and propionic acid), as well as two D2R knockdown strains (UAS-miR and UAS-RNAi for D2R). We obtained similar results for larvae with D2R knockdown in DAN-c1. In addition, our naïve olfactory, naïve gustatory, and locomotion data ruled out the possibilities that the responses were caused by impaired sensory or motor functions. Comparison with the control group (odor paired with distilled water) ruled out the potential effects if habituation existed.

Output: pass


Input: Claim: 3D printing can create patient-specific surgical models for preoperative planning. [SEP] Text: Examples of landmark selection a on the femur, b pelvis and c knee for the acquisition of the relevant anatomical and functional axes and planesFull size imageOnce the operator has selected all landmarks, he proceeds with adjusting the cut height. During the planning of the stem position and of the femoral head resection, all parameters related to the femoral resection in terms of cut height, cut angle and cut anteversion can be modified according to the surgeons’ planning philosophy. The stem is positioned in terms of AP (anterior/posterior) and ML (medial/lateral) offset from the default position, Fig. 2a. Fig. 2Examples of images from the MyHip software: a Preoperative planning of the femoral component, b anteversion view, c preoperative planning of the acetabular component positioned with 45° of inclination and 15° of anteversion (Lewinnek safe zone)Full size imageThe stem size is chosen to achieve a good fit in the intramedullary canal and head size is selected to adjust the femoral offset. Stem version (Fig. 2b), leg length adjustment, cup positioning can all be adjusted to achieve optimal alignment between the two legs (using the lesser trochanter as a reference). The surgeon defines the acetabular reaming depth and the acetabular angles (anteversion and inclination). Different spatial views are available, the operator can change the size of component among those available, while adjusting the level of fit-and-fill, Fig. 2. The result of the previous steps can be evaluated in the so-called global view and the leg length view. While the first one is focused on the affected side, the second one involves the contralateral side for comparison. This view is used to check the alignment between the two femurs, using the lesser trochanter as reference, Fig. 3. Fig. 3Example of (a) the global view and (b) leg length view after planning. While the first displays the affected side, the second one involves the contralateral side too. This view is used to check the alignment between the two femurs, using the lesser trochanter as reference.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Hence, it is not surprising that the respiratory site under consideration is a factor that potentially influences microbiota composition and diversity. Several poultry studies have reported distinct bacterial communities across different respiratory sites [5, 24, 25, 34]. Sohail et al. [12] also reported distinct spatial bacterial ecology between the ceca and trachea using Polymerase chain reaction/denaturing gradient gel electrophoresis (PCR/DGGE) fingerprinting methodology. Tracheal samples were predominantly dominated by and Oscillospira whereas cecal samples had Aerococcus, Anaerococcus, Globicatella, Lactobacillus, Nosocomiicoccus and Staphylococcus as the prevailing genera in their study. In conformation with results from Sohail et al. [12] study, Kallapura et al. [47] also reported a higher rate of Salmonella recovery in the trachea compared to the ceca. This disparity could possibly be because of the two mucosa milieus, the trachea is the easiest route for microbe entry and colonization. Additionally, longer feed retention time, and lower pH afford the ceca its unique bacterial composition and diversity [48]. Taylor et al. [18] provided unique perspective on the peculiarity of the poultry respiratory tract microbiota, as they report unique microbiota compositions of the avian trachea and nasal cavity. It is speculated that the avian trachea harbors more transient species compared to the nasal cavity. Similar observations have been reported in the human respiratory tract [6, 49]. Furthermore, the type of poultry bird is another factor that reportedly dictates the composition and diversity of the poultry respiratory tract microbiota. Although some bacterial taxa overlap occur, distinct differences in the URT microbiota (trachea) of chickens and turkeys have been reported [9]. Kursa et al. [9] further showed that the chicken trachea harbors significantly greater bacterial diversity compared to the turkey trachea. This difference in respiratory microbiota composition and diversity is speculated to be confounded by the age of the flocks, as the average age of chickens and turkeys from which samples were obtained in the study was 30 and 25 weeks, respectively [9]. The impact of age on the composition and diversity of the microbial communities in the avian respiratory tract has received less attention compared to the gut.

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: All these results supported this single odor learning paradigm is reliable to assess the learning abilities of Drosophila larvae. And the failure of reduction in R. I when larvae with D2R knockdown in DAN-c1 were trained in quinine paired with the odorant is caused by deficit in aversive learning ability. We will add a paragraph to address this in the Discussion part. Weakness #3: A second major weakness is apparent when considering the sketch in Figure 2g and the equation defining the response index (R. I. ) (line 480). The point is that the larvae that are located in the middle zone are not included in the denominator. This can inflate scores and is not appropriate. That is, suppose from a group of 30 animals (line 471) only 1 chooses the odor side and 29, bedazzled after 30-min quinine or sucrose exposure or otherwise confused by a given opto- or thermogenetic treatment, stay in the middle zone. . . a P. I. of 1. 0 would result. It is a good question. We gave 5 min during the testing stage to allow the larvae to wander in the testing plate. Under most conditions, more than half of larvae (>50%) will explore around, and the rest may stay in the middle zone (will not be calculated). We used 25-50 larvae in each learning assay, so finally around 10-30 larvae will locate in two semicircular areas. Indeed, based on our raw data, a R. I. of 1 seldom appears. Most of the R. I. s fall into a region from -0. 2 to 0. 8. We should admit that the calculation equation of R. I. is not linear, so it would be sharper (change steeply) when it approaching to -1 and 1. However, as most of the values fall into the region from -0. 2 to 0. 8, we think ‘border effects’ can be neglected if we have enough numbers of larvae in the calculation (10-30). Weakness #4: Unless experimentally demonstrated, claims that the thermogenetic effector shibire/ts reduces dopamine release from DANs are questionable.

Output: pass


Input: Claim: 3D printing can create patient-specific surgical models for preoperative planning. [SEP] Text: The red model (arrow) represents the pre-op position and the green is the post-op oneFull size imageA single, cementless implant system was used (Quadra-H System femoral components and acetabular components, Medacta, Switzerland). Surgical guides and anatomical models were produced by Sintering technology (SLS). PA 2200, a type of nylon that is a special moulding material developed by German EOS for their SLS rapid prototyping equipment, was used for 3D printing. (EOS, Munich, Germany). The components were sterilised either using gamma irradiation and delivered to our hospital or autoclaved at the hospital before intra-operative use. Intra-operative assessment and use of PSIFor each patient, the planned sizes of acetabular and femoral components were recorded and compared to the actual sizes used at surgery. Complications such as intra-operative femoral fracture was also recorded. During surgery, a patient-specific instrument guide was used to position the cup and cut the femoral neck, Fig. 4. Fig. 4The acetabular and femoral PSI guides. The acetabular guide is seated into the acetabulum, and two pins are inserted through attached drill sleeves. The guide is removed, leaving the two pins to act as either a constrained or unconstrained guide to reaming and component placement (a). The femoral guide has a contoured fit to the femoral neck/head and is kept in place for the neck cut by two intraosseous pins (b) [13]Full size imageThe acetabular guide was 3D printed to match the acetabular rim. Once seated into the acetabulum two pins were inserted through attached drill sleeves. The guide was then removed, leaving the two pins to act as either a constrained or unconstrained guide to reaming and component placement. The femoral PSI guide was 3D printed to fit the contours of the femoral head-neck junction (ie, no cartilage present at this junction). Once seated, it was secured with two threaded pins. The femoral neck cut was then completed using a standard method, with the saw blade flush on the cutting surface of the guide to deliver a femoral cut at the planned angle and location.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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).

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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].

Output: contrast


Input: 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).

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


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

Output: pass


Input: Claim: 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.

Output: corroborate


Input: 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.

Output: contrast


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

Output: pass


Input: Claim: 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.

Output: corroborate


Input: 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].

Output: contrast


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

Output: pass


Input: Claim: 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.

Output: corroborate


Input: 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.

Output: contrast


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

Output: pass


Input: Claim: 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].

Output: corroborate


Input: 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.

Output: contrast


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

Output: pass