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

Output: corroborate


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

Output: contrast


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

Output: pass


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

Output: corroborate


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

Output: contrast


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

Output: pass


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

Output: corroborate


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

Output: contrast


Input: Claim: Some studies suggest that olfactory receptors may function through quantum effects. [SEP] Text: (a) Dendrites and axons of DAN-c1 are labeled by DenMark and sytGFP, correspondingly. (b-e) The innervation patterns of DAN-c1. DAN-c1 is labeled with GFP, the MB with RFP, and DANs with TH antibody (blue color). Only one DAN soma is identified (c), axons from DAN-c1 innervate the lower peduncle (LP) of the MB (d), and synapses from DAN-c1 to the LP of the MB are shown by GRASP (e). A schematic diagram (f) shows the innervation patterns of DAN-c1. Modified from Eichler et al. 46, and Saumweber et. al. 28. (g) A schematic paradigm for larval olfactory learning (top) and two different training paradigms for thermogenetics (bottom). (h-j) Blocking dopamine release from DAN-c1 during learning using shibirets1 strain under 34°C impairs larval aversive learning. (k-l) Activation of DAN-c1 with dTRTPA1 under 34°C induces aversive learning. QUI, quinine. DW, distill water. SUC, sucrose. Data are shown as mean ± SEM. Two-way multiple comparisons test, *p < 0. 05, **p < 0. 01, ***p < 0. 001, ****p < 0. 0001. numbers, see Table S2. Scale bars: 50 µm (a and b), 20 µm (c, d and e). To reveal the role of DAN-c1 in larval olfactory learning, a single odor learning paradigm and thermogenetic tools were applied17,18,63. Compared to those trained with distilled water (DW), control larvae exhibited repulsion to the odorant pentyl acetate (PA) after being trained with quinine (QUI) paired with PA, reflecting aversive learning. In contrast, larvae were attracted to PA after being trained with sucrose (SUC) paired with PA, reflecting appetitive learning. The extent of repulsion or attraction was represented with a response index (R. I. ) that is compared to the DW group (Figure 2g).

Output: pass


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

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Possible rationale for these microbial interactions across both sites may include aerosolization of fecal bacteria, gastroesophageal reflux, or systemic circulation of gut-derived microbial metabolites [25, 151]. Similar microbial overlap has been reported in turkeys and broiler chickens [18, 34]. et al. [34] showed that the tracheal microbiota slightly mirrors that of the gut in broiler chickens, Taylor et al. [18] speculated that bacteria in the gut migrate to the respiratory tracts via aerosolization. To further validate this cross talk between the gastrointestinal tract and the respiratory system, respiratory microbiota dysbiosis has also been shown to be capable of potentially inducing gut microbiota dysbiosis. For example, inhaled ammonia was found to induce tracheal injury and tracheal microbiota dysbiosis in ammonia-challenged broiler chickens [152]. This ammonia exposure subsequently triggered gut microbiota dysbiosis via direct and indirect mechanisms including gut leakage and Toll-like receptor signaling pathways [152]. In addition to ammonia exposure, other stressors including heat stress have been reported to perturb the microbial community structure and diversity in the intestinal and respiratory sites concurrently [10]. These results emphasize the potential role of microbial cross talk in poultry susceptibility and resilience to environmental stressors, and the need for additional studies to provide more insights on the microbial relationship between these sites. Potential implications for poultry healthThe potential for the gut microbiome to influence microbial compositions and functions in other tissues, particularly respiratory sites via the gut-lung axis, presents the opportunity to develop therapeutic interventions in poultry for the gastrointestinal tract microbiota that extend beyond the gut [153]. Although the mechanism of action is yet to be fully delineated, several studies have affirmed the contribution of gut microbiota in immune responses to respiratory infections in poultry. Using an antibiotic-mediated microbiota-depletion protocol, Peng et al. [130] showed that gut microbiota depletion increased the severity of colibacillosis induced by intratracheal inoculation of APEC in chickens. The results showed increased production of proinflammatory cytokines IL-1β and IL-6, both at transcript and protein levels, in addition to significant pathological damage to the lungs in gut microbiota-depleted chickens compared to the control group. Conversely, acetate, a microbiota metabolite, was found to inhibit lung pathological injury, bacterial load, and inflammatory cytokine production in chickens [130].

Output: contrast


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

Output: pass


Input: Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [SEP] Text: 5*ρ A *(rmλopt)3*CP−max, and ωopt = λoptrm*uThe power for a certain wind speed is maximum at a certain value of rotational speed called optimum rotational speed,ωopt. This optimum rotational speed corresponds to optimum tip speed ratio, λopt. In order to track maximum possible power, the turbine should always operate at λopt. This is achieved by controlling the rotational speed of the WT so that it always rotates at the optimum rotational speed. As shown in Figure 5, for TSR calculation, both the wind speed and turbine speed need to be measured, and the optimal TSR must be given to the controller. The first barrier to implement TSR control is the wind speed measurement, which adds to system cost and presents difficulties in practical implementations. The second barrier is the need to obtain the optimal value of TSR, this value is different from one system to another. This depends on the turbine-generator characteristics results in custom-designed control software tailored for individual wind turbines [14]. Figure 5. The block diagram of the tip speed ratio control of WECS [15]. 3. 1. 2. Feedback (PSF) controlIn PSF control [14], it is required to have the knowledge of the wind turbine’s maximum power curve, and track this curve through its control mechanisms. The maximum power curves need to be obtained via simulations or off-line experiment on individual wind turbines or from the datasheet of WT which makes it difficult to implement with accuracy in practical applications. In this method, reference power is generated using a maximum power data curve or using the mechanical power equation of the wind turbine where wind speed or the rotational speed is used as the input. Figure 6 shows the block diagram of a WECS with PSF controller for maximum power extraction. The PSF control block generates the optimal power command Popt which is then applied to the grid side converter control system for maximum power extraction as follow [15]: Popt =Kopt *ωr3E6The actual power output, Pt is compared to the optimal power, Popt and any mismatch is used by the fuzzy logic controller to change the modulation index of the grid side converter, PWM-VSC as shown in Figure 6.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Gut microbiota-derived acetate has also been found effective against severe respiratory syncytial virus and AIV infection in mice [154, 155]. In conformation with this result, Yitbarek, et al. [71, 87] utilized antibiotic-mediated microbiota-depletion protocols to demonstrate that the commensal microbiota in the gut play a critical role in the infection and replication of H9N2 AIV in chickens. In both studies, microbiota depletion in the gut resulted in higher virus shedding compared to chickens with intact microbiota. Additionally, gut microbiota depletion triggered downregulation of type-I IFN expression in the gut and respiratory sites [71, 87]. Similarly, antibiotic-treated ducks demonstrated increased shedding of H5N9 HPAI virus and reduced antiviral immune responses in the gut [115]. The role of the gut microbiota in influencing immune responses in distant organs, such as the respiratory tract, has also been supported by numerous studies involving germ-free or antibiotic-depleted mice [116, 150, 154, 156]. The findings from these studies and models clearly demonstrate that depletion of the gut microbiota compromises immune responses against respiratory infections. Metagenomic sequencing has thus far provided a good understanding of commensal and pathogenic bacterial species associated with the reported immunosuppression. Nevertheless, there is a need for broader exploration of other microbial residents in the gut and respiratory tract, including fungi, parasites, and viruses, and their association with immune responses in poultry. Adopting a metabolomics approach would offer a better understanding of the roles and metabolic pathways associated with these microbes, going beyond a simple microbial census. This approach could facilitate the development and validation of important therapeutic metabolites and biomarkers for economically important poultry diseases.

Output: contrast


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

Output: pass


Input: Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [SEP] Text: The PWM-VSC is used to interface the WT with the electrical utility and will be controlled through the power angle, δ and modulation index, ma to control the active and reactive power output from the WTG [15]. Figure 6. The block diagram of power signal feedback control [15]. 3. 1. 3. Optimal torque control The aim of the torque controller is to optimize the efficiency of wind energy capture in a wide range of wind velocities, keeping the power generated by the machine equal to the optimal defined value. It can be observed from the block diagram represented in Figure 7, that the idea of this method is to adjust the PMSG torque according to the optimal reference torque of the wind turbine at a given wind speed. A typical wind turbine characteristic with the optimal torque-speed curve plotted to intersect the CP-max points for each wind speed is illustrated in Figure 8. The curve Topt defines the optimal torque of the device (i. e. maximum energy capture), and the control objective is to keep the turbine on this curve as the wind speed varies. For any wind speed, the MPPT device imposes a torque reference able to extract the maximum power. The curve Topt is defined by [26]:Topt =Kopt *ω2optE7=0. 5*ρ A *(rmλopt)3*CP−maxE8Figure 7. The block diagram of optimal torque control MPPT method. Figure 8. Wind turbine characteristic for maximum power extraction [26]. 3. 1. 4. Load angle control The load angle control can be explained by analyzing the transfer of active and reactive power between two sources connected by an inductive reactance as shown in Figure 9. The active power, PS, and reactive power, QS, transferred from the sending-end to the receiving-end can be calculated from the following equation [26]:Ps=Vs VRXgen sinδE9Qs=Vs2Xgen−Vs VRXgenE10Figure 9. Load angle control of the generator-side converter [26]. 3. 1. 4. 1. Load angle control of the generator-side converterThe operation of the generator and the power transferred to the dc-link are controlled by adjusting the magnitude and angle of the voltage at the ac terminals of the generator-side converter.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: A principal component (PCA) analysis based on the relative abundance 16S rRNA gene OTUs showed that the oxic sediment samples from both the field and at the end of the experimental incubations were tightly clustered (Fig. 2). In contrast, the samples from the anoxic and intermediate sediment cores were more dispersed after the re-oxygenation treatment. Alpha diversity calculations on all OTUs using Shannon’s H index showed a significantly higher diversity in the oxic samples (9. 56 ± 0. 11, n = 7), compared to intermediate (8. 97 ± 0. 28, n = 8) and anoxic (8. 75 ± 0. 32, n = 7) samples (one-way ANOVA; oxic and intermediate F = 22. 63, p < 0. 01, and oxic and anoxic samples F = 26. 72, p < 0. 01; also significantly different with non-parametric Kruskal-Wallis tests (p < 0. 01)). Levin’s niche width (B) index was used to analyze the OTU uniformity of distribution between sites, with high values indicating habitat generalists while a low B indicates habitat specialists. All values suggested a majority of generalists although the intermediate sediment samples showed statistically significant higher values (B = 8. 11 ± 0. 27, n = 9) compared to the oxic samples (7. 67 ± 0. 28, one-way = 7. 85, p < 0. 05, n = 7). The anoxic sediment samples had a B value of 7. 88 ± 0. 31 (n = 7), and the differences were non-significant with the other groups. Fig. 2Principal component analysis of the OTU communities in the top 1-cm sediment layer. Symbols: gray circles, oxic field; black circles, oxic control; white circles, oxic-to-anoxic; gray squares, anoxic field; black squarse, anoxic control; white squares, anoxic-to-oxic; gray diamonds, intermediate field; white diamonds, intermediate-to-oxic; and black diamonds, intermediate-to-anoxicFull size image The dominant taxa in the microbial communities from the oxic, intermediate, and anoxic sediments were affiliated with the Delta-, Epsilon-, and Gammaproteobacteria plus Bacteroidetes (Fig. 3).

Output: contrast


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

Output: pass


Input: Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [SEP] Text: With the proposed algorithm for maximum power extraction, only voltage and current at the inverter terminal need to be measured, and no information about the wind and generator speeds and the dc-link voltage is required [1]. Figure 18. Wind turbine output power versus speed [1]. ΔPout Δ(Vinvma) Decision > 0 > 0 Increase δ < 0 < 0 Decrease δ > 0 < 0 No change < 0 > 0 No change Table 1. Maximum power tracking algorithm [1]. B. Reactive powerThe inverter should be able to regulate its output reactive power to provide the reactive power demand of the utility system, Figure 17. The inverter output reactive power must be controlled so as the maximum real power extraction is not violated. The real and reactive power components (Pout, Qout ) at the inverter output terminals are [1]:Pout=Vinv VsysXTsin(δ)E24Qout=Vinv VsysXTcos(δ)−(Vsys)2XTE25Vsys is the utility system voltage and XT is the reactance between the inverter and the utility system. It is seen from Equation (22) and Equation (23) that Vinv and δ can be controlled so as Qout is regulated at a desired value while Pout is kept constant at its maximum corresponding to the wind speed. in Equation (22) by 322 ma Vd, This equation can be deduced as follow:Pout=3 ma Vdc Vsys22XTsin(δ)E26Assuming that Vdc does not change over the small sampling period T, Pout corresponding to the sampling time nT and (n +1)T isPout(n)=3 ma(n) Vdc Vsys22 XTsin(δn)E27Pout(n+1)=3 ma(n+1) Vdc Vsys22 XTsin(δn+1)E28To keep the real power constant, i. e. , Pout (n+1) = Pout (n), while providing a desired reactive power, Equation (23), the inverter voltage angle must satisfy the following condition.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: 5952 OTUs (constituting 95. 9% of the total) in the sediment phases at the end of the experiment were relatively stable compared to the field sediments (i. e. , changed <0. 1% in relative abundance). In contrast, more than a 0. 1% increase or decrease in relative abundance of OTUs between the field sediments and the end of the incubations was recorded for 246 OTUs. These changing OTUs represented 47–61% reads in the initially anoxic sediment, 52–57% in the intermediate, and 22–25% in the initially oxic sediment. The changing OTUs primarily belonged to the Sulfurimonas and Sulfurovum genera (Epsilonproteobacteria), but also included, e. g. , Desulfobacula, Spirochaeta, and unclassified OTUs (Additional file 5: Figure S2). Consistent with the tightly clustered oxic samples in the PCA (Fig. 2), the relative abundance of the dominant OTUs in the cores from the oxic field samples and after incubations had ≤2-fold difference between all the treatments. These dominant populations included OTUs affiliated with the genera Desulfobulbus, Nitrosomonadaceae, Anaerolineaceae, and Gemmatimonadaceae (Additional file 5: Figure S2 and Additional file 6: Data S1). In the sediment cores from the intermediate site, there was a stronger microbial community shift compared to the oxic cores, with Sulfurimonas-like OTUs increasing from <1% in the field samples to a relative abundance of 14. 4 and 8. 4% in the intermediate-to-oxic and intermediate-to-anoxic cores, respectively (Fig. 3 and Additional file 6: Data S1). Dominant individual Sulfurimonas-like OTUs in the intermediate-to-oxic cores increased up to 374-fold (Additional file 6: Data S1). In addition, Sulfurovum-like OTUs increased from <1% in the field to a relative abundance of 2. 2 and 4. 8% in the intermediate-to-oxic and intermediate-to-anoxic cores, respectively (Fig. 3), with one dominant Sulfurovum-like OTU in the intermediate-to-anoxic increasing 365-fold (Additional file 6: Data S1).

Output: contrast


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

Output: pass


Input: Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [SEP] Text: MPPT control algorithm has been introduced using FLC to regulate the rotational speed to force the PMSG to work around its maximum power point in speeds below rated speeds and to produce the rated power in wind speed higher than the rated wind speed of the WT. Indirect vector-controlled PMSG system has been used for this purpose. The input to FLC is two real time measurements which are the change of output power and rotational speed between two consequent iterations (ΔP, and Δωm). The output from FLC is the required change in the rotational speed Δωm-new*. The detailed logic behind the new proposed technique is explained in details in the following sections. Two effective computer simulation software packages (PSIM and Simulink) have been used to carry out the simulation effectively where PSIM contains the power circuit of the WECS and Matlab/Simulink contains the control circuit of the system. The idea behind using these two different software packages is the effective tools provided with PSIM for power circuit and the effective tools in Simulink for control circuit and FLC. Figure 21. Schematic diagram of the overall system. 4. 1. Wind energy conversion system description Figure 22 shows a co-simulation (PSIM/Simulink) program for interconnecting WECS to electric utility. The PSIM program contains the power circuit of the WECS and Matlab/Simulink program contains the control of this system. The interconnection between PSIM and Matlab/Simulink has been done via the SimCoupler block. The basic topology of the power circuit which has PMSG driven wind turbine connected to the utility grid through the ac-dc-ac conversion system is shown in Figure 21. The PMSG is connected to the grid through back–to-back bidirectional PWM voltage source converters VSC. The generator side converter is used as a rectifier, while the grid side converter is used as an inverter. The generator side converter is connected to the grid side converter through dc-link capacitor. The control of the overall system has been done through the generator side converter and the grid side converter. MPPT algorithm has been achieved through controlling the generator side converter using FLC. The grid-side converter controller maintains the dc-link voltage at the desired value by exporting active power to the grid and it controls the reactive power exchange with the grid. Figure 22.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: In the sediment cores from the anoxic site, the relative abundance of Sulfurimonas-like OTUs increased from 4. 5% in the anoxic field sample to 18. 4% in the anoxic control and 13. 8% in the anoxic-to-oxic cores (Fig. 3). Dominant single Sulfurimonas-like OTUs increased up to 414-fold in the anoxic control and up to 565-fold in the anoxic-to-oxic sediment (Additional file 6: Data S1). In contrast, the Sulfurovum-like OTUs decreased from 0. 4% in the field samples to 0. 2% in the anoxic control while increasing strongly to 19. 0% in the anoxic-to-oxic treatment (Fig. 3 and Additional file 6: Data S1). The additional experiment conducted during May 2014 showed similar results regarding Sulfurimonas- and Sulfurovum-like OTUs when exposed to oxygen or maintained anoxic (Additional file 7: Figure S3). Fig. 3Stacked bar graphs of microbial community composition from the start and the end of the incubation experiment. The partial 16S rRNA gene amplicons show relative abundance (%) of counts. Taxonomic affiliation was linked to the metagenome by extracting 16S rRNA gene sequences from the metagenome dataset (relative abundance (%) of counts is shown). The community composition identified based on the metatranscriptome was derived from the taxonomy affiliated with annotated genes in the metatranscriptomic dataset (proportion (%) of FPKM). are divided into classes and Epsilonproteobacteria has been further divided into Sulfurimonas, Sulfurovum, and other size image Extracted 16S rRNA gene sequences from the metagenomic dataset at the end of the incubation showed a higher relative abundance of archaea in the sediment surface (up to 5% compared to 0. 2% in the 16S amplicon data), possibly due to the primers used for PCR selecting against archaea [37]. The annotated metatranscriptomic dataset showed that archaea were active in the sediment at the end of the incubation and were represented up to 26% of the annotated data (proportion (%) of FPKM; Fig. 3).

Output: contrast


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

Output: pass


Input: Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [SEP] Text: Co-simulation block of wind energy system interfaced to electric utility. 4. 1. 1. Wind turbine model Wind turbine converts the wind power to a mechanical power. This mechanical power generated by wind turbine at the shaft of the generator can be expressed as:Pm =12CP(λ,β) ρ A u3E30where ρ is the air density (typically 1. 225 kg/m3), β is the pitch angle (in degree), A is the area swept by the rotor blades (in m2); u is the wind speed (in m/s) and Cp(λ, β) is the wind-turbine power coefficient (dimensionless). The turbine power coefficient, Cp(λ, β), describes the power extraction efficiency of the WT and is defined as the ratio between the mechanical power available at the turbine shaft and the power available in wind. A generic equation shown later in Equation (3) is used to model Cp(λ, β). CP is a nonlinear function of both tip speed ratio, λ and the blade pitch angle, β. The tip speed ratio, λ is the ratio of the turbine tip speed, ωm*R to the wind speed, u. This tip speed ratio, λ, is defined as [28]:λ=ωm *RuE31Where ωm is the rotational speed and R is the turbine blade radius, respectively. For a fixed pitch angle, β, CP becomes a nonlinear function of tip speed ratio, λ, only. According to Equation (29), there is a relation between the tip speed ratio, λ and the rotational speed, ωm. Hence, at a certain wind speed, the power is maximum at a certain ωm called optimum rotational speed, ωopt. This speed corresponds to optimum TSR, λopt [15]. The value of the TSR is constant for all maximum power points. So, to extract maximum power at variable wind speed, the WT should always operate at λopt in speeds bellow the rated speed. This occurs by controlling the rotational speed of the WT to be equal to the optimum rotational speed. Figure 23 shows that the mechanical power generated by WT at the shaft of the generator as a function of the rotational speed, ωm.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Sulfurimonas-like and Sulfurovum-like OTUs were present in the metagenomic dataset with the highest abundance in the anoxic-to-oxic sediment with 7. 6 and 16. 1%, respectively. However, these two Epsilonproteobacteria genera did not represent more than 1. 4% in the metatranscriptome data. Instead, Gammaproteobacteria in the sediment had a proportion up to 36% in the metatranscriptome data at the end of the experiment (Fig. 3). To investigate changes in microbial community composition due to chemical fluxes from the sediment surface, we also analyzed OTU data in the water phase of the incubated sediment cores. Microbial communities dominant in the water phase consisted of Actinobacteria (Fig. 3) (which is typical for the [56]), with abundant OTUs primarily belonging to the orders Acidimicrobiales, Corynebacteriales, and Frankiales (Additional file 6: Data S1). Other dominant groups in the water phase were Alpha-, Beta-, Epsilon-, Gammaproteobacteria, and Bacteroidetes, along with unclassified OTUs (Fig. 3). In comparison to the sediment community, the water phase contained significantly higher relative abundance of Alphaproteobacteria (one-way = 27. 9, p < 0. 01) and lower numbers of Deltaproteobacteria (one-way = 465. 9, p < 0. 01). In the water phase there was a decreased relative abundance of OTUs affiliated with the Actinobacteria in the oxic-to-anoxic (22. 5 to 3. 9%) and intermediate-to-anoxic (13. 0 to 4. 0%; Fig. 3 and Additional file 6: Data S1). In contrast, an increase was observed in the Cyanobacteria (1. 3 to 14. 1%), Alphaproteobacteria (5. 5 to 15. 1%), and Sulfurovum-like OTUs (0. 6 to 8. 2%) (Fig. 3 and Additional file 6: Data S1). The relative abundance of Sulfurimonas-like OTUs strongly increased from 9. 4 to 84. 2% in the anoxic control water phase (Fig. 3). A decrease in Bacteroidetes OTUs (13. 7 to 3.

Output: contrast


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

Output: pass


Input: Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [SEP] Text: These curves have been extracted from PSIM support for the wind turbine used in this study. It is clear from this figure that for each wind speed the mechanical output power is maximum at particular rotational speed, ωopt as shown in Figure 23. Figure 23. Typical output power characteristics. 4. 1. 2. PMSG modelThe generator is modeled by the following voltage equations in the rotor reference frame (dq axes) [29]:vsd=Rs isd+ dλsddt−ωr λsqvsq=Rs isq+ dλsqdt+ωr λsdE32Where λsq, and λsd are the stator flux linkages in the direct and quadrature axis of rotor which in the absence of damper circuits can be expressed in terms of the stator currents and the magnetic flux as [29]. λsd=Ls isd+ψFλsq=Ls isqE33Where ψFis the flux of the permanent magnets. The electrical torque Te of the three-phase generator can be calculated as follows [29, 30]:Te=32P [λsd isq−λsq isd]E34is the number of pole pairs. For a non-salient-pole machine the stator inductances Lsd and Lsq are approximately equal. This means that the direct-axis current isd does not contribute to the electrical torque. Our concept is to keep isd to zero in order to obtain maximal torque with minimum current. Then, the electromagnetic torque results:Te=32P ψF isq=Kc isqE35Kc is called the torque constant and represents the proportional coefficient between Te and isq. 4. 2. Control of the generator side converter (PMSG)The generator side controller controls the rotational speed to produce the maximum output power via controlling the electromagnetic torque according to Equation (33), where the indirect vector control is used. The proposed control logic of the generator side converter is shown in Figure 24. The speed loop will generate the q-axis current component to control the generator torque and speed at different wind speed via estimating the 24. The torque control can be achieved through the control of the isq current as shown in Equation (33). Figure 25 shows the stator and rotor current space phasors and the excitation flux of the PMSG.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: 7%), order Clostridiales (class Firmicutes, 4. 9 to 0. 5%), and Lentisphaerae (4. 5 to 0. 1%) occurred in the anoxic-to-oxic water phase (Fig. 3 and Additional file 6: Data S1). Correlations between microbial populations (OTUs) and chemical dataAt the level of major taxa, the Actinobacteria, Alpha-, and Betaproteobacteria in the water phase were negatively correlated with PO4 3− (p < 0. 01; r = −0. 42, −0. 37, and −0. 44, respectively; Additional file 8: Table S3). Cyanobacteria and Betaproteobacteria in the water phase positively correlated with NO2 − + NO3 − while Cyanobacteria also had a positive correlation to SO4 2− in the water phase (p < 0. 05; Pearson correlations r = −0. 42, −0. 37, and −0. 44, respectively). In contrast to the water phase, the majority of the major taxa level in the sediment correlated with NO2 − + NO3 − (Additional file 8: Table S3). and Verrucomicrobia all correlated positively with NO2 − + NO3 − (r = 0. 49 to 0. 82, p < 0. 05). Groups that correlated negatively included Bacteroidetes, Parcubacteria (OD1), Omnitrophica (OP3), Firmicutes, Lenthisphaerae, and Spirochaetae (r = −0. 44 to −0. 63, p < 0. 05). Several of these phyla also positively correlated with PO4 3− (data shown in Additional file 8: Table S3) except Proteobacteria that negatively correlated (r = −0. 66, p < 0. 01). Correlations with SO4 2− were all negative and included the phyla of Actinobacteria, Latescibacteria (WS3), and Candidate division TA06 (r = −0. 50 to 0. 68, p < 0. 05). The Sulfurimonas-like OTUs in the water phase had a significant positive correlation with PO4 3− (p < 0. 01; r = 0.

Output: contrast


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

Output: pass


Input: Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [SEP] Text: Power turbines provide a number of advantages that have resulted in their widespread use in several wind markets, including India, China, and the United States, among others. Due to the larger size of the rotor, more energy can be captured when the wind moves past the blades of the wind turbine. Hence, for a given turbine generator capacity, the generator runs closer to or at its rated capacity for an increased percentage of the time duration in such reduced specific power wind turbine. Hence, reduced specific power wind turbines naturally result in higher energy generation for their installed capacity, resulting in a higher capacity utilization factor (CUF). With the highly sophisticated control systems of modern wind turbines, this higher capacity utilization factor can often be achieved with a relatively limited impact on the overall turbine cost. In such cases, LSP wind turbines provide a direct path toward a lower levelized cost of energy (LCOE) by providing a higher generation per investment. Further, as reduced specific power turbines have lower-rated wind speeds, resulting in evenly distributed power production over a wide range of wind speeds, the variability of wind generation is well mitigated with such turbines. With significant penetration of RE generation into the grid, such variability management will be extremely beneficial to grid managers, and the overall energy mix can be well managed with reduced storage requirements. Further, the energy generation profiles resulting from reduced specific power turbines have also been found to increase the wholesale market value of wind energy [6]. Turbines with reduced specific power and taller towers can be conceptually correlated with higher electricity prices in some markets by producing less during high wind hours and producing more during low wind hours [7, 8]. With such a background, this 2. Low specific power (LSP) wind turbineThis section explains the key properties of the LSP wind turbine, as well as the characteristics of other wind turbine types currently on the market, in greater detail. The influence of the CUF on different sites with the wind turbines under consideration is also investigated. 2. 1 Wind turbine characteristicsThe study covers available wind turbines with specific power (SP) ranging from 379 W/m2 to 173 W/m2, which represent multi-MW scale turbine types prevalently installed in the Indian Market. These wind turbine types considered in this study, when correlated with its period of deployment, clearly show that there is a definite, reduced specific power trend in India, the United States, China, and Brazil.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Sequences for genes attributed to thiosulfate oxidation (coding for the Sox complex) was present in low abundance in all sediments (CPM 12 ± 6, 5 ± 7, 28 ± 32). Methane oxidation genes from formaldehyde assimilation were present in all treatments with the most sequences in the anoxic-to-oxic sediment (CPM 65 ± 12, 101 ± 31, and 228 ± 144). Metatranscriptomics of metabolic functions in the sedimentsThe de novo assembly of RNA sequences yielded 204,997 transcripts of which 57,386 could be annotated against the UniprotKB/Swiss-Prot (Additional file 9: Data S2) and PFAM reference databases (the two databases gave similar findings and therefore, only results from the UniprotKB/Swiss-Prot are presented hereafter). In the oxic sediments (oxic control: 4938 ± 0 and oxic-to-anoxic: 6272 ± 176 genes), fewer genes were expressed than in the intermediate-to-oxic/to-anoxic (7771 ± 375 and 7643 ± 303 genes, respectively) and anoxic control/to-oxic sediments (7859 ± 414 and 8274 ± 319 genes, respectively; Additional file 9: Data S2). RNA transcripts with significantly different FPKM counts according to edgeR analysis are presented in Additional file 9: Data S2 (UniprotKB/Swiss-Prot). Several of the most abundant known genes in the RNA transcripts were associated with inorganic sulfur transformations indicating activity of oxic and anoxic sulfur cycling in the different treatments (Fig. 4). RNA transcripts annotated for aerobic sulfide oxidation to sulfate were higher in the anoxic-to-oxic and intermediate-to-oxic cores compared to the anoxic controls or intermediate cores turned anoxic. These included genes for oxidizing sulfide to sulfur, such as sulfide:quinone oxidoreductase (sqr), sulfide dehydrogenase (fccB), and dissimilatory sulfite reductase (dsrA) that can act in reverse [59]. Spearman correlations of genes and chemistry data showed a significant negative correlation between sqr and sulfate concentration (r = 0. 68, p < 0. 05; Additional file 10: Table S4).

Output: contrast


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

Output: pass


Input: Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [SEP] Text: The sites are defined by 120 m hub height wind speed data derived from one continuous year of met. Mast-based standard measurement. Table 2 depicts the site details, wherein Figure 3 shows the wind speed distribution of the said four sites as below:Site name (units)Average wind speed @ 120 m (m/s)Weibull “A” (m/s)Weibull “k”High W10. 411. 692. 29Med W8. 59. 602. 62Low W6. 97. 802. 66Low coast W6. 37. 022. 82Table 2. Details of sites considered for the analysis. Figure 3. Wind speed distribution of the sites considered in the study. (a) Represents the histogram of high W site, wherein (b), (c), and (d) represents the histograms for med W, low W and low coast W sites. 2. 4 Comparative analysisThe generation for the entire year is estimated for each of the considered wind sites and wind turbines using the latest tools and % CUF are plotted in Figure 4. The trend seems instructive and shows the industry’s ability to achieve higher capacity utilization factors by lowering the of the turbines in the low and medium wind sites. Although the %CUF varies with respect to the wind potential, as obvious, there is a strong positive correlation found between the reductions in with the increase in Factor (%) estimations. However, the increase in generation in the high wind sites is considerably lower (in the order of around 30%) compared to low wind sites (an increase of 110–145%). Moreover, the futuristic Power wind turbine (LSP-105) simulated in the study is expected to produce appreciable generation in low wind sites resulting in lower LCOE. This is an encouraging phenomenon in particular for a situation when the wind penetration is increasing into the lower wind-speed regions. Figure 4. Turbines representing specific power vs. net capacity utilization factor (%). Hence, it is evident that reducing is undoubtedly enhancing the %CUF in all type of site conditions considered in this study, but highly significant in low wind sites. Of course, a higher capacity utilization factor is a mid-point, and it is not necessary that such wind turbine will have the economic superiority, as there is a cost to achieve it – a larger rotor assembly needs to be built and maintained for a given wind turbine capacity.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: In addition, heterodisulfide reductase (hdrA), used in both methanogenesis [60] and zero-valent sulfur oxidation [61], were present in all cores from the different treatments (Additional file 9: Data S2). Finally, RNA transcripts for sulfate adenylyltransferase (sat) that is suggested to be used in reverse to oxidize sulfite to sulfate [62] were also present in all cores from all treatments (Additional file 9: Data S2). In the UniprotKB/Swiss-Prot database, these RNA transcripts (Additional file 11: Data S3) were ascribed to Acidithiobacillus/Aquifex (sqr) and Gammaproteobacteria (fccB and dsrA). On the species level, fccB and dsrA were ascribed to the sulfide oxidizer Allochromatium vinosum (the Chromatiaceae family was present in the 16S rRNA gene data). Fig. 4RNA transcripts of major metabolic processes in the sediments. Selected functional groups of RNA transcripts from the sediment after incubation in darkness for 21 days (RNA transcripts that were ≥100 FPKM in one of the treatments are shown). Functional groups were chosen based on statistical significance from edgeR analysis of the studied chemistry and 16S rRNA gene data. a Anoxic-to-oxic (n = 2, two left bars for each gene) and anoxic control (n = 2, two right bars for each gene). b Intermediate-to-oxic (n = 2, left bars) and intermediate-to-anoxic (n = 2, right bars). c Oxic-to-anoxic (n = 2, left bars) and oxic control (n = 1, right bar). Stars denote a fold change of at least 1. 5 for the average of the two replicates compared to each replicate of the other shown treatment. All values show TMM normalized FPKM units. The colors denote relative abundance (% relative to column height) of taxonomic groups for the annotated RNA transcripts (i. e.

Output: contrast


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

Output: pass


Input: Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [SEP] Text: , 20 m/s, 18 m/s, 15 m/s, and 13 m/s), the LCOE seems to be on an increasing trend in high and medium wind sites, mainly because of the anticipated energy loss at the higher wind speeds. Figure 8. LCOE sensitivity analysis for the reduction in cut-off wind speed of the low SP wind turbine – (a) shows 2% reduction in capital cost and (b), (c) and (d) shows 3%, 4% and 5% reduction respectively. The results suggest that though reducing the cut-off wind speed will lead to significant energy loss in high wind sites and may impact the revenue and energy generation balance, in low wind sites it is economical to reduce cut-off wind speeds, as the expected reduction in blade mass and related savings will definitely outweigh the drop in energy generation. This is an encouraging takeaway in the present scenario and may lead to a conducive environment for more wind penetration into low wind sites. Advertisement 5. ConclusionThe design of modern wind turbines has seen major changes over the course of history. For example, one of the most noticeable manifestations is the steady decline in the specific power of wind turbines over time. With an eye toward analyzing the prospects of further specific power reduction in the future, this 24. EA. 1. 1CUF is a metric often used to evaluate the technical performance of a wind turbine/wind farm. It is a measure of “how well the plant is utilized”. Advertisement A. 2 of Energy (LCOE)of Energy (LCOE) is the cost of generating electricity over its lifetime. It is an economic assessment and defines the minimum price at which energy must be sold for a project to break even. Advertisement A. 3 Specific Power(SP) is defined as the ratio between the rated power of the turbine and its swept area [19], and is expressed in units of Watts per square meter (W/m2) as shown below:Specific PowerSP=. 3. 1The specific power is required to be lower in order to extract more output from lower wind speeds. The following equation can explain the reason behind it by showing that power is proportional to the rotor area and wind speed [20]. P=12ρAV3EA. 3.

Output: pass


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: The RNA transcripts for dmsA were affiliated with the class Halobacteria belonging to the Euryarchaeota phylum, while those for ddhAB were affiliated with the Alphaproteobacteria family Rhodobacterales (Additional file 11: Data S3). Both of these lineages were present in the 16S rRNA gene dataset (Additional file 6: Data S1). RNA transcripts for genes mediating methane oxidation (pmoA1A2 and pmoB1B2 encoding methane monooxygenase) were higher in anoxic-to-oxic (2232 to 3542 FPKM) compared to the anoxic control (<220 FPKM) as well as in the oxic control (720 for pmoA1A2, and 564 for pmoB1B2; n = 1) compared to the oxic-to-anoxic treatment that had FPKM values below 40 (Fig. 4). In contrast, RNA transcripts for genes coding for methane oxidation were not higher in the intermediate-to-oxic treatments (Additional file 9: Data S2). RNA transcripts for pmoA1A2 and pmoB1B2 had a significant positive correlation with redox potential (i. e. , high redox had more of these RNA transcripts; r = 0. 83, p < 0. 01; Additional file 10: Table S4) and were ascribed to the Gammaproteobacteria reference organism Methylococcus capsulatus (Additional file 11: Data S3) that was present in the 16S rRNA gene dataset (Additional file 6: Data S1). RNA transcripts coding for methanogenesis (acsA, mtaB, and mtbC2C3) were higher in the anoxic control (168 to 821 FPKM) compared to the anoxic-to-oxic sediment (13 to 194 FPKM). Also, mtaB and mtbC2C3 had more RNA transcripts attributed in the intermediate-to-anoxic cores (33 to 206 FPKM) when compared intermediate-to-oxic (2 to 72 FPKM; Fig. 4). The acsA RNA transcripts were affiliated with a wide diversity of bacteria including, e. g. , Alpha-, Beta-, Epsilon-, and Gammaproteobacteria (Additional file 11: Data S3).

Output: contrast


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

Output: pass


Input: Claim: Wind turbines generate power by converting kinetic energy from wind into electricity. [SEP] Text: 2Where, P–Power in the wind (W), ρ – (kg/m3), A–Rotor swept area (m2) and V–(m/s) A. 4 Wind class of a wind turbineWind class of a wind turbine helps to choose a suitable turbine for a particular site. The design of wind turbine and site conditions should complement each other for the successful operation of the wind turbine at the particular site, throughout its design life. As per the International Electro-technical Commission (IEC) standard IEC 61400-1, three wind classes (Class I, II & III) are categorized to represent high, medium, and low wind regimes. The classification is governed by the average annual wind speed (measured at the turbine’s hub height), the speed of extreme gusts that could occur over 50 years, and how much turbulence is there at the wind site. Generally, turbines are designed to cope up with high average wind speeds in the range of 10 m/s. A Class II turbine is designed for windier sites up to 8. 5 m/s average wind speed, whereas a Class III turbine is designed for a low wind site with the annual average wind speed up to 7. 5 m/s. Each of the mentioned further has subclasses to design turbines matching up with the wind turbulence at the site. A. 5 Power curve of wind turbinePower curve of a wind turbine shows the relationship between the output power of the turbine and wind speed, provides a convenient way to model the performance of wind turbines. A typical power curve for a pitch regulated wind turbine is shown below (Figure A1):Figure A1. Power curve of pitch-regulated blades. As shown in the figure, the power output in region-I is zero, as the wind speeds are less than the threshold minimum, known as the cut-in speed. In region-II, between the cut-in and the rated speed, the power production increases proportionally to the wind speed. In the region-III, a constant output (rated) is produced until the cut-off speed is attained through regulating the system. Beyond this speed (region IV) the turbine is shut down to protect its components from high winds; hence, it produces zero power in this region. A.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: RNA transcripts for mtaB affiliated with the archaeal class Methanomicrobia and Alphaproteobacteria class Rickettsiales, while mtbC2C3 affiliated only with Methanomicrobia (Additional file 11: Data S3); both of these classes were present in the 16S rRNA gene dataset (Additional file 6: Data S1). RNA transcripts encoding hdrA used during anaerobic methanogenesis [64] were present in all cores for all treatments. RNA transcripts coding for nitrate reductase (narH) were higher in the oxic-to-anoxic cores (1250 to 394 FPKM) compared the oxic control (1436 FPKM). In addition, RNA transcripts coding for narH were found at lower amounts in the other treatments (Fig. 4). RNA transcripts attributed to narH also correlated positively with NO2 − + NO3 − (r = 0. 68, p < 0. 05; Additional file 10: Table S4). These RNA transcripts were mainly affiliated with halophilic denitrifying archaeal species belonging to the class Halobacteria (closest available affiliation in UniProt KB: Haloferax mediterranei; Additional file 11: Data S3). Halobacteria were also present in the 16S rRNA gene data (Additional file 6: Data S1) and phylogenetic analysis, with reference sequences of Marine group and Haloferax species, indicated that the OTUs aligned with the order Halobacteriales and the ‘Group 6’ (DHVEG-6; Additional file 12: Figure S4). in the sediment might thrive in micro-niches or contain low-saline species as has been reported previously in sulfur and sulfide rich conditions [65]. In general, there was a low level of RNA transcripts coding for the general stress response in all treatments (Fig. 4). One exception was for the HSP70 family (hspA) that had higher RNA transcripts in the oxic-to-anoxic cores (538 to 778 FPKM) compared to the oxic control (46 FPKM).

Output: contrast


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

Output: pass


Input: Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: is Open Access? is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers. are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn 10. 5772/52063 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. power generation using innovative renewable and alternative geothermal energy technologies have shown merits and received renewed interest in recent years due to an increasing concern of greenhouse gas (GHG) emissions, being responsible for global warming & climate change, environmental pollution, and the limitations and conservation of natural energy resources. Cycle (ORC) power generation using low-temperature geothermal resources is one of these innovative geothermal power generation technologies. The vast low-temperature geothermal resources found widely in most continental regions have not received much attention for electricity generation. Continuous development of ORC power generation and state-of-the-art drilling technologies and other factors make this renewable and nonconventional energy source one of the best future viable, alternate and available source to meet the required future electricity demand worldwide, significantly reducing GHG emissions and mitigating global warming effect. The first part of this 2. ORC-based geothermal power generation 2. 1. Developments & utilization of low-temperature geothermal energy resources for power generation The geothermal resources of the Earth are vast and abundant.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: A second exception was for HSP90 family genes (hspC2/4–1/4–2) that had more RNA transcripts attributed in the oxic-to-anoxic cores (1216 to 1682 FPKM) compared to the oxic control (205 FPKM). The taxonomic affiliations of RNA transcripts coding for general stress were diverse and also included eukaryotes such as Amoebozoa. In addition, the chaperones dnaK and htpG were assigned to Sulfurimonas and Sulfurovum spp. (Additional file 11: Data S3) that tended to be higher in treatments turned oxic or the oxic control cores. DiscussionIn this study, we investigated changes in the microbial community composition and metabolic function in long-term oxic and anoxic sediments plus intermediate sediments from a coastal bay. The tightly clustered microbial communities in the oxic sediment field sample, control cores, and oxic-to-anoxic incubations were likely due to long time exposure to oxygen selecting for obligate aerobes (PCA, Fig. 2). In contrast, microbial communities in the anoxic-to-oxic sediment incubations were slightly less clustered, potentially as facultative anaerobes could adapt to increased oxygen concentrations. The intermediate sediment communities were most widespread compared to other locations, with populations of generalists adapted to the changing oxygen concentration. This suggested that historical oxygen concentrations will affect the microbial response to shifts in oxygen concentrations. Reduction of sulfate to H2S by sulfate-reducing bacteria is prevalent in sediments including anoxic sediment rich in labile organic matter [66] and the generated sulfide can be re-oxidized to sulfate in aerobic conditions [67]. Recent molecular phylogenetic and genetic analyses show that many genes coding for sulfur cycling processes occur in anoxic environments. For example, a metagenomic investigation of the waters in the oxygen-minimum zone outside the northern Chilean coast found that genes for both sulfate reduction and sulfide oxidation were present in the anoxic water [68]. Furthermore, with the use of isotope-labeled sulfate and oxygen, cryptic sulfur cycling was observed in anoxic salt march sediments. The results indicate microbial reduction of sulfate to H2S followed by microbial re-oxidation of H2S into intermediary sulfur species and eventually sulfate [69].

Output: contrast


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

Output: pass


Input: Claim: Geothermal energy harnesses heat from beneath the Earth’s surface for power generation. [SEP] Text: For example, the part of geothermal energy stored at a depth of 3 km is estimated to be 43,000,000 EJ (equivalent to 1,194,444,444 TWh) which is much larger compared to all fossil fuel resources, whose energy equivalent is 36,373 EJ, combined (Chandrasekharam & Bundschuh, 2008). Conventional energy resources, such as oil, natural gas, coal, and uranium, being widely consumed in the world, originate from finite energy sources embedded in the crust of the Earth. Only one energy resource of the crust is renewable, namely geothermal energy. The word “geothermal” is originated from Greek words; “geo” meaning the Earth and “therme” meaning heat, so geothermal energy means the natural heat energy from the Earth. The source of geothermal energy is the continuous energy flux flowing from the interior of the Earth towards its surface. Unlike other conventional and renewable energy sources, geothermal energy has unique characteristics, namely it is abundantly available, stable at all times throughout the year, independent of weather conditions, and has an inherent storage capability (Hammons, 2004). Distinct from fossil-fuelled power generation, geothermal power generation is also considered to be a clean technology and environmentally friendly power source which could significantly contribute to the reduction of GHG emissions by replacing fossil fuels and other non-clean energy sourcesused for power generation (Chandrasekharam& Bundschuh, 2008). Depending on the temperature and depth of the resource, the rock chemical composition and the abundance of ground water, geothermal heat energy resources vary widely from one location to another (Gupta & Roy, 2007). Geothermal heat sources are typically classified based on their available temperature, thus enthalpy energy level, from about 50 oC to 350 oC. The high-temperature (high-enthalpy) geothermal resources (with temperature > 200 oC) are typically found in volcanic regions and island chains, whereas the moderate-temperature (150-200 oC) and low-temperature (low-enthalpy) geothermal resources (<150 oC) are usually found broadly in most continental regions and by far the most commonly available heat resource (Chandrasekharam& Bundschuh, 2008; Gupta & Roy, 2007).

Output: corroborate