Input: Claim: Changing one's diet has no impact on the microbiome composition. [SEP] Text: Several poultry studies [64,65,66,67] have recorded unique shift in gut microbiota in response to heat stress. In terms of the respiratory tract, Wang et al. [10] reported reduced bacterial richness and diversity in the lung microbiota of cyclic heat-stressed (30 °C, 10 weeks) layer-type pullets compared to their thermoneutral counterpart (24 °C). In terms of microbiota composition, significant reduction in members of the genus Brevibacillus was also recorded in the heat-stressed birds. This is a notable observation given that Brevibacillus species are often considered for probiotic use due to their ability to secrete antimicrobial peptides that can hinder the growth of pathogenic microorganisms [68]. In conformity with Wang et al. [10] result, Sohail et al. [12] also recorded reduced tracheal microbial diversity in broiler chickens subjected to chronic HS (35 ± 2 °C, 42 days). Additionally, their study provided insights into the limitations of popular methods utilized in microbiome research, particularly related to taxonomic resolution, such as PCR-DGGE and 16S rRNA throughput sequencing. Results obtained using PCR-DGGE showed that the impact of HS on the ceca was more pronounced compared to trachea. Moreover, dietary supplementation with mannooligosaccharides was found to restore tracheal microbial homeostasis in heat-stressed birds. In addition to identifying differences in microbial composition between HS and thermoneutral groups, the use of 16S rRNA high throughput sequencing provided distinct bacterial clades and relative abundances between treatment groups at a much lower taxonomic resolution. Taken together, these results demonstrate that HS induces respiratory microbiota community disruption and microbial diversity reduction. The extent of this microbial dysbiosis resulting from HS may vary based on factors such as the type, intensity, and duration of the stress [69]. Therefore, it is crucial to continue studying heat stress intervention strategies for the poultry industry, as the documented effect of reduced microbial diversity on decreased resistance to opportunistic infections has been well-established [70, 71]. Although yet to be fully explored, antibiotic use is another factor that can influence the poultry respiratory tract microbiota community structure. For over six decades, antibiotics have been used at sub-therapeutic doses to enhance the growth performance of poultry flocks [72].

Output: contrast


Input: Claim: Fossil records provide critical evidence for understanding evolutionary processes. [SEP] Text: Topologically, the deep relationships of the WCS recovered in this study are incongruent with those recovered based on the phylogenomic analysis of target-capture sequencing and transcriptome data [28], which proposed a successive divergence of Daphniphyllaceae, Cercidiphyllaceae, Altingiaceae, and Hamamelidaceae with robust support for each node. Based on these results, phylogenetic incongruence between plastid and nuclear data (cytonuclear discordance) was detected in the deep clades of the WCS (Fig. 4). Cytonuclear discordance is commonly observed in some phylogenetically recalcitrant plant lineages [16, 50, 78,79,80,81]; in most cases, nuclear phylogeny is more congruent with morphologic characteristics than plastid phylogeny, and such discordance is thought to be caused by hybridization [17, 50, 82, 83]. Fig. 4Comparison of deep relationships within the “woody clade” in Saxifragales. (A) Phylogenetic analyses of plastomes. (B) Phylogenetic analyses of target-capture sequencing and transcriptome dataFull size imageBy comparing the plastome (this study) and nuclear genome phylogenies [28] with the morphological characteristics, we found that the interfamilial relationships recovered based on the analyses of target-capture sequencing and transcriptome data [28] are more consistent with the morphologies. Specifically, Altingiaceae has traditionally been treated as a member of Hamamelidaceae [10, 11, 13]. Both families are monoecious and have one or two-chambered anthers, distinct from the dioecism and four-chambered anthers of Cercidiphyllaceae and Daphniphyllaceae [23, 24, 26, 84]. These morphological affinities justify the close relationship between Altingiaceae and Hamamelidaceae.

Output: pass


Input: Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: 2 for TransportAssisted and partially automated systems are already common in series-production vehicles. They take over longitudinal and/or lateral control of the vehicle for a certain period of time and/or in specific situations. In the assisted systems, the driver is warned; in partially automated systems, the system assumes control. The impetus—for example braking—must still be provided by the driver. The most well-known driver assistance systems (DAS) are anti-lock brake systems (ABS) and electronic stability programs (ESP). Due to severe road accident, lane-change assistants, lane-departure warning systems and adaptive cruise control functions became mandatory for new vehicles ahead of schedule in 2013. Other systems are in development or are already ready for production, such as anti-rollover systems, cornering systems or parking systems. They are primarily focused on assisting the driver on the guidance and stabilization levels. Highly and fully automated vehicles can also navigate freely; they are situation- and infrastructure-independent. Fully automated systems can function without a driver. Not only activities on the navigation level are assumed by the driving robot, but also the activities that are necessary to return the system to a minimal-risk state when components fail. From the perspective of freight traffic and logistics, technological solutions for the stabilization and guidance levels are therefore not the primary focus. Of greater interest are the potential use cases with and without drivers and free navigation. A closer examination is conducted on the following distinctions in the use cases: 1. as highly automated highway driving with a driver and free navigation; 2. Demand as highly automated highway driving without a driver and with free navigation; 3. for Extended Availability—Follow-me Vehicle as highly automated driving without a driver and without free navigation (missing use case 3/4), 4. as highly automated driving without a driver and without free navigation (missing use case 3/4). In the following, the use cases will be introduced and initial arguments for and against the respective degree of autonomy in logistics and freight transport are presented. In each case it will be assessed to what extent the use cases in the freight traffic system coincide with the “of Autonomous Driving” (see Chap. 2) for private transportation, what differences exist and what the implications of such differences are for implementation. 3. 3 with Driver and Free NavigationInterstate pilot describes the use case in which an autopilot function is used, but a driver is available at all times.

Output: corroborate


Input: Claim: Changing one's diet has no impact on the microbiome composition. [SEP] Text: However, their continued use has been linked to public health concerns related to antibiotic resistance and residues, leading to calls for their reduction and ultimate discontinuation by governments and the general public [73]. Several studies [71, 74, 75] have affirmed that antibiotics are capable of inducing changes in microbial structure in the poultry gut. This antibiotics-induced shift in microbial structure has also been associated with decreased adaptive immune responses in poultry, particularly in a dose-dependent manner [76,77,78]. Conversely, only one study [18] investigated the influence of antibiotics on the poultry respiratory microbiota. In this study, commercial turkey flocks treated with chlortetracycline recorded a lower abundance of Mycoplasma in the trachea. The abundance of Mycoplasma was negatively correlated with the average weight of birds in the same study, which is notable considering a link between gut microbiota composition and bird weight has previously been established [79]. Vancomycin-colistin induced gut microbiota dysbiosis with concomitant impairment of lung immunity has also been reported in mice [80]. Despite these findings, further studies are required to fully understand the role of antibiotics in dysbiosis of the poultry respiratory tract and its effects on immunity and growth performance. Pathogens, especially viruses, have also been shown to cause respiratory microbiota disruption in humans [81,82,83]. This has recently been demonstrated in the poultry respiratory tract in a longitudinal study in which birds were challenged with low pathogenic avian influenza (LPAI) H5N2 virus [84]. Viral infection increased the total bacterial content in nasal microbiota samples; however, this increase had an inverse relationship with bacterial species richness. Samples retrieved from the nasal cavity also showed significant virus-induced change in beta diversity. Results from this study also suggest that virus-induced microbiota disruption is respiratory-site specific. Compared to the control treatment in this study, virus-induced microbial disruption across several taxa reduced linearly from the URT to the LRT. The number of microbial taxa disrupted by the LPAI H5N2 virus were 16, 5, and 1 across the nasal cavity, trachea, and lungs, respectively.

Output: contrast


Input: Claim: Fossil records provide critical evidence for understanding evolutionary processes. [SEP] Text: Among these, the plastomes of 27 species representing 10 genera were newly sequenced in this study (Table S1), and publicly available plastomes of 29 species representing 13 genera across four WCS families were obtained from the database (Table S2). The genomic DNA of these newly sequenced samples was extracted from approximately 100 mg of silica gel-dried leaf tissue using a modified CTAB method [87]. Then, the shotgun DNA libraries were constructed using a (Illumina, Inc. , ) following the manufacturer’s instructions. Paired-end sequencing was performed on an Illumina NovaSeq 6000 platform to generate approximately 4 Gbp of raw reads for each sample. Plastome assembly and annotationTrimmomatic v0. 40 [88] was used to filter low-quality Illumina raw reads with pre-set parameters. The pipeline GetOrganelle v1. 7. 7. 0 [89] was employed to assemble plastomes using Illumina clean reads with default parameters, using the plastomes of the corresponding species as a reference (Table S2). The assembled plastomes were further adjusted using Bandage v8. 0 [90] and annotated using the online program GeSeq [91]. Information on the initiation codon, stop codon, and intron sites of the protein-coding genes was examined and manually adjusted using Geneious v10. 2 [92]. tRNA genes were annotated using trnascan-SE v2. 0 [93]. The inverted repeat (IRa and IRb) regions of the plastome were determined using Geneious v10. 2 [92]. All WCS plastomes were progressively aligned with the complete plastome of Hamamelis mollis, as a reference, using the multiple genome alignment tool Mauve v4. 0 [94], after one of the inverted repeat regions was removed. Phylogenetic analysisGiven the close relationship between the WCS and Paeoniaceae [e. g. , 27, 52], eight Paeonia species were selected as outgroups. In total, 78 plastid PCGs (Table S4) commonly shared by the sampled plastomes were extracted from each plastome for phylogenetic analysis using PhyloSuite v1. 2. 2 [95]. The PCGs were aligned with MAFFT v7.

Output: pass


Input: Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: The driver hands over the stabilization and guidance level (and thus also navigation) to the autopilot, ideally after specifying a destination address. The use of an interstate pilot is intended between entering and exiting the interstate. The driver assumes control when confronted with unclear driving situations (e. g. construction zones). The posited benefits of the interstate pilot are provided by the reduction of accidents and an improved traffic flow. Particularly in traffic jams or stop-and-go traffic situations, but also on long, monotonous journeys, as well as due to the ever-increasing time-pressure of modern life, drivers are often close to their performance limits. In addition to unburden the driver from stressful driving situations, the interstate pilot also creates free time that can be used for other purposes. Here there are deliberations as to whether activities such as route planning and fleet management might not also be decentralized. A first case of application has been already reported from as early as 1987, when the University of the in Munich, Germany, was experimenting with a lorry driving on a motorway [15]. In 2013, the vehicle-manufacturer Scania presented a truck that could “independently accelerate, brake and steer” up to a speed of 50 km/h [16]. In 2014, Daimler had a truck driving up to 85 km/h amid other vehicles on a closed section of interstate [17]. The envisioned interstate pilot is essentially enabling fully automated driving. Due to safety considerations, however, the concept still depends on an available driver, which in effect reduces the scenario to being only highly automated. The effective top speed and the higher maximum permissible mass together require different safety concepts than in private transportation. Changes are expected primarily in terms of the job description of the driver. To date, drivers have been obliged to learn much about vehicle technology and securing loads. Would the driver continue to perform the technical inspection of the vehicle? What economic and environmental benefits would be associated with the concept? 3. 4 Vehicle on a Driver and with Free NavigationVehicle on demand most closely resembles the use case, which, in the field of freight transport, is known as an autonomous, decentralized AGT/AGV system. A driver’s seat is not envisioned. However, in the use case outlined in Chap. 2 the vehicle can operate at speeds of up to 120 km/h and in unknown scenarios.

Output: corroborate


Input: Claim: Changing one's diet has no impact on the microbiome composition. [SEP] Text: Additionally, genera Clostridium, Enterococcus, Escherichia-Shigella, and Pseudomonas were positively correlated with viral titers in the nasal cavity, while genera Enterococcus, unclassified Erysipelotrichaceae, Escherichia-Shigella, Staphylococcus, and Pseudomonas were positively correlated with viral titers in trachea. Conversely, the Lactobacillus genus was negatively correlated with viral titers in the trachea. In conformation with our respiratory site-specific microbiota disruption hypothesis, Yildiz et al. [83] have also reported marginal effects of virus infection on lung microbiota (LRT) in a murine model. Furthermore, considering that active virus replication occurred between days 5–7 post infection [83], another important finding from this study is that microbiota disruption may persist even after virus clearance. The reduction in bacterial species richness was still observed in samples retrieved from the nasal cavity even at 14 days post infection. Innate immune responses, particularly via cytokine production related pathways, is a possible mechanism for the virus-mediated microbiota dysbiosis. This has been pointed out in several in vivo mouse and poultry studies [71, 85,86,87]. Therefore, virus-induced dysbiosis could potentially support the proliferation of pathobionts and opportunistic infections in poultry flocks [88, 89]. It is yet to be seen if probiotic formulations can restore microbiota homeostasis in the poultry respiratory tract, following viral challenge. Despite the very limited number of studies that have explored the effects of various intrinsic and extrinsic factors on the poultry respiratory microbiota, it is important to state that in the absence of well-designed a priori experiments, it is difficult to determine which of these factors exert the greatest effects. Much of the available data in the literature are confounded by several variables in their experimental design, hence the interpretation of these results needs to be treated with caution. For instance, multivariate regression models revealed that experimental factors including bird type, age, body site, and flock in the study by Taylor et al. [18] were only able to explain about 54% of the total sample variance. This suggests the existence of additional sources of microbial heterogeneity beyond those accounted for.

Output: contrast


Input: Claim: Fossil records provide critical evidence for understanding evolutionary processes. [SEP] Text: 402 [96] and concatenated using Geneious v10. 2 [92], with the default parameters. BI, ML, and MP methods were used to infer phylogenetic relationships. For BI analysis, PartitionFinder v2. 1. 1 [97] was used to estimate the best partitioning schemes and substitution models (Table S5). MrBayes v3. 22 [98] based on BI, was used to construct a phylogenetic tree. Carlo (MCMC) analyses were run for two million generations, sampling one tree every 100 generations and discarding the first 25% of trees as burn-in. The obtained trees were evaluated for convergence using Tracer v1. 7. 1 [99], with effective sample sizes (ESSs) > 200. For the ML analysis, IQ-tree v2. 1. 3 [100, 101] was used to estimate the best partitioning schemes and substitution models (Table S6), and the ML bootstrap support (MLBS) value of each branch was calculated with 1000 replicates. The MP analyses employed PAUP* 4. a168 on the XSEDE platform via the Cyberinfrastructure for Science () Gateway web server. All characters were treated as unordered and equally weighted, while branches with a minimum optimized length of zero were condensed [102]. The analysis utilized a heuristic search approach with tree bisection-reconnection (TBR) branch swapping. It generated 1000 replicates, employing random-addition-sequence methodology and allowing for the storage of up to 10,000 trees per replicate. From the retained most-parsimonious trees (MPTs), a strict consensus tree was derived. Bootstrap support values were calculated using bootstrap analyses [103] involving 1000 replicates. Each replicate consisted of 10 random-addition-sequence replicates, with a maximum of 100 trees saved per replicate. Fossil record and estimation of divergence timeData for the fossil record of the WCS were obtained from the literature, the ( and the Index ( BEAST v1. 10. 4 [104] was employed to estimate the divergence time. According to the most recent time-calibrated angiosperm phylogeny [29], the stem age of the WCS is constrained to 105.

Output: pass


Input: Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: There is some reason to believe that the “vehicle on demand” use case comes very close to the conception of autonomous vehicles in freight transport desired by companies in the industry: The local-language-speaking driver who is willing to undertake long interstate journeys with long periods of absence and irregular working hours for low pay is ever-more-difficult to find. Overtired truck drivers are the most common cause of severe accidents. Docking and maneuvering in tight delivery situations is always a tricky task for the driver. The use of automation technology could therefore be of great benefit. However, the approval of all scenarios for heavy trucks will take time, in particular due to safety concerns and required changes in the supply chain (see Sect. 18. 4). More likely is the realization of the interstate pilot without a driver and with free navigation between rendezvous points in the medium term, for example between rest areas on highways or between well-connected commercial zones. The likelihood of implementation of this use case could be increased through the following enhancement concepts:Safety concerns due to the lack of a driver on the fallback level could be mitigated by having separate highway entrances and exits for autonomous vehicles, as this would reduce encounters with other vehicles to a minimum. A dedicated lane for autonomous, coupled vehicles could probably avoid these concerns entirely. At the same time, such a dedicated lane could be the starting point for an extended concept with alternative drive technology, if the lane were, for example, outfitted with an overhead contact line for supplying electricity. The coupling of vehicles (platooning) would combine the initial concept of having a driver available in the lead vehicle as a backup with the extended concept in which vehicles drive autonomously without drivers and utilize the benefits of each concept. The platooning vehicles would be connected via a software system. Implementing the so-called electronic drawbar at high speeds would enable not only improved utilization of the road infrastructure, but above all reduced fuel consumption and emissions due to reduced wind resistance. Numerous tests have been carried out with the electronic drawbar since the mid-1990s [e. g.

Output: corroborate


Input: Claim: Changing one's diet has no impact on the microbiome composition. [SEP] Text: The information provided here on select intrinsic and extrinsic factors that potentially influence the respiratory microbiota would be valuable for the design and execution of poultry microbiota studies with reproducible outcomes. Role of poultry respiratory microbiota in pathobiology and immune responsesThe avian mucosal surfaces are occupied by an abundant number of microbial communities that are in constant interaction with the host, other members of the community and the environment. A healthy balance of this tripartite relationship, especially in terms of compositional stability and diversity, ensures a healthy microbiota in humans [90]. Any compositional or functional alteration of this relationship is referred to as microbial dysbiosis, which is generally characterized by increased abundance of pathobionts [91]. Microbial dysbiotic conditions are implicated in several pathological conditions in avian species, including pathogen colonization, excessive inflammation, dysregulated immune responses, and severe disease conditions [33, 92]. The potential of the respiratory microbiota to influence viral infections, vaccine responses, secondary bacterial infections, and disease severity has also been reported [12, 93]. Consequently, the role of resident microbiota in the poultry respiratory tract on the host immune system and disease outcomes continues to be the subject of recent research [13, 94]. Information from studies of this nature could offer a better understanding of economically important avian diseases and zoonoses. Economically important avian respiratory diseases that are microbiota-exacerbated or linked to bacterial etiologies include but are not limited to Mycoplasma infection, infectious coryza, Colibacillosis and [10, 28, 71, 95,96,97,98,99] (Table 2). Under homeostatic conditions in the respiratory tract, commensal microbiota contribute to host immunity by inhibiting pathogen persistence and transmission via several means, including competitive exclusion, bacteriostatic activities, biofilm formation, quorum sensing, and others [100]. Conversely, under dysbiotic conditions, potential respiratory pathogens include Escherichia coli, Ornithobacterium rhinotracheale, Mycoplasma synoviae, Mycoplasma gallisepticum, Gallibacterium anatis, Avibacterium species, and Staphylococcus aureus [9, 33, 101].

Output: contrast


Input: Claim: Fossil records provide critical evidence for understanding evolutionary processes. [SEP] Text: AbbreviationsBP: Base pair BS: Bootstrap CTAB: Cetyltrimethylammonium bromide DNA: Deoxyribonucleic acid ESS: Size GC: Guanine and Cytosine GTR: Reversible IR: Inverted repeat ISC: Large single-copy MCMC: : MPT: Most-parsimonious tree PCG: Plastid protein-coding genes PP: Posterior probability rRNA: Ribosomal RNA tRNA: : Small single copy TBR: Bisection-reconnection WCS: Thewoody clade? in Saxifragales 9. 19. 77. 150. 5488. 22. 232. 4. Stuttgart, Germany: Borntraeger Science; 1892. 1959. 1981. 49. 25. 1997. 20. 10. 1111/jipb. 13415. . Discordance between nuclear and chloroplast phylogenies in the heuchera group (Saxifragaceae). Evolution. 1995;49(4):727–42. 50. 1012. 41. 57. 61. Fu D, Endress PK. Cercidiphyllaceae. In: editors. Flora of China. . St. Louis: Science Press, Beijing & Garden; 2001. p. 126. 2008. p. 315–317. Tang M-S, Tsai C-C, Yang Y-P, Sheue C-R, Liu S-H. A multilocus phylogeny of Daphniphyllum (Daphniphyllaceae). Environ. 2022;107:137–52. 1997. 29. 82. 70. 31. 56. 3544. 694. 70. 104. 67. 6. 66. 57. 8. 84. 68. 708. 12. 80. 7183. 74. 8. 70. 26. 66. 30. 2. 70. 412. 66. 63. 71. 14. 98. 983. Magallón S. From fossils to molecules: phylogeny and the core eudicot floral groundplan in Hamamelidoideae (Hamamelidaceae, Saxifragales). Syst Bot. 2007;32(2):317–47. 230. 64. 66. 75. 121. 22. 83. 72. 8. 8. 93. 43.

Output: pass


Input: Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: the European projects “and II”, “Trains for the Environment ()”, “Cooperative mobility solution for supervised platooning ()”, the Californian “PATH Program”, the German “-Projekt: Entwicklung und Untersuchung des Einsatzes von elektrisch gekoppelten Lkw-Konvois auf Autobahnen”, and the Japanese “ITS Project” by the (NEDO)]. In these projects, multiple trucks or a convoy of lead trucks and following passenger cars safely drove at up to 90 km/h with a minimum gap of 4 m. The systems were based on surveillance by radar sensors, stereo cameras, three-dimensional maps and usually also data exchange with other vehicles. Previous trials were always conducted with a driver in the lead vehicle and with or without drivers for extended availability in the following vehicles. There are already ideas under consideration for a driverless lead vehicle. As a rule these projects build on proven existing technology: adaptive cruise control systems integrated in series-production vehicles serve to maintain the gaps between the vehicles. Data transmission between the lead and following vehicle is frequently conducted via wireless-transmission or infrared technology. The measured fuel savings and CO2-reduction potential differed depending on the selected vehicle-to-vehicle (V2V) communication (in “off-center” mode), the type and construction of the lead vehicle and following vehicles, the gap, the speed as well as the road and environmental conditions (surface, temperature, inclines, altitude). The difference was roughly 5 % for the lead truck and 10–15 % for the following passenger vehicles [18]. 3. 5 for Extended Availability—Follow-Me VehicleIn the use case presented in Chap. 2, the vehicle takes fully automated control whenever the situation is approved for that mode. The driver is at any time available and can resume vehicle control if necessary. In practice it is an extension of the interstate pilot in terms of approved situations and the permissible speed and is very similar to autonomous driving. This use case is only interesting from an economic perspective if the accompanying driver can pursue value-creating activities during the drive. The next delivery could be prepared or processed from an administrative standpoint during the drive itself.

Output: corroborate


Input: Claim: Changing one's diet has no impact on the microbiome composition. [SEP] Text: In addition to modifying the structural composition of the microbiota, another way in which AIV infection influences antiviral immune response in the host is by influencing microbiota diversity. There are conflicting reports on the effect of AIV infection on microbiota diversity in the literature. Kaul et al. [97] reported significantly higher microbial diversity (Beta diversity) in AIV-infected ferrets compared to uninfected ferrets. These microbial perturbations are facilitated by host antiviral responses, such as the induction of interferon [97]. In contrast, Chrzastek et al. [111] reported reduction in microbial richness and phylogenetic diversity in the colon microbiota of H9N2 AIV-infected birds. Similar reduction in species richness have been reported in the fecal microbiota of H5N1 AIV-infected swans [114]. In spite of this inconsistency related to the effect of AIV infection on microbial diversity in the literature, a simplistic definition of “low” or “high” levels of diversity and their relation to host antiviral immune responses may not be entirely correct. A closer look at the specific colonizers of the microbial community and the ecological niche occupied may afford better insight. For example, microbiota dominated by and Veillonella may signify specie richness. However, these taxa have been associated with increased AIV replication [71, 98]. It is also possible that the activities of this taxa differ depending on the ecological niche occupied. Also, they may increase AIV virulence in respiratory sites compared to the gut. Nonetheless, being the main site of infection, the effect of AIV infection on the respiratory microbiome requires more attention. Conversely, certain microbial species have demonstrated positive impacts on adaptive immune responses and antiviral responses. For instance, Alqazlan et al. [119] showcased the ability of specific Lactobacillus strains (such as L. salivarius, L. johnsonii, and L. reuteri) to induce immunostimulatory and antiviral responses against H9N2 AIV.

Output: contrast


Input: Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: Skip to main content Explore journals Get published About BMC My account Search all BMC 19. 25 million years ago (Mya). This time represents the average between paleontological and molecular estimates, which place the origin of the crown group radiation, respectively, at 18. 5 and 20 Mya. Fig. 1Phylogenetic relationships within the Liolaemus radiation showing variation in body size (snout-vent length obtained by averaging male and female SVLs) across species (black bars, in mm). Clade colours indicate the eight main groups (or subgenera) within the genusFull size image Analyses of lineage diversificationAnalyses based on the time-calibrated phylogenetic tree were performed to quantify the evolutionary tempo and mode of diversification in Liolaemus, with focus on both lineage and body size diversity. To quantify historical rates of species accumulations (i. e. , tests of the prediction that diversification has slowed down over time following an early burst) we created a lineage through-time (LTT) plot implemented in the R package ‘ape’ [41]. For the LTT curve, we first implemented Pybus & Harvey’s [42] (MCCR) test. This analysis calculates the γ statistic for incompletely sampled phylogenies, by comparing the distribution of inter-node distances between the tree root and its temporal midpoint to the distribution of distances between the temporal midpoint and the tree tips [43]. Negative γ values indicate that inter-node distances between the root and the midpoint are shorter than the distances between the midpoint and the tips, and hence, that most branching events occurred earlier in the evolutionary history of the clade, a pattern consistent with a decline in the rate of species accumulation over time (i. e. , an ‘early burst’ model of diversification). When lineage diversification follows a constant rate process, the branching events are evenly distributed throughout the tree, with γ being normally distributed and with a mean of 0. Given that incomplete taxon sampling in a phylogeny increases type I error rates in diversification analyses, the MCCR test computes corrected γ distributions through simulations of phylogenies to the known clade size (~240 species in Liolaemus) under the null hypothesis of a constant rate pure-birth diversification process.

Output: pass


Input: Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: Such vehicle concepts would also be conceivable for use in commercial passenger traffic, for example for activities in elderly care, the insurance industry, etc. , which involve documentation and administrative work. The “EmiL” concept study of a partially autonomous delivery vehicle in 2011 can be regarded as a sort of preliminary stage of development before full automation [19]. In this concept, the delivery person does not need to continually get in and out of the vehicle, but can instruct the vehicle via mobile phone to drive next to him at walking speed (follow-me function). For all unknown situations (e. g. merging situations, intersections), there is the additional DriveStick mode in which the vehicle can be driven at up to a speed of 6 km/h. The use of a local wireless-connection precludes signal interruptions that are feared and can indeed occur with GPS connections. The research project determined a time-saving of about 40 min over a one-day delivery period. The typical injury risk of twisting or rolling an ankle when getting out of the vehicle is also reduced. 3. 6 Valet Parking—Valet DeliveryValet parking refers to the use case in which the autopilot independently navigates the vehicle to a previously assigned parking spot. The concept presented in Chap. 2 assumes that this could be possible in the public sphere at speeds of up to 30 km/h. One example could be autonomous driving from a driver’s place of residence to a defined parking spot. This use case is scarcely conceivable in the context of freight transport, however, as there are truck-parking locations rarely connected via the secondary road network. Much more likely is a scenario in which the autopilot would take over in tight inner-city areas and industrial and retail delivery zones not designed for large trucks and park or dock the truck independently. This could prevent expensive car damages to occur. The driver would be relieved of stressful driving tasks, which would be particularly advantageous in light of the driver’s remaining responsibility for long, exhausting highway drives. Valet delivery could also help drivers comply with legal rest times if the “last mile” could be conducted without their assistance. Another potential use case could be delivery to construction sites. In major construction jobs, trucks are increasingly held in so-called waiting zones in the vicinity of the construction site.

Output: corroborate


Input: Claim: Changing one's diet has no impact on the microbiome composition. [SEP] Text: These responses encompass the activation of interferon-stimulated gene (viperin) and the robust expression of cytokines in cells of the ceca tonsils, including T-helper (Th)1 type cytokines (interleukin [IL]-2, IL-12, and IFN-γ), pro-inflammatory cytokines (IL-1β and IL-6), and an immunoregulatory cytokine (IL-10). Moreover, a combination of Lactobacillus species, comprising Lactobacillus salivarius, L. johnsonii, L. reuteri, L. crispatus, and L. gasseri, has also been shown to enhance both cell- and antibody-mediated immune responses against H9N2 AIV [78]. This beneficial effect is particularly pronounced when co-administered with synthetic CpG oligodeoxynucleotides (ODN) 2007. Avian mycoplasmosisis a chronic infectious respiratory disease that affects poultry of all ages. The economic consequence of this disease includes poor hatchability, reduced egg production, poor weight gain and egg production [120]. The etiological agent of this disease is the pathogenic bacterium, Mycoplasma gallisepticum (MG), which is commonly isolated on most poultry farms. This pathogen can be spread both vertically and horizontally and can cause severe inflammatory responses and immune dysregulation in birds [120, 121]. Current management tools available to curb MG infection on farms include sourcing birds from breeding stock that are certified as healthy, implementing rigorous biosecurity practices, administering vaccinations, and delivering antimicrobials to infected birds [122, 123]. Although the use of antibiotics like fluoroquinolones and macrolides is a popular approach, there is a growing concern related to the development of antimicrobial resistance in this pathogen [124, 125]. By combining both 16S rRNA sequencing methods and an antibiotic-induced microbiota depletion protocol, Wang et al. [10] were able to provide insight into the role of the respiratory microbiota on MG infection in layer chickens. The findings of this study revealed that MG infection induces a distinctive shift in the microbiota community. Specifically, there was a significant decrease in the relative abundance of bacteria such as Staphylococcus, Lactobacillus, Weissella, and Butyricicoccus in their study.

Output: contrast


Input: Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: Species are then randomly pruned from the simulated trees to replicate incomplete sampling (109 species are included in our tree; see above). Our analysis is based on 10,000 simulations. The MCCR test was conducted using the ‘laser’ package in R [44]. We then analysed the diversification dynamics that are more likely to have shaped the LTT trend of Liolaemus species accumulation by fitting multiple evolutionary models that rely on different evolutionary processes. We used Etienne et al. ’s [45] maximum-likelihood fitting-model approach to test four alternative hypotheses of diversification. This technique employs a hidden Markov model (HMM) approach to calculate the likelihood of a phylogenetic history under multiple diversity-dependent birth-death models of diversification. These models account for the influence that species other than those included in the phylogeny (i. e. , both extinct species and species missing from the phylogeny) may have on historical rates of diversification (given that speciation rates are a function of existing species at each point in time). Therefore, this approach is comparable to the results produced by the MCCR test above as both techniques consider the potential effects of species missing from the tree [43]. Two of the four fitted models assume constant diversification rates. These are the pure-Birth (or Yule) model, which assumes no extinctions, and the constant rate birth-death model (crBD), which allows extinctions but assumes that the rates of speciation and extinction remain constant through time and across lineages. The other two models, density-dependent logistic (DDL + E) and density-dependent exponential (DDE + E), assume diversity-dependence and thus quantify diversification rates as functions of changes in accumulating diversity over time (while accounting for extinctions, E). While the DDL + E models linear rate changes in diversification, the DDE + E models exponentially declining speciation rates as a function of extant lineage diversity at any point in time. We fitted all four models under two alternative assumptions about the proportion of missing species in the phylogeny. First, we assumed that the Liolaemus clade consists of its currently known 240 species. We then assumed that the genus consists of many more species than those currently reported, and that our phylogeny only accounts for 30 % of the ‘real’ diversity of the lineage.

Output: pass


Input: Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: These processes could be optimized if drivers in large-scale concrete-pouring actions could concentrate on their runs and the wait times were eliminated. The autopilot would take over driving between the waiting point and the construction site. From a legal standpoint, implementation would depend heavily on the distance between the waiting point and site as well as the concrete scenario. Otherwise implementation would seem to be more of a mental than a technical challenge. In particular from the driver’s perspective, who would be confronted with scenarios in which it is essentially alleged that he or she does not handle their working tool with the required skills. 4 Changes in the Resulting from a of Automation in Freight TransportA systematic examination of the changes in the supply chain resulting from a higher degree of automation in freight transport can be seen in the generic supply chain depicted in Fig. 18. 1. Every supply chain consists of a succession of the activities “raw material extraction”, “processing/production”, “trade”, and the intermediate logistical processes “goods issue (handling)”, “transport”, and “goods receipt (handling)”. Moreover, under the current conditions of production and retail logistics, it is equally interesting to consider the effects of a lacking driver since a lot of added-value-services are performed by the logistics providers in those scenarios. Fig. 18. 1Example of driver-relevant activities in a generic supply chain. Source Author, based on [20]Full size image Aside from driving itself, “transport” requires inspection of the vehicle, route planning as well as documentation and other administrative tasks (shipping documents) taken over by the driver. These activities are even more comprehensive in the case of cross-border traffic (customs). With highly automated, driverless or fully automated driving, the common problem of driving and rest-period violations would become obsolete and routes could be planned differently. If transports were conducted in a highly-automated manner, the driver would be expected to perform other tasks in this period, for example performing route planning, fleet management or simply resting [21]. In other words, it could lead to both a decentralization and parallelization of functions. The use of the transport time for other activities is not new, however.

Output: corroborate


Input: Claim: Changing one's diet has no impact on the microbiome composition. [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: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: For both scenarios, we fitted all four models using the R package ‘DDD’ [45]. To evaluate the best-fit model, we employed the Criterion (AIC) approach [46]. We report the bias-corrected version of AIC, referred to as AICc [47, 48]. The goodness of fit of candidate evolutionary models is determined by identifying the lowest AICc scores, and hence, when shown as ΔAICc scores (the difference between the best or lowest AICc, and the AICc of each alternative model), then the best model has ΔAICc = 0 [47, 48]. Body size dataTo evaluate the potential relationship between clade diversification and phenotypic evolution during the radiation of Liolaemus, we investigated the rates and trajectories of body size diversification. We focus on body size as it is the single most important morphological trait that influences the majority of ecological and evolutionary processes via its correlation with most components of organismal form and function [49, 50]. In addition, body size is often considered to be a key morphological indicator of niche in natural populations [49, 51]. Also, in Liolaemus in particular, body size is ideally suited for diversification analyses as existing evidence suggests that its variation is not predictably influenced by geographic/climatic clines [28, 30, 34], it varies with numbers of coexisting species (Pincheira-Donoso, unpublished observation), and other phenotypic traits observed to respond to ecological pressures in other lineages (e. g. , body proportions, [1]) vary in rather unpredictable ways when linked to, for example, habitat characteristics [30, 52, 53]. We used snout-vent length (SVL), the traditional proxy for body size in lizards [54–56]. For the analyses, we collated an extensive body size dataset (Additional file 1) consisting of 6,500+ adult individuals (adulthood was estimated based on body sizes reported in previous studies, [30–32, 34]), representing >85 % of the currently known species diversity within the genus. To obtain SVL for each species, we averaged male and female SVL values, calculated independently using the upper two-thirds of the size range available for each sex in each species [30, 57].

Output: pass


Input: Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: In the road freight traffic system, the predominant transshipment systems generally involve changing the trailer rather than the driver. These systems have many advantages such as quick transfer times for materials and goods, and results in increasing productive times of the vehicles. They also improve the working conditions for the drivers, who can return to their local branches daily. In practice, however, this only works in large networks or in partnership-type relationships with trustworthy and reliable partners. This applies not only to punctuality, but also with respect to the securing of the load. To date, the most widespread use of these systems has been with the large integrators and courier, express and parcel (CEP) service providers specialized on the transshipment of standardized packages. For the implementation of driverless transport chains in road freight transport, then there are also questions of cost and liability in transport law that need to be clarified, going beyond questions of technical compatibility of the autonomous vehicles with the infrastructure and with other vehicles or platooning trucks. If damages to the goods or vehicle occur due to system failure, product liability comes into play (for details, see Chap. 25). It follows that autopilots, consisting of hardware components (sensors, processors and actuators) and software components must be constructed in such ways that potential damages can be traced to the individual components and thus their producers. 6 Assessments of in the ChainJudging from a macroeconomic perspective as well, automated systems could provide an approach to resolve structural deficits in the current transport system. In times of tight public budgets, a significant expansion of the traffic infrastructure, and in particular rail and inland waterways, is not likely to occur in the near future. Automated driving, with its minimal space requirements and rather equal speed levels, could at least double the existing average road infrastructure capacity. Moreover, it could contribute to achieving environmental policy goals, as automated driving reduces fuel consumption. Already today, driver assistance systems make a significant contribution to reducing traffic accidents. Such accidents could also be avoided by the use of highly and fully automated vehicles, as they can recognize the ends of traffic jams, avoid risky overtaking maneuvers and entirely eliminate wrong-way driving. However, the questions concerning legal liability in the event of an accident have not yet been exhaustively resolved. In the case of platooning, a professional driver could be used in the lead vehicle, as is envisioned in most of the projects.

Output: corroborate


Input: Claim: Changing one's diet has no impact on the microbiome composition. [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: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: Although maximum SVL has been extensively used as a proxy for size in lizards, it has been shown that the use of extreme values may result in body size overestimations [58]. In contrast, the use of intermediate percentiles between the maximum recorded value and the mean from the entire adult sample provides accurate estimates of asymptotic size [58]. The entire dataset was collected by the same person (DPD) to control for error arising from inter-individual measurements (e. g. , [57]). The species included in our dataset encompass the entire phylogenetic, phenotypic, ecological, and geographic diversity known in Liolaemus [30, 52, 53], and therefore, they provide an adequate sample of the body size diversity in this genus (Fig. 1). Modelling body size evolutionWe investigated the evolutionary dynamics of body size throughout the phylogenetic history of Liolaemus using two quantitative approaches based on our time-calibrated phylogeny. First, we quantified the tempo and mode of body size diversification by fitting four alternative models that describe different evolutionary dynamics: the Brownian-motion model (BM, which describes a random walk of trait evolution along branches in the phylogeny, with increase in trait variance centered around the initial value at the root of the tree, and increasing with the distance from the tree root; [59]), the Ornstein-Uhlenbeck model (OU, which assumes that once traits have adaptively evolved, stabilizing selection pulls the trait values around an adaptive optimum for the trait; [60]), the Early-Burst or “niche-filling” model (EB, which describes exponentially increasing or decreasing rates of evolution over time based on the assumption that niches are saturated by accumulating species within a lineage; [8]), and the Delta model (a time-dependent model of trait evolution, which describes the effects that early versus late evolution in the tree have on the rates of trait evolution; it returns a δ value which indicates whether recent evolution has been fast when δ > 1, or slow when δ < 1; [61]). Comparisons of goodness of fit for these models were performed through the Criterion (AIC) [46]. Selection of the best evolutionary model is based on the same AICc approach described above for model-selection of lineage accumulation. Model implementation and fitting was conducted with the R package ‘geiger’ [62].

Output: pass


Input: Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: However, these systems have not received sufficient attention either in the media or in transport research. In in-house logistics, for example, transponders are used as waypoints that can also store information. If vehicles in the public space would always have to recognize all environmental features and special characteristics of every route, appropriate cost-intensive hardware and software systems would be required. This could be avoided if waypoints would provide certain information that could be read-out temporarily. In this context, extrapolated to the transport system, the question arises as to how the road infrastructure would have to be changed. Fully automated driving creates the opportunity to steer vehicles between the departure point and the destination without human intervention. The autopilot performs not only the stabilization and guidance tasks, but also navigation, and thus is also responsible for ensuring accident-free driving. The assessment of the current situation with respect to the development of autonomous road traffic systems made clear that passenger and freight traffic must be viewed in a much more integrated fashion. It would also be interesting to have a closer look at autonomous system development with regard to other means of transport. Autopilots in aircrafts have long been the norm. As early as the beginning of the twentieth century, stabilization systems were used in aviation. Drones are already being used. The first studies on the European level of unmanned maritime transport are under way. Remote control of trains has been in use for years as well. What is new is the quality of autonomous decision-making in the case of changing environmental situations and changing driving and transport orders. The expanding fields of application, for instance through drones in the aviation sector, highlight the importance of completely rethinking the distribution of transport jobs across the different means of transportation. More research is needed here. Many exciting insights and new developments could be expected if the broad field of commercial passenger traffic would be studied in greater detail. Much alike in freight transport, it is concerned with mobility in exercising a profession. Since moving from one location to another is only a means to perform a service at the destination, in most cases mobility itself is regarded much more pragmatically, compared to individual mobility. The combination of partial scenarios of individual, autonomous logistics processes to form a comprehensive autonomous supply chain from automated raw material extraction and all phases of production and logistics to product delivery to the end customer requires an integrated approach. Technologically speaking, tremendous progress has been realized in recent years.

Output: corroborate


Input: Claim: Changing one's diet has no impact on the microbiome composition. [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: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: The plot projecting the Liolaemus phylogeny onto the body size morphospace (against time since the root), based on ancestral node estimations using maximum likelihood [66] is shown in Fig. 3 (see legend for details), and was built using the R package ‘phytools’ [67]. We finally investigated the influence of body size on macroevolutionary lineage diversification in Liolaemus. We employed the phylogenetic likelihood-based approach Speciation and Extinction (QuaSSE) implemented in the R package ‘diversitree’ [68]. This method fits evolutionary models based on the distribution of extant characters (body size) on a phylogeny, under the assumption that diversification follows a birth-death process and that a species can be characterized by its mean value of the measured trait, which affects diversification through its effect on the speciation-extinction rates (where rate of speciation is λ, and the rate of extinction is μ, see [69]). Evolutionary models are fitted by adding a ‘drift’ or ‘directional’ parameter (φ), which describes the deterministic (or directional) component of character evolution. That is, the expected rate of character change over time as a function of selection or other process which determines a directional tendency [68, 70]. Thus, this term does not refer to genetic drift specifically. After adding the drift term, the likelihood functions created by QuaSSE describe diversification by a constant, linear, sigmoidal, or hump-shaped function of log body size [68]. Identification of the best evolutionary model is performed via the AIC approach (see above). ResultsDiversification rates and evolutionary modelsThe results from the MCCR analysis, as shown by the lineage through-time plot (Fig. 2), suggest that lineage accumulation over time in Liolaemus differs from the pattern expected under the null pure-birth model of constant rate diversification (γ = −3. 84), but not significantly so (P = 0. 13). Although the shape of the LTT plot is not consistent with a traditional early-burst curve of diversification, two slight pulses of increased diversification rates followed by declines can be observed (however, both are contained within the 95 % confidence interval). One of these increases occurs approximately between 12–9 Mya, followed by a slight decline ~8-7 Mya.

Output: pass


Input: Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: For that reason, autonomous vehicles would need to stop upon all of these signals for safety reasons so as not to put traffic in danger. The following section will delve more deeply into the implications of the above on public acceptance. 5 Is the Ability to Communicate a Requirement for the Users to Vehicles? In terms of public acceptance, we must once realize again the effect of informal communication in “negotiating situations. ” In keeping with the aforementioned Sect. 1 of the Highway Code, drivers communicate in negotiating situations in order to resolve situations that are rule-governed in principle but where blindly following the rules would disturb traffic considerably. Example 1 If a vehicle is parking or idling in your lane, it is sometimes necessary to cross over to the opposite lane and, in extreme cases, even to cross a solid center line. In busy traffic, this requires coordinating with the oncoming vehicles. Apart from (unauthorized) crossing of the solid center line, the situation can be solved without a “negotiation” if oncoming traffic becomes sparse enough that the opposite lane can be used without vehicles communicating. The busier the traffic, the more likely a negotiation is necessary. How does the driver of an oncoming vehicle signal willingness to cooperate? First of all, by slowing down in order to leave room for you to cross onto the opposite lane. Yet in most cases slowing down alone is not enough of a signal because there are many reasons why a vehicle might keep a larger-than-usual following distance. That is why approaching drivers would usually signal this kind of offer by both slowing down and flashing their headlights. If the autonomous vehicle did not recognize this signal, it would be seen as a “traffic obstacle,” which would certainly not encourage public acceptance. Example 2 As shown above, autonomous vehicles will generally behave more cautiously than human drivers as they have a limited grasp of the context and informal signals. Yet they can also perform driving maneuvers that are normally not performed by a human driver. For one thing, they react more quickly than human drivers. Since they tend not to have a “moment of shock,” their reaction time is much faster. That is one reason why autonomous vehicles would cause fewer accidents. Furthermore, accident research has shown that many drivers do not make the most of their cars’ braking capabilities and, even when swerving, tend not to reach the physical limits based on the circle of forces.

Output: corroborate


Input: Claim: Changing one's diet has no impact on the microbiome composition. [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: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: A subsequent slight increase occurs ~5-4 Mya followed by a decline in the most recent phase of the clade’s history (Fig. 2). Fig. 2Tempo and mode of macroevolutionary diversification in Liolaemus lizards. The bottom plot shows the lineage through time (LTT) curve of species accumulation over time (solid line) and the 95 % (yellow area) to 50 % (red area) confidence intervals (note the most recent pulse is borderline). The phylogenetic tree in the background shows a maximum-likelihood phylogenetic reconstruction of ancestral body sizes (ln-transformed) along the branches and nodes of the tree, and the interspecific range is shown in the coloured bar with the frequency distribution of SVL of the entire genus. The top plot shows mean subclade disparity through time (DTT) for body size (solid line), compared with the median subclade DTT (calculated based on 10,000 simulations) of phenotypic evolution on the genus phylogeny under a Brownian motion model (dashed line). The grey shaded area represents the 95 % confidence interval of DTT range based on simulations of body size disparityFull size image The maximum-likelihood analyses of lineage diversification based on four candidate models identify the diversity-dependent linear model (DDL + E) as the best description of the estimated pattern of evolutionary diversification of Liolaemus (Table 1). This finding remains supported when the same models are fitted under the assumption that only 30 % of the ‘real’ diversity of the genus is sampled in the phylogeny, and therefore, these observations are unlikely to be an artefact associated with numbers of known and missing species. However, it is important to note that the ΔAICc values between the DDL + E and the Yule models are small – for the scenario based on the actual numbers of species known and sampled, the difference (1. 89, Table 1) is close to the threshold value of 2, which identifies well-supported models. The difference is much smaller (0. 68, Table 1) for the scenario that assumes 30 % of the real diversity, which indicates that both models are qualitatively similar.

Output: pass


Input: Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: Driver assistance systems such as automatic emergency brakes or emergency steer assist are intended to compensate for these deficiencies. On that basis, not only would an autonomous vehicle act more quickly in an emergency situation, but it would be able to take advantage of the physical limits of forward and lateral acceleration. How other road users will react is largely unknown at this point. The following section discusses a simple approach to solving this. 6 to Autonomous Vehicle’s Driving Errors? Closely related to the questions of interaction and negotiation discussed earlier is the issue of compensating for other road user’s errors. Apart from avoiding immediate collisions with swerving trajectories, accommodating other driver’s errors is normal in everyday road traffic. First let us clarify how “driving errors” is defined. From a human driver’s perspective, “driving errors” might include purely rule-abiding behavior that is not ideally adapted to the situation. For example, a human-driven car with right-of-way on a main road might let an autonomous vehicle from a side street turn first if the human-driven car wouldn’t otherwise be able to turn onto the side street. In other words, if the human driver yields despite having right-of-way, the autonomous vehicle would need the ability to recognize that reliably. Driving errors might also result from deficiencies in the autonomous vehicle’s repertoire of strategies for resolving special situations or might be caused by reaching system boundaries. autonomous vehicle operating in “traffic jam pilot” mode enters a safe state because the system’s boundaries have been reached. In other words, the vehicle slows down to a stop. Although slowing down to a stop is common in a traffic jam, the other road users will be confused at the least if all other vehicles are moving and only the autonomous vehicle is stopped. If the maneuver occurs fairly abruptly for a reason that other drivers do not understand, this can pose a danger. However, we should keep in mind that road users do not drive without errors either in current road traffic and other drivers usually compensate for their errors. This raises the question of which characteristics humans will ascribe to autonomous vehicles. Will they be seen as less competent than human drivers or treated as flawless machines? One goal of a deployment strategy must be to disseminate a positive yet realistic conception of autonomous vehicles among all road users. Then, and only then, will autonomous vehicles play an appropriate role in the traffic system.

Output: corroborate


Input: Claim: Changing one's diet has no impact on the microbiome composition. [SEP] Text: экономики и управления «НИНХ» (НГУЭУ-НИНХ) Новосибирский национальный исследовательский государственный университет (НГУ) Омская юридическая академия (ОмЮА) Омский государственный медицинский университет (ОмГМУ) Омский государственный педагогический университет (ОмГПУ) Омский государственный технический университет (ОмГТУ) Омский государственный университет путей сообщения (ОмГУПС) Оренбургский государственный медицинский университет (

Output: pass


Input: Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: An alternative model-fitting analysis based on the same four models, but using the package ‘laser’ [44], revealed identical results: the DDL model provides the best approximation to the observed pattern of species accumulation over time (results not shown). Table 1 Rates of species accumulation during Liolaemus diversification history based on multiple evolutionary models. Fitted models are pure-birth (Yule), birth-death (crBD), density-dependent logistic (DDL + E) and density-dependent exponential diversification (DDE + E). Best-fit of models based on (delta) bias-corrected Criteria (AICc)Full size table Tempo and mode of body size evolutionThe analysis of phenotypic DTT reveals that rates of subclade-level diversification in Liolaemus body size are consistently higher (positive) than expected under a Brownian motion model of evolution (MDI = 0. 23; Fig. 2). Therefore, Liolaemus subclades have extensively diversified in body size and converged to occupy overlapping regions of the lineage’s morphospace (Fig. 3). The DTT plot shows an overall tendency for relative disparity in body size to decrease over time, although there are multiple pulses of increases in diversification. There are two slight pulses during the Miocene (both within the 95 % CI calculated from simulations of body size disparity), one between approximately 15. 5-14 Mya, and one between approximately 10. 5-7. 5 Mya. More notably, however, a strong increase is observed during the Pliocene, in which the trend of body size relative disparity exceeds the 95 % DTT range of the simulated data (Fig. 2). Such high positive relative disparity remains through to the Pleistocene, when the model recovers an unusually high relative disparity peak between approximately 0. 6-0. 3 Mya (Fig. 2). Fig. 3Projection of the Liolaemus phylogeny into a morphospace defined by body size (ln-transformed, on y) and time since the clade’s origin (on x, in My elapsed since the root). Ancestral body size states are estimated using likelihood. The degree of uncertainty is indicated by increasing transparency of the plotted blue lines around the point estimates with the entire range showing the 95 % confidence interval.

Output: pass


Input: Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: In Germany, at any rate, it is commonplace to insist on one’s right-of-way so it would be unusual to attempt to merge without obtaining the other road user’s “consent,” as that would involve risking a collision. Traffic in the is characterized by more steady, lane-based driving. Aside from the official signals, informal signals play less of a role there. Traffic in China is—at least so far—marked by a low level of rule-abidance and by communication that is difficult for foreigners to interpret. Chinese drivers are masters of surprise, ignoring traffic rules and honking merely as a “friendly greeting” [16]. Communication between cars and pedestrians is especially critical. Pedestrians make themselves understood by gesturing with their hands, stepping onto the crosswalk, or waiting there until a car stops. What are the implications of this for the behavior of autonomous vehicles? For one thing, autonomous vehicles will predict whether a pedestrian intends to cross the street based on the pedestrian’s trajectory and acceleration. Apart from that, there are other modes of behavior that are more ambiguous, such as standing at a crosswalk without intending to cross (e. g. while having a conversation) or hesitating without making a clear signal of intending to cross. In all of these cases, the autonomous vehicle will need to stop for safety reasons. A consequence of this may be “unwarranted stopping” or the danger that children or teens catch on and begin to force vehicles to stop at crosswalks for fun. Yet the regulations and the actual behavior alike vary widely from country to country. For instance, Italy did not have a rule requiring cars to stop at crosswalks until recently. According to reports from visitors to China, it is only advisable to cross at a crosswalk there as part of a crowd because traffic is highly unlikely to stop for a lone pedestrian. While pedestrians who use hand gestures to communicate assume that their gestures are seen, they usually would not cross the street if an oncoming car did not slow down. 8 Means of CompensationIn the first stage, there is an easy antidote to the dilemma of robotic vehicles’ inability to communicate informally in “negotiation situations. ”Clearly and visibly marking autonomous vehicles as such would announce the vehicles’ uniqueness to the other road users and make their deviant behavior more understandable.

Output: corroborate


Input: Claim: Changing one's diet has no impact on the microbiome composition. [SEP] Text: Вавилова (СГАУВавилов) Саратовский государственный медицинский университет им. В. И. Разумовского (СГМУ) Саратовский государственный университет им. Н. Г. Чернышевского (СГУ) Сахалинский государственный университет (СахГУ) Севастопольский государственный университет (СевГУ) Северный (Арктический) федеральный университет им. М. В.

Output: pass


Input: Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: AbbreviationsAIC: Criterion BM: Brownian motion model crBD: constant rate birth-death model DDL + E: density-dependent logistic model DDE + E: density-dependent exponential model DTT: disparity through time EB: Early burst model HMM: hidden Markov model LTT: lineage through-time plot MCCR: : morphological disparity index m: metres My: millions of years Mya: millions of years ago OU: Ornstein-Uhlenbeck model QuaSSE: Speciation and Extinction SVL: snout-vent length 2009. 2000. 39. 96. 2001. p. 583–607. 67. 1953. 96. PubMed 502. 1944. 2003. 4. 104. 373. 45. 203. 7. 9. 7. 46. 7. 203. 71. 11. 9741. 35. 20. 78. 24. 68. 487. 207. 1993. 71. 8942. 102. 93. 57. 82. 57. 2006. 72. 1109. 50. 9. 23. 2002. 808. 1983. Book 2013. Book 12. 31. 20. 34. 26. 42. 2003. p. 356–80. 83. 92. 95. 84. 31. 25. 5. 11. 9. 23. 33. 2011. Book 2003. 2014. p. 567–72. 1997. 1986. 96. 1974. Book 308. 62. 105. 9. 2699. 29. 54. 409. 46. 85. 86. 28. 32. 32. 1997. p. 1–54. 64. 83. 9. 5. 1. Surface analysis showing the three inferred adaptive peaks (‘regimes’) of body size (large circles in blue, green and red), and the distribution of species body sizes clustering around these peaks (small circles). Figure S2. Surface analysis depicting phylogenetic convergences of body size in Liolaemus. The three colours (blue, green and red) represent the body size peaks shown in the S1 above. (DOCX 254 kb)Rights and permissions This is an 4. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. waiver ( applies to the data made available in this 10.

Output: pass


Input: Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: 9 of Communication for an of Information from Autonomous vehicles must generally be able to recognize and interpret other road users’ gestures and trajectories. In order to interpret them, they will need situational knowledge allowing them to put signals in the correct context. The “official” signals such as turn signals, the horn, and headlight flashing would probably suffice in most situations in order to convey information to other vehicles or road users. Kent Larsen’s research group at MIT proposed an interesting approach for communication between autonomous vehicles and pedestrians [17]. A prototype with a variety of sensors but only remotely resembling an actual car was fitted with a number of devices. Swiveling, blinking LEDs designed to look like an eye turn towards a pedestrian, signaling “I’ve seen you. ”Additionally, directional speakers point towards pedestrians and tell them that it is okay to cross the street. If any pedestrians are identified, color-coded LEDs in the wheels could change from green to yellow to red in order to acknowledge pedestrians or warn them (Fig. 7. 13). Fig. 7. 13:. Image rights: Nicholas Pennycooke, Changing places group, MIT (copyright belongs to author)Full size image Another signal that autonomous vehicles can use to communicate with others is “explicit driving. ”For example, if another vehicle would like to merge and the autonomous vehicle wants to cooperate, the autonomous vehicle should visibly slow down in order to accentuate the gap and assure the other driver that it is safe to merge. Communication and informal rules are in constant flux. To illustrate this point with a verbal example, take the expression that something “sucks. ” Not too long ago, this word was understood as a direct sexual reference, provoking understandable consternation when young people began generalizing it. Similarly, the non-verbal signal “thumbs up” recently spread to countries where it was unfamiliar due to its iconic use in social media. From one angle, that means that autonomous vehicles will need to periodically learn new signals of non-verbal behavior, for instance by regularly updating their visual “vocabulary” along with associated meanings. Yet another effect can also be anticipated. The increased presence in road traffic of autonomous vehicles practicing a distinct, particularly rule-based set of behavior could also encourage other road users to adopt such behavior. This would make traffic more standardized, but not necessarily more efficient.

Output: corroborate


Input: Claim: The GDPR in the EU regulates how companies collect and process user data. [SEP] Text: Commission published a legislative proposal in July 2023, the adopted its position after the first reading in April 2024, and the Council agreed on its general approach on 13 June 2024. Unfortunately, the reform features deeply troubling elements. It seeks to mainstream a controversial Irish approach to dealing with data protection complaints, namely “amicable settlements” that the (DPA) can in some cases decide that have been struck between individuals and digital corporations. Those “settlements” are not always to the benefit of data subjects. Further, and rather problematically, the reform foreshadows the end of the principle of proximity, which was a key part of the GDPR’s enforcement design. Gutting – or at least eroding – the proximity principle should ring alarm bells for anyone concerned with effective judicial remedies in the EU. In this blog post I explain why eroding the principle of proximity and further advancing questionable “amicable settlements” by the Irish DPA would be a catastrophe for European data protection law. In short, the legislator should stop both projects – gutting proximity and mainstreaming “amicable settlements” – in the final trilogues. The principle of proximity allows individuals faced with a GDPR infringement to lodge a complaint with a data protection authority (DPA) of their choice: the authority of the place of the alleged infringement, of their place of work, or of their habitual residence. If the DPA with which they lodged the appeal happens not to be the one competent to take a decision on the case because of the processing’s “cross-border” nature, the local authority will forward the complaint, triggering what is known as the “one-stop-shop” procedure. In these situations, it is the DPA of the of the main establishment of the entity that takes the lead in deciding the case. A data subject living in Brussels who has a problem with Instagram, for instance, can lodge a complaint with the Belgian DPA without having to worry about whether Instagram is based in Belgium or not. DPA shall know that Meta has its main establishment in Ireland and invite the Irish DPA to lead the path towards a decision on the case. That is, actually, only one aspect of the principle of proximity. There is a second important element, connected to the broader picture of effective judicial remedies in the GDPR.

Output: contrast


Input: Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: Skip to main content Explore journals Get published About BMC My account Search all BMC 0. 95. Links are drawn to highlight the topological differences. Colours correspond to species clades (see Table 1) as follows: black: outgroups, turquoise: percula, grey: Amphiprion latezonatus and A. chrysopterus (monospecific lineages), orange: clarkii, blue: akallopisos, rose: Australian, green: ephippium, yellow: polymnus, brown: Indian. A coloured legend, which repeats this information for the major clades, is located at the right of the figure. Full size imageFigure 2Homoplasy excess test. Panel (a) shows the distribution of the bootstrap support for each node which showed a homoplasy excess during the removal analysis. Each boxplot shows the null distribution of bootstrap values for the node and the red dot shows the bootstrap value of the node when the taxon of interest (name on the right) has been removed. Nodes names as given on the left correspond to that of panel (b) which shows the consensus tree of the nuclear phylogeny. There, nodes and taxa which showed up as outliers in the analysis have been coloured in orange. Species used as outgroup are shown in grey. The size of node labels has been altered to facilitate reading. Full size imageHybrid signalWe recover an excess of homoplasy at nine nodes of the clownfish phylogenetic tree (Figure 2). Removing either A. ocellaris from Bali or A. percula from Guinea increases the BS of Node 41 (Figure 2), which suggests genetic exchange between members of the percula clade. We find that the “black” specimen of A. clarkii collected in Bali likely originated from hybridisation between other members of the clarkii group (node 37, Figure 2). We measure further increase of BS at three different nodes (6, 16 and 17, Figure 2) when individuals of A. chrysopterus from either Moorea or the are removed. Moreover, the BS of node 17 increases when A. akindynos is removed, which suggests several hybridisation events between A. chrysopterus and species of the Australian and polymnus groups occurring around the timeframe of node 16 and 17.

Output: pass


Input: Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves. 1 MotivationThis paper aims to quantify the effects of autonomous driving on the traffic management level. This involves developing a model of autonomous driving that makes it possible to use human-controlled and autonomous vehicles with only minor modifications. This is important with regard to defining how the instruments of traffic management need to be developed in the future to enable them to handle autonomous vehicles in the transportation system. Of particular interest in this context is mixed traffic, in which normal and autonomously driving vehicles interact with each other. This will presumably be the normal state of affairs on roads for quite some time even after the introduction of autonomous vehicles; it is, therefore, of great practical significance to gain a good understanding of precisely this situation to predict and prevent any systemic effects that may occur. Since such vehicles do not yet exist, portions of the following observations must be regarded as an initial appraisal of possible developments presented as a scenario. However, modeling of human drivers is likewise far from complete, so the focus in this paper will be on establishing consistent modeling. The objective of the modeling presented here is to describe, as far as possible, human and autonomous vehicles with the same model, distinguished only by the different parameters used. A good example of this is the distance to the vehicle ahead expressed in terms of the time gap: an autonomous vehicle can achieve times of 0. 3…0. 5 s [1], whereas vehicles driven by humans are legally required to maintain a distance of at least 0. 9 s (in Germany). The legal recommendation is actually 2. 0 s, but this is seldom maintained except when traffic volumes are low. In heavy traffic, the value is often significantly lower; the figure for heavily traveled autobahns that occurs most often is 1. 1 s (see Fig. 15. 3), with an average value of 1. 4 s. If drivers complied with the legally stipulated specifications, traffic on many roads would come to a standstill much earlier than is currently the case. This paper builds on the papers by Friedrich [2] and Pavone [3] in this book. While [2] describes the general effects of autonomous vehicles on the transportation system, this paper addresses the modeling of autonomous and human-driven vehicles as well as the effects of autonomous vehicles on traffic management.

Output: corroborate


Input: Claim: The GDPR in the EU regulates how companies collect and process user data. [SEP] Text: In contrast, during the same year, in the context of the one-stop-shop it only issued 18 draft decisions and adopted even less final decisions (12 in total). The DPC has in the past discreetly managed to convince the members of the (EDPB) that all this is compatible with the GDPR, by accepting, as a small concession to their peculiar practices, to mark the withdrawal of the complaint by adopting a formal notification. The persistent problem is, however, that – coming back to the example above – this decision to regard the complaint as withdrawn is taken in Ireland, by the Irish DPA, and can only be contested in front of Irish courts. Thus, if the Irish DPA would, for instance, decide that a complaint I lodged with the Belgian DPA is closed (perhaps because I did not react on time, maybe because I did not understand their language, possibly because I never knew my GDPR rights depended on me opening unsolicited communications from distant authorities), and I would wish to contest the validity of their decision, I would have to travel to Ireland and figure out how to launch proceedings there. Just like in 1998. Just like before the GDPR and the EU Charter of Fundamental Rights. This creates a situation in which access to data protection remedies depends on the establishment of the data controller or processor. This goes to the disadvantage of every EU-based Meta user who does not live in Ireland, impacting millions of EU residents. We may end up in a situation which basically caters to large digital companies, rather than individuals. If it goes ahead, the reform threatens the credibility and effectiveness of data protection complaints against large social networks. This goes very much against the idea of the digital single market, and further erodes trust in the free flow of personal data. And, de facto, it grants a joker to all US-based companies that have their main establishment in Ireland. Irish coffee a-go-go The currently discussed legislative proposal on enforcement by further harmonising the procedures related to “cross-border” personal data processing should put an end to existing ambiguities and problematic developments in DPA practices, and make sure that the GDPR is fully respected. Commission fast-tracked its text and published it without even carrying out an impact assessment, arguing that no assessment was necessary because the initiative would not affect the rights of data subjects as set out by the GDPR.

Output: contrast


Input: Claim: Adaptive radiation explains the diversification of species in new environments. [SEP] Text: We also find evidence of genetic exchanges between members of the Indian group at nodes 20, 22 and 23 (Figure 2). Finally, we identify gene exchange between the two A. sandaracinos individuals at node 13 and A. pacificus, but the effect on bootstrap values is relatively small. Identification of recent hybrid parental speciesWe infer the phylogenetic trees of mitochondrial (Figure 3) and nuclear (Figure 4) markers for all sampled clownfish species augmented with the additional sequences of the supposed hybrid individuals. The cloning procedure allowed us to separate the two parental BMP4 sequences of each hybrid individual analysed (clones 1 and 2 in Figure 4). As expected, we find that the BMP4 sequences from those hybrid individuals that were cloned do not cluster by individual species but are separated among parental clades (Figure 4). We deduced the maternal origin of the putative hybrids with the mitochondrial phylogenetic tree. We find that both hybrids 2 and 3 inherited their mitochondrial genes from A. akallopisos. The plastid sequence of hybrid 1 is similar to A. perideraion and A. leucokranos, as already proposed [20], derives from A. chrysopterus. Moreover, in the mitochondrial tree, A. leucokranos clusters with A. chrysopterus sampled from the Solomon Islands, the origin of A. leucokranos (Figure 5). The maternal origin of the four hybrid individuals is highly supported in the phylogenetic tree with posterior probabilities of 1 on the respective ancestral nodes (Figure 3). The nuclear tree confirms those results and each individual has one of the cloned sequence that clusters with the same species as in the mitochondrial tree (Figure 4). The position of the other clone provides information on the paternal origin of the hybrids. A. leucokranos clusters with high posterior probability with A. sandaracinos and hybrid 1 and 3 both cluster with A. perideraion. The other hybrid individual (hybrid 3) clusters with any of A. pacificus, A. sandaracinos and A. akallopisos, but low posterior probabilities forbid any definitive conclusion. Nevertheless, we do not find clear evidence of recombination events amongst the cloned sequences, suggesting that we sequenced individuals deriving from recent hybridisation events. Figure 3Mitochondrial phylogeny of hybrid individuals.

Output: pass


Input: Claim: Autonomous driving technology aims to reduce human error in transportation. [SEP] Text: Such a modeling approach does in any case lead to a very broad spectrum of \( \tau \) values, just as is observed empirically. A second point in which a human differs from a machine is what is known as the action point mechanism [22]. Strictly speaking, a human driver cannot be described by a differential Eq. (15. 1). Rather, a vehicle is controlled through correction of the acceleration (accelerator-pedal position) at irregular time intervals, as shown in the example Fig. 15. 4. Fig. 15. 4Acceleration as a function of time with a human driver. It can be seen that the acceleration changes erratically at the action points. Between the action points, it remains nearly constant. The data was recorded in a “drive” by the author with a driving simulator; similar images can be found in all data records with sufficiently accurate measurement of the acceleration or accelerator and brake pedal. Image rights: copyright resides with authorFull size image The time gaps between successive action points also demonstrate a very broad distribution, with values between 0. 5 and 1. 5 s. Here there is evidently another modeling approach for traffic safety questions—if the time between two action points becomes very long, a critical situation can arise. In normal cases that does not occur, however, and there are only minor variances between a model based on Eq. (15. 1) and a model in which the action points are explicitly used [23]. In particular, the action point mechanism alone does not lead to a wide distribution of gaps between the vehicles. This too is demonstrated in the example used in Sect. 15. 2 of the chain of vehicles following a leading vehicle. An evaluation of the gap measured (in the simulation), here as a function of the number of the following vehicle, shows that in most cases an autonomous vehicle follows the leading vehicle with significantly less variance—in spite of the sometimes extremely volatile behavior. A representation of this is found in Fig. 15. 5. Fig. 15. 5Gap size behavior for human and autonomous vehicles. The graphic shows the average gap and the 25th and 75th percentiles, in each case as a function of the position in the chain. The upper curve is for the model of the human driver and the lower one models a chain of autonomous vehicles. Image rights: copyright resides with authorFull size image Thus the models used in this 0.

Output: corroborate