Two-thirds of the cataloged genes were assigned to bacteria, followed by eukaryotes (5.4%), archaea (1.1%), and viruses (0.69%). Genes encoding proteins involved in repairing UV-induced DNA damage and aerosolization of cells were ubiquitous across samples, and appear as fundamental requirements for the aeolian lifestyle, while genes coding for other important functions supporting the aeolian lifestyle  varied among the communities sampled.Atmospheric transport is a major vector for the long-range transport of microbial communities, maintaining connectivity among them and delivering functionally important microbes, such as pathogens. Though the taxonomic diversity of aeolian microorganisms is well characterized, the genomic functional traits underpinning their survival during atmospheric transport are poorly characterized. Here we use functional metagenomics of dust samples collected on the Global Dust Belt to initiate a Gene Catalogue of Aeolian Microbiome (GCAM) and explore microbial genetic traits enabling a successful aeolian lifestyle in Aeolian microbial communities. The GCAM reported here, derived from ten aeolian microbial metagenomes, includes a total of 2,370,956 non-redundant coding DNA sequences, corresponding to a yield of ~31 × 10 Atmospheric dust loads are concentrated in a broad region extending from the west coast of North Africa, through the Middle East, into Central Asia (approximately 10 to 30°N), deemed the “Global Dust Belt”3 , the back-trajectories of the air masses sampled  is poorly understood, but must be controlled by a set of inherent functions conferring resistance to transport in a dry air medium or particulate Aeolian dust. We provide here the first attempt at describing the Red Sea Aeolian microbiome from a functional metagenomics perspective, and reviewed the literature on the functions postulated to be important in supporting successful Aeolian microbial transport to propose a set of targeted genes that help define the Aeolian lifestyle. As a consequence, a set of functions was selected to be analyzed in the airborne metagenomes, these functions are aerosolization13, allowing microbes to be entrained from surfaces  to the air facilitated by gas vesicles inside the cells, aerotaxis14 and chemotaxis15, which are the movement of microbes under the influence of oxygen or chemical gradient that allow them to be positioned at the surface of their habitat, UV radiation16 and heat resistance17, which function in repairing the DNA damages induced by UV radiation and thermal stresses, germination18 and sporulation19, where the cells can form spores, which allow them to survive desiccation and exposure to UV radiation and germinate under harsh conditions, and biofilm formation20 that enable the microorganisms to attach to surfaces such as dust particles. We propose here this set of functions as a parsimonious set of traits that delineates capacities that, in combination, allow biological particles to survive atmospheric transport.However, current understanding of the Aeolian microbiome focusses on quantifying loads and fluxes and characterizing the identity of the microorganisms transported21. The quest to characterize microbial life and gene function and diversity in the ocean has continued through global expeditions, such as TARA Oceans22 and the Malaspina Circumnavigation Expedition23 and global assessments of soil microbiota24. However, this powerful approach (whole genome shotgun sequencing) has been applied rarely, to the best of our knowledge, to describe aeolian microbiota, using samples of aeolian microorganisms indoor25 and outdoor built urban26 environments, compared to hundreds of (WGS) studies published on land and oceans27. Likely, the fact that no metagenomic studies have targeted the aeolian microbiome over the Red Sea thus far is attributable to the very low density of microorganisms in the atmosphere 28, about 6 and 8 orders of magnitude lower than in soil (1012 to 1015 cells m−3)29 and seawater (1010 to 1013 cells m−3)29, respectively. In turn, the amount of DNA typically required to retrieve high-quality metagenomes (~500 ng) would require sampling 6 million m3 of atmospheric air25 in contrast to much lower sample sizes of seawater (~1 liter)30, or soil (~5 g)31 required to achieve this DNA content for sequencing. The large air sample size required to recover enough DNA7 for whole genome shotgun sequencing confounds the analysis of Aeolian microbiomes thus far27, as conventional techniques collecting dust on filters would saturate large-sized filters before a fraction of the required volume would be filtered. Indeed, metagenomic databases (e.g. EBI Metagenomics)32 lack information on aeolian microbes, despite their interest to unveil the functional strategies underpinning survival under harsh conditions, capabilities for attachment and survive long-distance transport on dust particles, and impacts on organisms, including humans.Metagenomic approaches describing the functional properties and diversity of microbiomes were first introduced by the Global Ocean Expedition, where massive shotgun sequencing was applied to describe microbial and gene diversity in plankton communities in the Sargasso SeaHere we provide a first functional genomics assessment of the Aeolian microbiome in the “Global Dust Belt”, with a focus on the genetic traits enabling microbial aeolian lifestyle. We do so by initiating a Gene Catalogue of Aeolian Microbiota (GCAM), based on 10 metagenomes assembled from samples collected over the Red Sea, in the center of the Global Dust Belt. This was enabled by applying of an approach yielding high-quality metagenomes with samples that containing very small DNA amounts (about 5 ng DNA), 100 times smaller than those conventionally required for metagenomics studies, of the aeolian microbiota sampled over the Red Sea. We then interrogate the gene catalogue to detect a parsimonious set of genes coding for functions hypothesized to play a significant role in allowing aeolian microorganisms to survive atmospheric transport.−3 and bacterial loads ranging from 109 × 103 to 212 × 103 cells m−3 compared to the single sample collected from an air mass transported by winds originating from the SE (5 µg m−3 and 288.89 103 cells m−3)  within the global dust belt Fig. . Samples−3) Fig. , which im−3 Fig. .−1), which produced a total of 2,543,974 redundant coding DNA sequences , results are yet to be reported. Moreover, we were unable to find published metagenomes for soils sampled within the Global Dust Belt. Hence, both our global and regional comparisons focus on the ocean and the Red Sea, respectively.An overall total of 68 Giga base-pairs (Gbp) of DNA were sequenced from the ten metagenomes (6.8 Gbp sample36 (≤100 m) were affiliated with bacteria (59.6%), archaea (4.16%), viruses (6.14%), and eukaryotes (0.46%). Hence, the Aeolian microbiome in the “global dust belt” sampled over the Red Sea is highly enriched in eukaryotic genes (>10-fold higher) compared to the underlying pelagic microbiome. Variability within the sampled aeolian metagenomes was also present. For instance, a sample collected in fall 2016, encompassing an air mass from the NW (KAUST_080) had the highest eukaryotic proportion (21.63%), while KAUST_025, collected in spring 2016 from an air mass from the NW had the lowest  of the genes were assigned to bacteria, followed by eukaryotes (5.4%), archaea (1.1%), and viruses (0.69%). In comparison, half of the total coding genes in surface water metagenomes from the Red SeaAscomycota and Arthropoda genes dominated the sequences assigned as eukaryotic coding genes in the ten aeolian metagenomes, while Actinobacteria, Proteobacteria, and Firmicutes dominated the bacterial sequences, whereas Euryarchaeota and Thaumarchaeota dominate the archaeal sequences , diatoms, in a sampling day when a phytoplankton bloom was ongoing in the bearing of incoming wind , backward trajectories or sampling season  of the gene catalogue  of pelagic metagenomes from the Red Sea were hypotheticals, suggesting a higher number of novel gene families in GCAM. The taxonomic assignment of sequences to bacterial phyla might be correlated to soil microbiotaind Fig.  indicateson Fig. .40 to further explore the functional assignment of the metagenomics data, in particular, the presence and abundance of aeolian lifestyle-related genes were investigated among the ten metagenomes using their KEGG IDs Ds Table . For robmes Fig. . Generalmes Fig. . Genes e16, was present across representative of all three domains of life 44, germination (gerQ and yaaH)45, thermal resistance (HSP70 and HSP90), and sporulation (SpoIVFK and spoVK)43. These were particularly enriched in communities sampled from air masses following a NW trajectory. However, sporulation-coding genes were also enriched in bacteria sampled from air masses transported from the SW. Aeolian bacteria were also enriched in genes coding for biofilm formation, facilitating attachment to dust ife Fig. . In cont26, which are enriched about 100 times in microorganisms relative to outdoors and are not representative of the microbes transported across the atmosphere, but of those emitted by humans in indoor habitats and cities. Whereas microbiomes have been studied across a range of habitats46, including thousands of published metagenomes from soils, ocean, and plant and animal halobionts27, our results provide the first metagenomics assessment of the aeolian microbiome and the corresponding gene catalogue. Aeolian microbial metagenomes were rich in unique genes, with a yield of 30.9 × 106 predicted genes per Tera base-pair of DNA sequenced, resulting in a total of 2.37 million non-redundant coding DNA sequences contained in the GCAM. The aeolian communities were dominated with Actinobacteria, Proteobacteria, and Firmicutes of the prokaryotic genes and Ascomycota of the eukaryotes, which is in line with findings in other bioaerosols studies50. Moreover, these communities were variable, likely a consequence of the diverse sources of the microorganisms present, and highly enriched in eukaryotic genes compared to the underlying seawater surface microbiome. The application of massive sequencing approach revealed that the aeolian microbiome of the Global Dust Belt is a rich source of novel genes sequences given that over half a million predicted genes were not anchored with functions based on current reference databases encompassing gene sequence from diverse biomes. Importantly, our study emphasizes that microbial aeolian lifestyle might be dependent on the prevalence of key genetic features enabling long-range dispersal, resistance to harsh atmospheric conditions and extended non-vegetative periods, which differs significantly from a planktonic lifestyle. In addition, genes encoding proteins involved in repairing UV-induced DNA damage and aerosolization of cells were ubiquitous across samples, and appear as fundamental requirements for the aeolian lifestyle. That is based on the fact that the Red Sea atmosphere is exposed to intense UV radiations that causing DNA damage. Moreover, the high atmospheric dust concentrations in this region plays an important factor in either scatter or absorb the UV radiation, where the attached microbes to the dust particles could be affected and genes such as UVrA are needed to repair the UV-induced damage54. Although no metagenomes of soil-borne microbes sampled in the global dust belt are available, we compared the relative abundance of genes selected with those in soil metagenomes sampled in Alaska55 and Australia56. We observed that the relative abundance of Chemotaxis, HSP, Sporulation, and UV resistance genes in soil metagenomes were depleted in the soil samples compared relative to the aeolian bacteria metagenomes reported here. However, we caution that this comparison is weakened by the fact that the soils available are unlikely to act as sources for the airborne bacteria sampled here and the limited number of airborne metagenomes analyzed here (n = 10) precludes robust comparisons.The results presented here provide, by solving the challenge of applying massive sequencing approaches at the low DNA concentration of open-air aerosol samples, a pioneer analysis of the Aeolian microbiome. The few aeolian microbiomes published thus far refers to indoor and built outdoor urban samples57, and in turn, long residence time and transport of the aeolian microbiome. The lack of desert soil metagenomic microbiomes precluded comparison of our datasets with metagenomes from soils that can be putative sources of organisms to the atmosphere in the Global Dust Belt. Aerosolization, aerotaxis, biofilm formation, and UV-damage DNA repair genes were comparable, in standardized abundance, with Red Sea metagenomes, further suggesting the Red Sea to be a likely source of microorganisms fit for aeolian lifestyles.Our metagenomics analyses of Aeolian microbiome demonstrate the enrichment of particular functional traits likely associated with the aeolian lifestyle, many of which are overrepresented relative to ambient microbial communities in pelagic waters of the Red Sea. In particular, sporulation genes were greatly enhanced in the aeolian microbiome compared to Red Sea plankton community, along with chemotaxis, germination, and heat-shock protein genes—all facilitating survival microbes in harsh environmentset al.58 report seasonal differences in community structure of Aeolian communities sampled in China, whereas Park et al.59 report no seasonal difference over Japan.Our results do not suggest evidence of major differences in metagenomics composition of the microbes, for the targeted genes, depending on season, although the small sample size (e.g. only 1 metagenome collected in summer), involves low power in detecting such differences. Whereas no prior study of functional genomics of Aeolian dust is available, studies of community structure, using amplicon sequences, do not consistently report changes in community structure. For instance, Li These findings not only have implications for conservation genetics and forensic microbiology but also human health, as they imply that microbial communities that settle on sand storms carry functional traits beneficial for their large-scale dispersal, that may propagate microorganisms interacting with humans and animals, such as pathogens, across vast distances.http://www.mcvsa.com/Productes/Atm%C3%B3sfera/CaptadordealtovolumenMCVCAVAmb/tabid/114/Default.aspx) that have been preserved in 4% HCl for 12 h. Filters and filter holders set were put inside clean plastic zip bags and transported to the sampler. Figure http://www.mcvsa.com/Productes), equipped with TSP cut off inlets at a flow rate of 20 m3 hr−1 over periods of 24 hours to one week. Air was sampled through the inlet by means of an in-built pump. The ambient air was filtered to collect the suspended particles on quartz fiber filters . The high-volume sampler on board the research vessel was equipped with a weather vane, which would switch off the pump and thus cease sampling immediately whenever the sampler was downwind of the ship’s exhaust. The mass concentration of TSP was determined by weighing the filters before and after sampling and expressed as µg per m3. Aeolian dust samples were collected at two locations: a land-ocean interface and an offshore regions of the Red Sea from December 2015 to November 2016  before use. To grip the filters, sterilized tweezers were used. Filters were placed in filter holders (circular filter holder for 15 cm diameter filters)  (6 Table. . A new c6 Table. .https://ready.arl.noaa.gov/), which is a complex atmospheric system for computing simple air particle trajectories as well as complex transport, dispersion and deposition simulations using archived meteorological data. The meteorological data obtained through the Real‐time Environmental Applications and Display System and archived in the Global Data Analysis System (GDAS1) to calculate the source of the air being sampled backward up to 120 hours and on three height levels  using metrological data of the archived analysis  were cut into small stripes and 5 ml of lysis buffer  and NaCl) was added to the tubes containing ca. 100 mg of 0.1 mm glass beads  and ca. 100 mg of 0.1 mm zirconia beads . To each tube, of 1 mg/ml lysozyme  was added and mixed. By inverting every 15 minutes, the tubes were incubated at 37 °C for 30 minutes. After incubation, 205 μl of 20% SDS and 10 μl of proteinase K  were added and mixed thoroughly for incubation at 55 °C for 2 hours with inverting the tubes every 30 minutes. Then, 5 ml of phenol-chloroform-isoamyl alcohol  was added followed by centrifugation at maximum speed for 10 minutes. After spinning, white interference layers were visible and the supernatants were transferred to fresh microcentrifuge tubes leaving the interference behind. To remove the phenol, 5 ml of chloroform-isoamyl alcohol  was added to each tube and centrifuged for 5 minutes in a microcentrifuge. The supernatants were transferred to 10,000 MW cutoff Amicon Ultra centrifugal filters  and centrifuged in a swinging rotor for 15 minutes. Filtrates in the lower tubes were discarded and 5 ml of 10 mM Tris-HCl was added to each tube and centrifuged until the volume reduced to <250 μl. For the second time, filtrates were discarded and 10 mM Tris-HCl was added to each tube to reach 250 μl following mixing. The concentrated volumes in the upper tubes containing the extracted DNA were transferred to clean 2 ml Eppendorf tubes and quantification of DNA was done using Qubit dsDNA HS (High Sensitive) Assay Kit  and Promega® GloMax-Multi Detection System . For the control protocol, a new clean filter was used as the negative control, where DNA concentration was below detection limit. The dust filters that used in the study of Yahya ion Fig. .60 with the following parameters: ILLUMINACLIP::4:30:10 LEADING:20 TRAILING:20 SLIDINGWINDOW:4:20 MINLEN:60. The internal sequencing standard PhiX 174 was subsequently removed by mapping the quality-trimmed reads against the PhiX 174 genome using Bowtie2 v2.2.4561 with default settings. At each stage, the quality of read sequences was assessed using FASTQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).Ovation Ultralow library systems V2  were used to prepare the aeolian metagenomic libraries due to the typically low DNA concentration present in atmospheric dust samples. This kit produces high-quality libraries for next-generation sequencing (starting from 10 pg of DNA) without the need for pre-amplification and decreasing the PCR artifacts. Briefly, genomic DNA was normalized to 5 ng (which was the minimum DNA present in one sample) in a final volume of 120 µl and was sheared to an average size of 400 bp with an M220 ultrasonicator . Sheared DNA samples were used for paired-end indexed library construction using Ovation Ultralow library systems V2 , according to the manufacturer instructions. Most of the fragments were recovered because no size selection was applied. Thirteen PCR cycles and Illumina adapter-specific primers amplified the DNA fragments. AMPure XP beads  were used to purify the libraries. The quality and concentration of amplified products were assessed by Bioanalyzer , which showed that all samples had a similar peak shape, and Qubit  which showed that all the samples were in a similar concentration range. The indexed libraries were pooled in equimolar concentration and subjected to Illumina HiSeq 4000 platform deep sequencing on one lane using Hiseq 3000/4000 SBS reagent 300-cycle kit  and bidirectional sequencing of 150 bp. Library preparation, multiplexing, and deep sequencing were performed at the Bioscience Core Lab facility at King Abdullah University of Science and Technology, Saudi Arabia. Raw read sequences were quality-trimmed while removing adaptor sequences, using Trimmomatic v0.323n = 10 samples) were assembled independently with metaSPAdes v3.9.062 using a kmer range of 21 to 127 while employing the error-correction mode and preset metagenomic options. The assembled contigs were then filtered to a minimum length of 500 bp followed by gene prediction using MetageneMark v3.3863. We retained protein-coding genes with a minimum length of 100 bp, yielding a total of 2,543,974 redundant genes, averaging (±SD) 254,397 ± 85,976 genes per sample (n = 10). Table The resulting high-quality paired-end reads for each dataset , resulting in the Gene Catalogue of Aeolian Microbiome (GCAM) with 2,370,956 non-redundant gene sequences. The resultant representative gene sequence clusters were subsequently functionally and taxonomically annotated using the online server DMAP61 with default settings. The resultant mapped read counts per gene (and sample) were normalized into a common metric of gene abundance—that is, fragments per kilobase of exon per million fragments mapped (FPKM) using Cufflinks and Cuffdiff65. Both the annotated gene catalogue and abundance matrix were then placed in DMAP for further interrogation including community and gene profiling for metabolic pathways of interest.A gene abundance matrix was generated by mapping the error-corrected paired-end reads against the Aeolian microbiome gene catalogue using Bowtie2http://www.cbrc.kaust.edu.sa/aamg/docs/DMAP_Documentation.html. Extended taxonomic and functional annotations of GCAM produced by DMAP annotation module are indexed for interactive visualizations and comparisons in DMAP compare module developed by extending the functionality of Metagenomic Reports (MetaRep) software66.DMAP has two modules one for annotation and another for sample comparisons, see DMAP documentation at Taxonomic assignments for genes are carried out using high throughput BLASTp comparisons to Universal Proteins Knowledgebase (UniProtKB) considering both best blast hit and the least common ancestor approach (LCA). The LCA based taxonomic assignment results are loaded to DMAP comparison module for interactive interrogation. Functional role assignments for genes are carried out using BLASTp to KEGG ortholog (KO) assigned sequences from KEGG database. KEGG orthologs are linked to KEGG enzymes, modules and pathways. In cases where no functional role assignments are available from KEGG, generic gene descriptions are obtained from UniProtKB BLASTp results. Gene Ontology and signature functional domain information is obtained through InterProscan analysis. A final gene information table produced based on DMAP based annotations is indexed and deposited to DMAP Compare module that links Taxonomy, KEGG Ortholog, Enzymes, Gene Ontology Pathways identifiers to their parent-child hierarchies for a deeper interactive analysis.Gene catalogue information table is submitted to DMAP compare module considering a weight of 1 or 0 to denote gene presence or absence information. Gene information tables are produced for each sample by extracting sample specific gene abundance estimate from the gene abundance matrix alongside annotations of corresponding genes from the common gene catalogue. These sample specific gene information tables are indexed and deposited to DMAP compare module similar to a gene catalogue but considering sample specific gene abundance estimates as weight.The compare function in DMAP comparison module allows users to select several samples and see comparative results as tables or visualizations such as heatmaps, bar graphs, Multi-Dimensional Scaling (MDS) and other interactive heatmaps, see DMAP documentation for a full overview and examples. For taxonomic profiling of samples, users can select a filter either 16S genes or universal single-copy genes (SCGs) from the list of filters provided. In case of samples with only protein-coding genes, it is best to select one of the filters from DMAP provided Universal, Prokaryotic or Eukaryotic single copy genes filters. It may be appropriate to initially look at all SCGs based results for selected samples and later choose a more appropriate one SCG as a proxy to 16S gene like profiling of samples. Users can increase or decrease the fold change control to clearly find any profound patterns in the results.An exciting feature of DMAP compare module is the ability to compare samples based on groups of genes required to activate a complete pathway module. Predefined groups of necessary or alternative KOs are compared with sets of KOs available from selected samples and an interactive heatmap is produced showing white cells for incomplete modules and red for complete modules across samples being compared. Clicking on a cell leads to KEGG Pathway module diagram showing sample specific genes highlight in red to describe the completeness of a module for a sample in question.Aalismail et al Supp info SREP-19-17724A


Significantly higher values were found for condylar volume in hypodivergent patterns (p = 0.001) and for the asymmetry index of the condylar volume in Class II patients (p < 0.05). The prevalence of relevant asymmetry was high for condyle height and volume (73.1% y 75.6% respectively). Higher height and volume values were found among men, Class III, and hypodivergent patients. Linear and volumetric asymmetries were more prevalent among men, Class III and hyperdivergent patterns.This study aimed to quantify the height of the mandibular condyle and ramus, condylar volume, and the asymmetry index in adult patients of different sex, skeletal class and vertical pattern using Cone-Beam Computed Tomography (CBCT), and to determine whether there were differences between these groups. The study used CBCT scans of 159 patients with a mean age of 32.32 ± 8.31 years. InVivoDental® software was used to perform both linear  and condylar volume measurements. Linear and volumetric asymmetries were calculated. There were not significant differences between right and left sides. The mean value obtained for condyle height was 7.27 mm, ramus height 42.3 mm, total height 49.6 mm and condyle volume 1907.1 mm Traditionally, the most commonly used have been two-dimensional (2D) radiographs, including posteroanterior (PA) cephalograms6, submentovertex7 and panoramic radiographs11.Different techniques including clinical examination, photography, and radiography have been used to assess mandibular asymmetry12 and Kjellberg’s13 methods being the most frequently used. The method proposed by Habets et al.12 is considered a reliable method for quantitatively assessing vertical mandibular asymmetry. According to these authors, asymmetry index values higher than 3% on panoramic images may be referred to as mandibular asymmetry12.In order to measure mandibular asymmetries quantitatively from panoramic radiographs, various techniques have been proposed, Habets’14, posterior crossbite15, cleft lip and palate18 and different occlusion patterns22 have been evaluated in several studies using Habets’ method12.The associations between mandibular asymmetry and different factors such as TMDs23. It has been noted that the reproducibility of vertical measures and angles will be acceptable providing the head is positioned correctly when taking x-rays. Habets et al.12 only considered asymmetries relevant when the asymmetry index was higher than 3%, which was associated with a 6% difference between sides when measured on panoramic radiographs.Panoramic radiographs present some vertical magnification and other distortion problems derived from projection geometry, which can lead to inaccurate evaluations and limit their diagnostic usefulness25, providing a three-dimensional (3D) reconstruction of anatomical structures with high resolution and no magnification26. Lim et al. compared the mandibular asymmetry index using panoramic radiography and CBCT finding low reliability and validity for panoramic radiographs, thus recommending CBCT27. Only one published study has evaluated condylar and ramal vertical asymmetries using CBCT in patients with different vertical growth patterns but with normal, Class I sagittal skeletal patterns28. Meanwhile, volumetric measurements of the jaw and the mandibular condyle have been reported in CBCT images of Caucasian patients30 and in patients with juvenile arthritis31. Only one study has described volume according to the vertical and anteroposterior skeletal pattern (in a Japanese population) but without taking into account the symmetry or asymmetry that subjects presented32.Cone-Beam Computed Tomography (CBCT) is an accurate and reliable method for assessing craniofacial structuresThe aims of this study were firstly, to quantify the height of the mandibular ramus and condyle, condylar volume and the asymmetry index in adult patients of different sex, skeletal class and vertical pattern using Cone Beam Computed Tomography (CBCT), and secondly, to determine whether there were differences between these groups.33.The study protocol was approved by the Ethics Committee for Research Involving Human Subjects at the University of Valencia, Spain (H1465893129760). Rights were protected by the Institutional Review Board. All subjects gave their informed consent to take part in the study. Any data that might disclose the identity of the participants have been omitted. This study was designed following guidelines established in the Helsinki declaration and the STROBE statementCBCT scans of patients attending the orthodontic clinic at the University of Valencia (Spain) between January 2015 and March 2017 were obtained from the clinic’s archives. All CBCT scans were taken for diagnostic reasons relating to dental treatment, so the patients did not receive any additional radiation for the purpose of the present study. A total of 195 patient’s records were selected by VPG and LVM. After receiving information about the study, a total of 189 patients were willing to take part. Six patients did not wish to participate for personal reasons. The positive response rate was 96.9%. Informed consent was obtained from all participants.All CBCT scans were taken using a Planmeca Promax 3D imaging device  and included images of the complete skull (field of view 20 × 19 cm) with a voxel size of 0.4 mm. The scans were taken with the patient’s head in its natural position and the lips and tongue in the resting position.Patients who were to undergo any dental treatment;Patients with a CBCT as part of their general dental records taken before the patient underwent any treatment;Caucasian patients;Patients with all dentition present from first lower molar to first lower molar on the contralateral side;Patients older than 25 years, no longer in mandibular growth.Inclusion criteria were:Patients with any craniofacial anomalies or syndromes;Patients with antecedents of any kind of trauma to the mandible;Patients with presence of any type of cross-bite and/or mandibular functional shifting caused by occlusal interferences;Patients with presence of any type of temporomandibular disorder (TMD).Exclusion criteria were:Power analysis showed that a sample size of 159 patients would provide an 80% probability of detecting a medium effect (f = 0.25) for differences in the asymmetry index between skeletal classes or vertical patterns using an ANOVA model at a confidence level of 95%.34.Anteroposterior skeletal class . Steiner ANB angle was used to classify patients by skeletal Class: Class I presenting values of 2 ± 2°; Class II presenting values >4°, and Class III presenting values < 0°35 .Vertical pattern: this refers to the vertical position of the mandible with respect to the cranial base: hypodivergent, normal, hyperdivergent. This was determined by Ricketts XY axis angleLateral cephalogram radiographs were extracted from the CBCT images using Dolphin Imaging software in order to classify the patients according to the following parameters:CBCT images were imported from the software InVivoDental® 5.1  and both linear and volumetric measurements were performed.12 as shown in Fig. 1 and O2) and a line was drawn through them (A-line). Another line, (B-line) was drawn from the most superior point of the condyle perpendicular to the A-line. Firstly, the distance between point O1 and the intersection point of A and B lines was measured, representing condylar height (CH). Secondly, the distance between O1 and O2 representing ramal height (RH). Lastly, the distance between point O2 and the intersection point of A and B lines was measured, representing total height (CH + RH). Vertical mandibular asymmetry indexes of the condyle, ramus, and condyle plus ramus were calculated using the formula developed by Habets et al.12: Asymmetry index (%): [(Right − Left)/(Right + Left)] × 100.Condylar (CH), ramal (RH) and total  were measured on both sides of each mandible using Habets’ methodCondylar volume (CVol), comprising the head and neck of the condyle, was measured as follows: on the sagittal view, the condylar area was delimited by identifying the lowest point of the sigmoid notch and a line was drawn through it, parallel to the Frankfort plane, which was constructed separately for each side by identifying right and left Porion and Orbitale points. The delimited condylar structure was isolated from the rest of the image using the software’s cropping tool. The threshold value was set based on the best visualization of the structure.3  repeated measurements of 100 CBCTs, with a 1-week period between the first and second measurements. Inter-observer reproducibility was evaluated by a second observer (VP), instructed and calibrated, who repeated the measurements of 100 CBCTs. Both observers redefined all the points before taking the second measurement, including the landmarks for sigmoid notch and Frankfort Plane. The two examiners chose their own threshold settings. Both linear and volumetric measurements were repeated. In this way, measurement error could be estimated, as well as intra- and inter-observer reproducibility. The Dahlberg formula (d) was used to estimate error, calculating the coefficient of variation (CV) as an indicator of relative error or measuring technique. The data obtained were entered on a spreadsheet, using Microsoft Excel 2011  and transferred to the statistical software package SPSS v. 22.0 for analysis.36 and Lin’s coefficient of agreement37 were used. The existence of differences in means was determined by the Paired t-test.To analyze the agreement between the measurements of the right and left sides, the Passing Bablok regression lineIndependent t-test was used to test differences in means of linear and volumetric measurements, and asymmetry index between male and female patients. One-way analysis of variance (ANOVA) models were used to study independently differences involving skeletal class or vertical pattern. Tuckey test was used as post hoc test. Differences in the prevalence of relevant asymmetries (asymmetry index >3%) were estimated using the Chi-squared test.To analyze the associations regarding asymmetry prevalence  of linear and volumetric measurements with the independent variables , a multivariate analysis by logistic regression was conducted with the Forward Selection Method.Level of significance α = 0.05 was set up for the analysis.The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.After applying the inclusion and exclusion criteria, the final sample consisted of 159 patients  with a mean age of 32.32 ± 8.31 years, ranging from 24.34 to 41.21 years.61 patients with skeletal Class I: 29 with normal vertical pattern (10 men and 19 women); 19 with hypodivergent pattern (8 men and 11 women); and 13 with hyperdivergent pattern (3 men and 11 women). Their mean age was 33.65 ± 12.39 years.54 patients with skeletal Class II: 22 with normal vertical pattern (12 men and 10 women); 16 with hypodivergent pattern (9 men and 7 women); and 16 with hyperdivergent pattern (7 men and 9 women). Their mean age was 33.13 ± 12.42 years.44 patients with skeletal Class III: 14 with normal vertical pattern (8 men and 6 women); 10 with hypodivergent pattern (6 men and 4 women); and 20 with hyperdivergent pattern (11 men and 9 women). Their mean age was 29.63 ± 11.91years.The final sample comprised the following:Sex distribution was found to be homogeneous, unlike vertical pattern distribution. The numbers of CBCT scans pertaining to hyperdivergent and hypodivergent patients were similar, and slightly higher than the number of patients with normal pattern.d of Dahlberg fell below 0.16 mm.Reproducibility results showed an intra-observer coefficient of variation (CV) of between 0.70% and 1.13%; the inter-observer CV ranged between 1.21% and 1.49. Intra and inter-observer error measurement method for No differences were found by the paired t-test in the means of condyle height, ramus height, total height and condyle volume between the right and the left side. Lin’s coefficient showed high agreement in all measurements except for condyle height, which was moderate Table . The PasLinear  and volumetric measurements (condylar volume) according to gender, skeletal class and vertical pattern are shown in Table Regarding asymmetry index for both linear and volumetric measurements according to gender, skeletal class and vertical pattern Table , statistThe 75.6% of the patients showed condylar volume asymmetry, and the 73.1% presented asymmetries in condylar height. The prevalence in ramus height asymmetry was 32.5%, this prevalence being 26.3% for total height.No associations were found between the prevalence of asymmetries (linear or volumetric) and sex, skeletal class or vertical pattern Table .Table 4PTable This study provides information about CH, RH, CH + RH, CVol, linear and volumetric asymmetries in adult patients of different sex, skeletal class and vertical pattern, in order to determine whether there are differences between these groups. Establishing the differences (if any) in mandible dimensions and asymmetries between patients with different skeletal characteristics could help our understanding of the etiology of mandibular asymmetries hence establish an accurate treatment plan.The sample was homogeneous regarding genders and skeletal classes. Only when making combinations between the variables, this homogeneity decreased. However, this was not considered a problem since the aim of the study was not to make associations between sex, skeletal class and vertical pattern variables. The only combination that exhibited heterogeneity was class I with hyperdivergent pattern (3 men and 11 women), which was not considered a limitation since the logistical regression conducted in the present study did not find significant associations between sex and the prevalence of asymmetries Table .12 and other subsequent research38, the present study took measurements from CBCTs rather than panoramic radiographs in order to avoid magnification and distortion problems17, and to provide high resolution26. All the CBCTs used in this study derived from patients’ dental records, and had been taken for other purposes , and not specifically for the study.Unlike the original study34 was used to determine skeletal class as in previous studies38. Ricketts’ XY axis angle35 was used to classify patients according to vertical pattern, unlike other studies that have used the mandibular plane32; this particular factor may constitute a difference between studies. The reason why the authors used Ricketts’ XY axis angle was to avoid mismatched planes resulting from mandibular plane differences between right and left sides, which could affect the measurements. Since Ricketts uses Gnation point, the authors considered this angle to be more reliable. Other studies on craniofacial structures classify the patients and make associations based on phenotypic clusters thus allowing clinical interpretation40. However, studies on mandibular shape and asymmetries classify patients according to vertical pattern and skeletal class like in our study, which allow more reliable comparisons between studies38The ANB angle17.In the present study, the patients selected did not present any type of crossbite, as it has been shown that the mandibular structure presents significant asymmetries within crossbite groups41. The mean age of all the patients in the present study was 32.32 years, implying that the mandible was no longer in growth, so that growth would not affect measurements.The mandible undergoes maximum growth months after the maximum pubertal outbreak of growth and continues for two years after the cessation of maxillary growth1.The patients selected for this study presented full permanent dentition from the first lower molar to the first lower molar on the contralateral side, as there is a proven relation between absence of the first lower molar and vertical mandibular asymmetry14.Patients with any kind of temporomandibular disorder (TMD) were also excluded from the study since TMD is associated with mandibular asymmetry42, Chilean43, Chinese44, and Japanese32 populations.The study sample only included Caucasian patients in order to avoid complications arising from ethnicity. This could explain why the results may not coincide with studies that investigated Turkish18.Patients with any craniofacial anomalies or syndromes such as cleft lip and palate were also excluded from this study, as these too are associated with mandibular asymmetry45, Simplant OMS46, MimicsTM44, Simplant Pro version 13.028, Analyze32.In this type of study, the accuracy and reliability of the imaging software used could be an important factor affecting the results. The present study used InVivoDental® software, unlike other investigations that have used Visualization Toolkit package, MIRIT, Matlab FastRBF Toolbox, Ortho Pro 2.0 softwareIntra- and inter-observer error was low and so reproducibility was high, the coefficient of variation value (CV) and the Dahlberg d-value being very low.47. It should be noted that, unlike the present study, some studies have used panoramic radiographs, and included patients with crossbite and mandibular shifting, factors that could affect the results28. Although differences were not significant, a high percentage of patients presented relevant linear asymmetries, condyle height asymmetry being particularly prevalent. The present study also found a high prevalence of volumetric asymmetry, although differences did not reach statistical significance. These results differ from others45. The differences could be due to the different inclusion criteria applied in the latter studies, in which these authors44 used a sample of patients diagnosed with mandibular asymmetry, and analyzed patients in mixed dentition45.Even though differences between right and left sides were found, they were not statistically significant, a finding that concurs with the results of other studies42 which found significant differences in the total asymmetry index between men and women. Contrarily to our results, other studies did not find significant differences47.Linear measurements were found to be higher among men than women, with greater difference in the ramus. These differences were statistically significant, a finding that partly agrees with Saglam study48. Regarding asymmetries in the different skeletal classes, Class III patients showed greater condylar asymmetries, while greater ramus asymmetries were found among Class II patients, although these differences were not statistically significant. Kasimoglu et al.47 also analyzed these differences, and like the present results did not find statistically significant differences. However, Saglam42 did find significant differences in total vertical asymmetry between skeletal classes. As for condylar volume, the present study found significant differences in asymmetry index between skeletal classes, which was higher for Class II patients, this finding being in contrast with Nakawaki et al. study which did not find significant differences among skeletal classes32. This disagreement may be owed to the ethnic differences between the samples, since their study uses a sample of Japanese patients unlike our study.In the present study, Class III patients showed higher linear measurement values, while other researchers have found the mandibular vertical dimension to be higher among Class II patientset al., who conducted a similar study to the present one, although the patient sample was smaller28. Hyperdivergent patients showed higher rates of asymmetry compared with the other groups, again without statistically significant differences; these results are in agreement with Celik et al.28. Significant differences in condylar volume were found between different vertical patterns. In this regard, our results agree with Nakawaki et al.32, who did find significant differences between hyperdivergent and hypodivergent patients, whereby the hypodivergent group presented higher volumes.Hyperdivergent patients showed lower linear values, although without statistically significant differences. These results agree with Celik 49By applying a multivariate analysis with logistic regression, the present study aimed to analyse the associations between different grades of asymmetry and the other studied parameters. Other authors have used different statistical tools such as geometric morphometric analysis to study asymmetries12. does not delimit the condylar head accurately, so condyle height measurement is an estimation of its length based on the location of cephalometric points. Secondly, although measurements taken from CBCTs are accurate, volume measurements may be subject to variations resulting from varying bone densities, which can affect the segmentation process50. Some 3D computational shape analysis methods are nowadays available to accurately assess anatomical aspects of the craniofacial complex51. These methods also allow to make precise superimpositions of the structures after performing semi-automated segmentation, thus asymmetries can be quantified53. De Dumast et al. have recently described the Shape Variation Analyzer, which consists in an interesting tool for this matter54. Further research would be interesting to analyze whether there are differences in means of results between the method used in the present study and the modern 3D segmentation analyses.The present study suffered certain limitations. Firstly, the method described by Habets et alDespite selecting patients without any craniofacial anomalies, previous trauma, crossbite, mandibular shifting, or any kind of TMD, the present study found a high prevalence of relevant asymmetry within the sample, which points to the importance of assessing asymmetries during diagnosis in order to ensure accurate and comprehensive treatment planning.Greater height and volume values were found among men, Class III, and hypodivergent patients. Linear and volumetric asymmetries were more prevalent among men, Class III and hyperdivergent patterns. Significant associations were found between condylar volume asymmetries >10% and hyperdivergent pattern, and between condylar height asymmetries >10% and skeletal class III.