Variety Of Textures Research Articles

A zircon trace element and Hf isotope geochemical study of syenites and carbonatite, exemplified by the Epembe alkaline carbonatite complex, Namibia

Abstract The Epembe Complex is one of the Mesoproterozoic (~1200 Ma) carbonatite alkaline complexes situated along the southern margin of the Congo Craton in northwestern Namibia. Nepheline syenites and minor syenites constitute the main lithologies, cross-cut by a calcite-carbonatite dyke. In order to constrain zircon forming-processes and magma sources, cathodoluminescence (CL) imaging combined with trace elements (including REE) as well as Hf isotope compositions of zircon grains extracted from one syenite, five nepheline syenite samples and one carbonatite sample are presented. Syenite zircons are generally unaltered and are characterised by positively sloping REE patterns in a chondrite-normalised diagram, with positive Ce anomalies. Syenite zircon further displays significant negative Eu anomalies attributed to earlier plagioclase formation and fractionation. These features are consistent with zircon formation in a magmatic environment. In the nepheline syenite samples, two zircon types are recognised. Type 1 zircon is magmatic, with homogeneous-grey, unzoned and oscillatory-zoned domains in CL, while type 2 zircon underwent low temperature fluid alteration and displays a cloudy appearance. Type 2 zircon is characterised by enrichment in LREE, Nb and Ti when compared to magmatic type 1 zircon. Carbonatite zircon displays a variety of textures and variable chemical compositions suggestive of the presence of both xenocrystal, altered and magmatic zircon. The Hf concentration and Hf isotope composition of type 1 and type 2 zircon are similar suggesting that zircon alteration did not affect the Hf isotope systematics. The similarity of ƐHf(t) values in zircon from syenite (+0.5 ± 0.4 to +1.5 ± 0.4), nepheline syenite (+1.6 ± 0.3 to +2.7 ± 0.5) and carbonatite (+1.5 ± 0.2 to +1.9 ± 0.1) is consistent with the melts having been derived from a moderately Depleted Mantle.

TEXTURES: The History and Art of Black Hair

TEXTURES: The History and Art of Black Hair

To feed or let eat! A scale of independence, exploration, and family to measure baby-led weaning as a complementary feeding approach.

This article reports the development and validation of a measure of parents' use of baby-led weaning (BLW). BLW is a child-centred approach to complementary feeding where the infant is allowed to eat whole foods (rather than purees) and explore a variety of foods and textures. To date, parents' use of BLW has been assessed using either single items or a wide variety of measures. In this study, exploratory and confirmatory factor analyses on independent samples supported three BLW subscales: independence, exploration, and family. The final 13-item scale showed adequate fit statistics and good reliability (χ2 (62) = 115.02, p < 0.001; CFI = 0.98; TLI = 0.98; RMSEA = 0.05; SRMR = 0.06; exploration a = 0.738; family a = 0.715; independence a = 0.809). In addition, the scale demonstrated good external validity and related in theoretically expected ways to an infant feeding-style measure and parent report of complementary feeding approach. This study was limited as it was mostly white parents, and the scale should be validated on a more diverse sample. Future research can use this scale to examine if BLW relates to infant taste preferences, parenting styles, and child eating behaviours to improve child nutrition and health outcomes.

Texture variability of clastic marine sediments across three major Harbours along the Egyptian Red Sea coast

Sediment textural variability and distributions of one hundred sediments samples across three major strategic Harbours, Quseir, Safaga, and Hurghada, along the Northern Red Sea have been assessed. Grain size parameters and cluster analysis have been applied to reveal indices of the marine environment, mainly the energy and hydrodynamic conditions, which controlled the mode of transportation and depositional settings. The three Harbours exhibited shallow marine environment, corresponding to siliclastic terrigenous flux, associated with carbonate sediments, which sustained biogenic fragments nearby reef and mangrove sites. Depositional settings were controlled by bottom topography, sediments sources, water depth, and closeness from shoreline. This was also coupled with the exposure degree to anthropogenic discharges that triggered sediment texture disturbance and impacted seabed habitat of Safaga Harbour. The latter is more affected by contamination than Quseir and Hurghada Harbours as its topography favored the accumulation of contaminants and acted as pollutant-traps. The C-M pattern suggested rolling as the main mode of nearshore sediments transportations across the three Harbours. Suspension transportation was recognized in Quseir Harbour with higher ratio compared with Safaga Harbour, while it was absent in Hurghada Harbour. Nevertheless, the investigated Harbours shared some common features, particularly low-energy hydrodynamic regime corresponding to their semi-isolated locations and arid climatic conditions, which ruled sediments texture and distributions.

Nails Deformation Sensing For Disease Detection Using Fusion Method For Texture Analysis

Medical image processing is essential for identifying conditions like skin cancer, brain tumors, breast cancer, and illnesses of the heart and lungs. To detect disorders, the human nail serves as a window into the body. Various methods are available in the healthcare industry for early disease diagnosis. One method for diagnosing diseases in their early stages is nail image analysis. It is possible to forecast some general and dermatological disorders using human nails. The naked human eye cannot detect numerous variations in nail color or texture. In this research, models for illness prediction using nail pictures are constructed. In this work, the fusion of statistical and structural texture featuresis employed to forecast diseases. Merging structural and statistical features allows for the analysis of the correctness of many models and the selection of the most relevant one. Numerous methods, particularly GLCM, LBP, and Gabor filter, were used to combine statistical and structural features

Digital mapping of coffee ripeness using UAV-based multispectral imagery

Timely and accurate monitoring of coffee ripeness is essential for harvest planning, especially in mountainous areas where the harvest is performed manually due to the limited use of agricultural mechanization. The increasing temporal and spatial resolutions of remote sensing based on low-altitude unmanned aerial vehicles (UAV) provides a feasible way to monitor the fruit ripeness variability. Due to these facts, this study was aimed to: (1) predict the fruit ripeness using spectral and textural variables; and (2) to determine the best variables for developing spatio-temporal variability maps of the fruit ripeness. To do so, an experiment with six arabica coffee fields was set up. During the coffee ripeness stage in the 2018–2019 and 2020–2021 seasons, seven flights were carried out using a quadcopter equipped with a five-band multispectral camera. After that, 12 spectral and 64 textural variables composed of bands and vegetation indices were obtained. For the same period, the percentage of unripe fruits (fruit ripeness) was determined using an irregular grid on each field. Then, the fruit ripeness was predicted with six machine learning (ML) algorithms using as input (1) the spectral variables and (2) the combination of spectral and textural variables. Among the evaluated ML algorithms, the random forest presented the higher accuracy, in which the model using the spectral and textural variables (r2 = 0.71 and RMSE = 11.47%) presented superior performance than the model based solely on spectral variables (r2 = 0.67 and RMSE = 12.09%). Finally, this study demonstrated the feasibility of using spectral and textural variables derived from UAV imagery for mapping and monitoring the spatiotemporal changes in the fruit ripeness at a fine scale.

Defect-associated microstructure evolution and deformation heterogeneities in additively manufactured 316L stainless steel

Previous research mainly focused on heterogenous microstructure modification to improve mechanical properties, but how the microstructure evolves during deformation has not been clarified yet when metallurgical defects are involved. In this work, austenitic 316L stainless steel was additively manufactured using selective laser melting (SLM), exhibiting typical heterogeneous microstructure with variation in grain structure, texture and metallurgical defects. Besides, the quasi-in-situ microstructural observation was conducted to reveal three competitive plastic deformation mechanisms to accommodate the deformation heterogeneities, i.e., lack-of-fusion pores, twinning and deformation bands. With presence of large lack-of-fusion pores (>1%), they accommodated the main strain and acted as preferential crack initiation sites, contributing to the pre-mature failure. In terms of near-full dense materials, microstructure and texture became significant with prevalent twinning and deformation bands. Twinning was activated in grains with <111> and <101> orientations parallel to the tensile direction, which promotes twin-dislocation and dislocation-cellular wall interactions and thus stabilizes the strain hardening. Deformation bands occurred along the columnar grain bands due to the deformation heterogeneities, which are likely to give rise to the initiation of micro-cracks with continuous strain. These findings offer insight into the microstructure modification and heterogeneous deformation behavior and provide key guidance for additively manufacturing stainless steels with high strength and ductility.

Using Computer Vision for Monitoring the Quality of 3D-Printed Concrete Structures

Concrete 3D printing has the potential to reduce material and process waste in construction. Thus, it contributes to making the construction industry more sustainable through the use of digital-fabrication technologies. While concrete 3D printing is attractive due to its potential to realize complex designs, practical challenges include an increased chance of defects and deformities. Quality assessment of 3D-printed elements is essential for large-scale implementation. Workability of concrete is known to decrease with printing time and it impacts extrudability. It is usually visible in 3D-printed elements, with the lower layers having a smooth finish, while the top layers have cracks and discontinuities. A computer-vision-based quality assessment method is proposed in this paper using a two-bin Linear Binary Pattern textural analysis. Information entropy is used as the metric for measuring the texture variation within each layer and its changes over the layers are studied. A higher entropy value is found for layers having deformities. Finally, through the error-minimization technique, a threshold entropy value is calculated and, using this, the printed layers can be assessed and corrective actions taken. This paper contributes to developing a non-intrusive quality assessment technique for concrete 3D-printed elements.

Color and Texture Analysis of Textiles Using Image Acquisition and Spectral Analysis in Calibrated Sphere Imaging System-I

Numerous imaging applications and analyses demand human perception, and color space transformation of device-dependent tri-band color interpretation (RGB) to device-independent CIE color space standards needs human intervention. The imaging acquisition environment, theoretical conversion errors, viewing geometry, well-defined illumination uniformity, and calibration protocols limit their precision and applicability. It is unfortunate that in most image processing applications, the spectral data are either unavailable or immeasurable. This study is based on developing a novel integrating sphere imaging system and experimentation with textiles’ controlled variation of texture and color. It proposes a simple calibration technique and describes how unique digital color signatures can be derived from calibrated RGB derivatives to extract the best features for color and texture. Additionally, an alter-ego of reflectance function, missing in the imaging domain, is suggested that could be helpful for visualization, identification, and application for qualitative and quantitative color-texture analysis. Our further investigation revealed promising colorimetric results while validating color characterization and different color combinations over three textures.

Investigating the Feasibility of Using DenseNet to Improve Coronary Calcification Detection in CT

Interscan reproducibility of coronary artery calcium (CAC) scoring can be improved by using a smaller slice thickness but at the cost of higher image noise. This study aimed to investigate the feasibility of using densely connected convolutional network (DenseNet) to reduce the image noise in CAC scans reconstructed with slice thickness < 3 mm for improving coronary calcification detection in CT. Phantom data acquired with QRM and CIRS phantoms were used for model training and testing, where the DenseNet model adopted in this work was a convolutional neural network (CNN) designed for super resolution recovery. After phantom study, the proposed method was evaluated in terms of its ability to improve calcification detection using patient data. The CNN input images (IMGinput) were CAC scans reconstructed with 0.5-, 1.0- and 1.5-mm slice thickness, while CNN label images were CAC scans reconstructed with 3-mm slice thickness (IMG3mm). Region of interest (ROI) analysis was carried out on IMG3mm, IMGinput and CNN output images (IMGoutput). Two-sample t test was used to compare the difference in Hounsfield Unit (HU) values within ROI between IMG3mm and IMGoutput. For the calcifications in QRM phantoms, no statistically significant difference was found when comparing the HU values of 400- and 800-HA calcifications identified on IMG3mm to those on IMGoutput with slice thickness of 0.5, 1.0 or 1.5 mm. On the other hand, statistically significant difference was found when comparing the HU values of 200-HA calcifications identified on IMG3mm to those on IMGoutput with a slice thickness of 0.5 and 1.0 mm. Meanwhile, no statistically significant difference was found when comparing the HU values of 200-HA calcifications identified on IMG3mm to those on IMGoutput with a slice thickness of 1.5 mm. As for the rod inserts in CIRS phantoms simulating 9 different tissue types in human body, there was no statistically significant difference between IMG3mm and IMGoutput with slice thickness of 1.5 mm, and all the p values were larger than 0.10. With regards to patient study, more calcification pixels were detected on IMGoutput with a slice thickness of 1.5 mm than on IMG3mm, so calcifications were more clear on the denoised images. According to our results, the CNN-based denoising method could reduce statistical noise in IMGinput with a slice thickness of 1.5 mm without causing significant texture change or variation in HU values. The proposed method could improve cardiovascular risk prediction by detecting small and soft calcifications that are barely identified on 3-mm slice images used in conventional CAC scans.

The conjoint influence of oxygen and hot extrusion on microstructure and mechanical properties of a powder metallurgy processed aluminum alloy

In this paper, Al-8.3Zn-2.3Mg-2.5Cu alloys with different oxygen contents were prepared through complex techniques including blending or ball milling, cold isostatic pressing, vacuum sintering, hot extrusion, and heat treatment, and their microstructure and mechanical properties were investigated. For the samples containing 0.15 wt% and 0.33 wt% oxygen, the sintered density values are 99.1% and 98.7% of the theoretical density, and the corresponding average grain sizes are 5.60 μm and 4.83 μm. After heat treatment, the average grain sizes of the samples with 0.15 wt% oxygen subjected to the extrusion ratios of 4:1, 9:1, and 17:1 are 5.17 μm, 4.28 μm and 3.83 μm, respectively, and the corresponding yield strengths are 607 MPa, 615 MPa, and 621 MPa. Moreover, the {111}<112> deformation texture changes to {001}<001> and {001}<111> recrystallization textures. In contrast, for the extruded sample (9:1 extrusion ratio) with 0.33 wt% oxygen, the grains are finer, and static recrystallization barely occurs with little variation in texture after heat treatment. This is due to the increase in content of thermostable γ-Al2O3 particles. Accordingly, the sample with high oxygen content exhibits excellent comprehensive mechanical properties with the ultimate tensile strength of 719 MPa, yield strength of 690 MPa, and elongation of 11.5%. Regarding the strengthening mechanism, apart from the common precipitation strengthening, the contributions of dislocation strengthening and grain boundary strengthening are greater in the 0.33 wt% oxygen alloy than in the 0.15 wt% oxygen alloy. This is due to the pinning effect of γ-Al2O3 particles on dislocations and grain boundaries.

Microstructure, Chemistry and Mineralogy Approach for the Diagnostics of Metallic Finds of the Tomba della Biga (Adria, Italy)

This work is focused on the characterization of metallic finds that are part of an ancient war biga from the Tomba della Biga, using different approaches for the diagnostics and also the conservation of this important cultural heritage. The materials analyzed were brought to light in the necropolis of Canal Bianco (Adria, Italy). The samples were analyzed using Optical Reflected Light Microscopy (ORLM), Scanning Electron Microscopy (SEM) and micro-Raman Spectroscopy (µ-RS). The results highlighted the occurrence of two different alloys (Fe and Cu-Sn-bronze) showing important structural and textural variations. These results, along with µ-RS, gave details on the surface and subsurface morphology, as well as the microstructures and the chemical and mineralogical composition of the finds. In particular, µ-RS highlighted the presence of magnetite and goethite as the main mineralogical phases for Fe-samples and malachite, azurite and Cu-oxide for bronze samples. In addition, an attempt was made to trace the manufacturing process, the purity of the alloys and the degree of slagging. Results of analytical studies show corrosion products of the samples which could be evidence of metallurgical aspects. The good state of conservation due to favorable conditions in the burial environment preserved the metal finds from intense corrosion, supporting the hypothesis that the samples reached equilibrium with soil during their life in the burial environment. This proposed methodology based on the microstructure, chemistry and mineral microanalysis allows the diagnostic and evaluation of the state of conservation of metallic finds.

A genome-wide association study of mammographic texture variation

BackgroundBreast parenchymal texture features, including grayscale variation (V), capture the patterns of texture variation on a mammogram and are associated with breast cancer risk, independent of mammographic density (MD). However, our knowledge on the genetic basis of these texture features is limited.MethodsWe conducted a genome-wide association study of V in 7040 European-ancestry women. V assessments were generated from digitized film mammograms. We used linear regression to test the single-nucleotide polymorphism (SNP)-phenotype associations adjusting for age, body mass index (BMI), MD phenotypes, and the top four genetic principal components. We further calculated genetic correlations and performed SNP-set tests of V with MD, breast cancer risk, and other breast cancer risk factors.ResultsWe identified three genome-wide significant loci associated with V: rs138141444 (6q24.1) in ECT2L, rs79670367 (8q24.22) in LINC01591, and rs113174754 (12q22) near PGAM1P5. 6q24.1 and 8q24.22 have not previously been associated with MD phenotypes or breast cancer risk, while 12q22 is a known locus for both MD and breast cancer risk. Among known MD and breast cancer risk SNPs, we identified four variants that were associated with V at the Bonferroni-corrected thresholds accounting for the number of SNPs tested: rs335189 (5q23.2) in PRDM6, rs13256025 (8p21.2) in EBF2, rs11836164 (12p12.1) near SSPN, and rs17817449 (16q12.2) in FTO. We observed significant genetic correlations between V and mammographic dense area (rg = 0.79, P = 5.91 × 10−5), percent density (rg = 0.73, P = 1.00 × 10−4), and adult BMI (rg = − 0.36, P = 3.88 × 10−7). Additional significant relationships were observed for non-dense area (z = − 4.14, P = 3.42 × 10−5), estrogen receptor-positive breast cancer (z = 3.41, P = 6.41 × 10−4), and childhood body fatness (z = − 4.91, P = 9.05 × 10−7) from the SNP-set tests.ConclusionsThese findings provide new insights into the genetic basis of mammographic texture variation and their associations with MD, breast cancer risk, and other breast cancer risk factors.

Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning

Soil moisture (SM) is an important biophysical parameter by which to evaluate water resource potential, especially for agricultural activities under the pressure of global warming. The recent advancements in different types of satellite imagery coupled with deep learning-based frameworks have opened the door for large-scale SM estimation. In this research, high spatial resolution Sentinel-1 (S1) backscatter data and high temporal resolution soil moisture active passive (SMAP) SM data were combined to create short-term SM predictions that can accommodate agricultural activities in the field scale. We created a deep learning model to forecast the daily SM values by using time series of climate and radar satellite data along with the soil type and topographic data. The model was trained with static and dynamic features that influence SM retrieval. Although the topography and soil texture data were taken as stationary, SMAP SM data and Sentinel-1 (S1) backscatter coefficients, including their ratios, and climate data were fed to the model as dynamic features. As a target data to train the model, we used in situ measurements acquired from the International Soil Moisture Network (ISMN). We employed a deep learning framework based on long short-term memory (LSTM) architecture with two hidden layers that have 32 unit sizes and a fully connected layer. The accuracy of the optimized LSTM model was found to be effective for SM prediction with the coefficient of determination (R2) of 0.87, root mean square error (RMSE) of 0.046, unbiased root mean square error (ubRMSE) of 0.045, and mean absolute error (MAE) of 0.033. The model’s performance was also evaluated concerning above-ground biomass, land cover classes, soil texture variations, and climate classes. The model prediction ability was lower in areas with high normalized difference vegetation index (NDVI) values. Moreover, the model can better predict in dry climate areas, such as arid and semi-arid climates, where precipitation is relatively low. The daily prediction of SM values based on microwave remote sensing data and geophysical features was successfully achieved by using an LSTM framework to assist various studies, such as hydrology and agriculture.

Detailed Avatar Recovery From Single Image.

This paper presents a novel framework to recover detailed avatar from a single image. It is a challenging task due to factors such as variations in human shapes, body poses, texture, and viewpoints. Prior methods typically attempt to recover the human body shape using a parametric-based template that lacks the surface details. As such resulting body shape appears to be without clothing. In this paper, we propose a novel learning-based framework that combines the robustness of the parametric model with the flexibility of free-form 3D deformation. We use the deep neural networks to refine the 3D shape in a Hierarchical Mesh Deformation (HMD) framework, utilizing the constraints from body joints, silhouettes, and per-pixel shading information. Our method can restore detailed human body shapes with complete textures beyond skinned models. Experiments demonstrate that our method has outperformed previous state-of-the-art approaches, achieving better accuracy in terms of both 2D IoU number and 3D metric distance.

Processing of metastable beta titanium alloy: Comprehensive study on deformation behaviour and exceptional microstructure variation mechanisms

Thermomechanical processing (TMP) is especially crucial for metastable β titanium alloys, which has received significant attention in the community for a long time. In this contribution, the processing-responding behaviour including microstructure evolution process, texture variation mechanism, and underlying deformation process of powder metallurgy Ti-5553 alloy in a wide processing parameter range was comprehensively investigated. Thermal physical simulation was performed on the alloy at temperatures ranging from 800 °C to 1100 °C, and strain rates between 0.001 s−1 and 10 s−1, to varied deformation degrees of 20%–80% height reduction. It was found that the processing parameters (i.e. temperature, strain rate, and deformation degree) are influential on the deformation process and resultant microstructure. Varied microstructural evolution processes for β phase including flow localization, dynamic recovery, dynamic recrystallization, and grain coarsening are activated in different processing domains, while different evolution mechanisms for α phase including dynamic precipitation, phase separation, dynamic coarsening, and mechanical shearing also play their roles under different processing conditions. In particular, four exceptional evolution mechanisms of α precipitation which have not been previously reported in titanium alloys were discovered and clearly demonstrated, more specifically, they are multi-interior twinning, internal compositing, layered coarsening and selective diffusion-actuated separation. After the establishment of comprehensive microstructural evolution mechanism maps, the guidance for precise processing and the knowledge reserve extension for deformation process of metastable β titanium alloys can be effectively achieved.

On the relationship between laser scan strategy, texture variations and hidden nucleation sites for failure in laser powder-bed fusion

While laser powder-bed fusion has overcome some of the design constraints of conventional manufacturing methods, it requires careful selection of process parameters and scan strategies to obtain favorable properties. Here we show that even simple scan strategies, complex ones being inevitable when printing intricate designs, can inadvertently produce local alterations of the microstructure and preferential grain orientation over small areas – which easily remain unnoticed across the macroscale. We describe how a combined usage of neutron imaging and electron backscatter diffraction can reveal these localized variations and explain their origin within cm-sized parts. We explain the observed contrast variations by linking the neutron images to simulated data, pole figures and EBSD, providing an invaluable reference for future studies and showing that presumably minor changes of the scan strategy can have detrimental effects on the mechanical properties. In-situ tensile tests reveal that fracture occurs in a region that was re-melted during the building process.

Effect of heat treatment on the microstructure and corrosion resistance of 316L stainless steel fabricated by hybrid in-situ rolled wire-arc additive manufacturing

The effect of heat treatment on the microstructure and corrosion resistance of 316L stainless steel fabricated using the hybrid in-situ rolled wire-arc additive manufacturing (HRAM) technique was studied. The results showed that the as-built sample was composed of dendritic δ-ferrite and irregular austenite grains. With increasing annealing temperature (650–1200 °C), the corrosion resistance of HRAM 316L steel in a proton exchange membrane fuel cell environment showed a complex change trend of constant-increase-decrease-increase, attributing to the coupling effects of the variation in phase composition, grain characteristics, and texture. Particularly, after annealing at 1050 °C, the δ-ferrite transformed into the σ-phase with higher contents of Cr and Mo, which promoted the formation and stabilization of the passivation film in the steel, thereby resulting in superior corrosion resistance.

Non-destructive monitoring of amylose content in rice by UAV-based hyperspectral images.

Amylose content (AC) is an important indicator for rice quality grading. The rapid development of unmanned aerial vehicle (UAV) technology provides rich spectral and spatial information on observed objects, making non-destructive monitoring of crop quality possible. To test the potential of UAV-based hyperspectral images in AC estimation, in this study, observations on five rice cultivars were carried out in eastern China (Zhejiang province) for four consecutive years (from 2017 to 2020). The correlations between spectral and textural variables of UAV-based hyperspectral images at different growth stages (booting, heading, filling, and ripening) and AC (%) were analyzed, and the linear regression models based on spectral variables alone, textural variables alone, and combined spectral and textural variables were established. The results showed that the sensitive bands (P< 0.001) to AC were mainly centered in the green (536∽568 nm) and red regions (630∽660nm), with spectral and textural variables at the ripening stage giving the highest negative correlation coefficient of -0.868 and -0.824, respectively. Models based on combined spectral and textural variables give better estimation than those based on spectral or textural variables alone, characterized by less variables and higher accuracy. The best models using spectral or textural variables alone both involved three growth stages (heading, filling, and ripening), with root mean square error (RMSE) of 1.01% and 1.04%, respectively, while the models based on combined spectral and textural variables have RMSE of 1.04% 0.844% with only one (ripening stage) or two (ripening and filling stages) growth stages involved. The combination of spectral and textural variables of UAV-based hyperspectral images is expected to simplify data acquisition and enhance estimation accuracy in remote sensing of rice AC.

Effectiveness of buccal dental‐microwear texture in African Cercopithecoidea dietary discrimination

AbstractObjectivesThis study compares ISO (ISO/FDIS 25178) roughness parameters, calculated from three‐dimensional (3D) molar buccal microtexture surfaces, among African Cercopithecoidea primates with different diets.Materials and MethodsWe examined 98 lower second molars from seven African Cercopithecoidea species with diverse dietary regimes and habitat exploitation. Buccal dental surfaces were analyzed using a Sensofar Plu Neox laser scanning confocal microscope. Thirty‐eight, areal surface texture parameters were extracted (Mountain 7® software). Uni‐ and multivariate statistics were used to obtain diet‐related patterns of buccal‐microwear textures and feeding ecology to differentiate between species.ResultsBuccal‐dental 3D texture parameters discriminate between Cercopithecoidea diets. Analysis of variance (ANOVA) revealed significant variation in microwear texture between forest‐adapted Mandrillus sphinx, which showed coarse flat features, and grassland forager Theropithecus gelada, whose buccal surfaces were characterized by a high density of thin features. Buccal‐microwear textures of folivorous species (Colobus polykomos) were related to a lower density of thicker surface indentations in comparison to Papio anubis and Cercocebus atys which are adapted to the consumption of tough and hard foods. The limited interspecific variation in the buccal‐microwear textures of savanna dwellers (Chlorocebus pygerythrus and Chlorocebus aethiops) probably reflects similarities in their foraging diets.DiscussionSignificant variations between‐species demonstrate that 3D microwear ISO roughness parameters applied to buccal enamel surfaces can distinguish between the diets of Cercopithecoidea.