Three-dimensional Texture Research Articles

Topological\u2013chiral magnetic interactions driven by emergent orbital magnetism

Two hundred years ago, Ampère discovered that electric loops in which currents of electrons are generated by a penetrating magnetic field can mutually interact. Here we show that Ampère’s observation can be transferred to the quantum realm of interactions between triangular plaquettes of spins on a lattice, where the electrical currents at the atomic scale are associated with the orbital motion of electrons in response to the non-coplanarity of neighbouring spins playing the role of a magnetic field. The resulting topological orbital moment underlies the relation of the orbital dynamics with the topology of the spin structure. We demonstrate that the interactions of the topological orbital moments with each other and with the spins form a new class of magnetic interactions - topological–chiral interactions - which can dominate over the Dzyaloshinskii–Moriya interaction, thus opening a path for realizing new classes of chiral magnetic materials with three-dimensional magnetization textures such as hopfions.

<p>Three-Dimensional CT Texture Analysis to Differentiate Colorectal Signet-Ring Cell Carcinoma and Adenocarcinoma</p>

PurposeThe objective of this research was to validate the diagnostic value of three-dimensional texture parameters and clinical characteristics in the differentiation of colorectal signet-ring cell carcinoma (SRCC) and adenocarcinoma (AC).MethodsWe retrospectively analyzed data from 102 patients with SRCC or AC confirmed by pathology, including 51 SRCC (from January 2015 to July 2019) and 51 AC patients (from January 2019 to July 2019). CT findings and clinical data, including age, gender, clinical symptoms, serological biomarkers, tumor size, and tumor location, were compared between SRCC and AC. CT texture features were quantified on portal phase images using three-dimensional analysis. A list of texture parameters was generated with MaZda software for the classification of tumors. The texture features, clinical data and CT findings were statistically analyzed for the discrimination ability of SRCC and AC, and the potential predictive parameters that may be used to differentiate the two groups were subsequently tested using the least absolute shrinkage and selection operator (LASSO) and logistic regression analyses. The receiver operating characteristic curve (ROC) provided a range of values for establishing the cutoff value, as well as the sensitivity and specificity of prediction for each significant variable.ResultsSRCC occurred more often in men than AC did (80.39% vs 49.02%, P < 0.01). The patients were younger in the SRCC group than in the AC group, without a statistically significant difference (55.84 vs 59.20 years, P = 0.216). There were no significant differences in the clinical symptoms, tumor size, or tumor location between the two groups (P=0.505, P=0.19, P=0.843, respectively). The elevation of serological biomarker CA724 was more common in SRCC than in AC (P< 0.001). Perc.01%3D, Perc.10%3D and s(1,0,0) SumAverg were lower in the SRCC group than in the AC group during the portal phase, with the areas under curve (AUCs) of 0.892–0.929, sensitivity of 76.5–84.3% and specificity of 88.2–96.1%. In the differentiation between SRCC and AC, the 1-NN minimal classification error (MCR) was 29.41%.ConclusionThree-dimensional texture parameters, including Perc.01%3D, Perc.10%3D and s(1,0,0) SumAverg, exhibited a favorable discriminatory ability to distinguish SRCC from AC.

Development of an effective model of parallel processing of multimedia data on the CPU and GPU in the cloud system

The paper deals with the technology of paralleling processing multimedia data process on the CPU and GPU of the user’s personal computer. To do this, we have developed an algorithm for planning the sharing of video accelerators, which controls and distributes the load between the processor and the GPU. The main difference of the proposed algorithm is that the integral transformations include the stabilization of three-dimensional objects and textures. Defining a unique sequence number allows you to apply optimal texture filtering when scaling for each object. These actions create optimal video acceleration when processing objects.

Radiomics for Distinguishing Myocardial Infarction from Myocarditis at Late Gadolinium Enhancement at MRI: Comparison with Subjective Visual Analysis.

To evaluate whether radiomics features of late gadolinium enhancement (LGE) regions at cardiac MRI enable distinction between myocardial infarction (MI) and myocarditis and to compare radiomics with subjective visual analyses by readers with different experience levels. In this retrospective, institutional review board-approved study, consecutive MRI examinations of 111 patients with MI and 62 patients with myocarditis showing LGE were included. By using open-source software, classification performances attained from two-dimensional (2D) and three-dimensional (3D) texture analysis, shape, and first-order descriptors were compared, applying five different machine learning algorithms. A nested, stratified 10-fold cross-validation was performed. Classification performances were compared through Wilcoxon signed-rank tests. Supervised and unsupervised feature selection techniques were tested; the effect of resampling MR images was analyzed. Subjective image analysis was performed on 2D and 3D image sets by two independent, blinded readers with different experience levels. When trained with recursive feature elimination (RFE), a support vector machine achieved the best results (accuracy: 88%) for 2D features, whereas linear discriminant analysis (LDA) showed the highest accuracy (85%) for 3D features (P <.05). When trained with principal component analysis (PCA), LDA attained the highest accuracy with both 2D (86%) and 3D (89%; P =.4) features. Results found for classifiers trained with spline resampling were less accurate than those achieved with one-dimensional (1D) nearest-neighbor interpolation (P <.05), whereas results for classifiers trained with 1D nearest-neighbor interpolation and without resampling were similar (P =.1). As compared with the radiomics approach, subjective visual analysis performance was lower for the less experienced and higher for the experienced reader for both 2D and 3D data. Radiomics features of LGE permit the distinction between MI and myocarditis with high accuracy by using either 2D features and RFE or 3D features and PCA.© RSNA, 2019Supplemental material is available for this article.

High-resolution and three-dimensional mapping of soil texture of China

The lack of detailed three-dimensional soil texture information largely restricts many applications in agriculture, hydrology, climate, ecology and environment. This study predicted 90 m resolution spatial variations of sand, silt and clay contents at a national extent across China and at multiple depths 0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm. We used 4579 soil profiles collected from a national soil series inventory conducted recently and currently available environmental covariates. The covariates characterized environmental factors including climate, parent materials, terrain, vegetation and soil conditions. We constructed random forest models and employed a parallel computing strategy for the predictions of soil texture fractions based on its relationship with the environmental factors. Quantile regression forest was used to estimate the uncertainty of the predictions. Results showed that the predicted maps were much more accurate and detailed than the conventional linkage maps and the SoilGrids250m product, and could well represent spatial variation of soil texture across China. The relative accuracy improvement was around 245–370% relative to the linkage maps and 83–112% relative to the SoilGrids250m product with regard to the R2, and it was around 24–26% and 14–19% respectively with regard to the RMSE. The wide range between 5% lower and 95% upper prediction limits may suggest that there was a substantial room to improve current predictions. Besides, we found that climate and terrain factors are major controllers for spatial patterns of soil texture in China. The heat and water-driven physical and chemical weathering and wind-driven erosion processes primarily shape the pattern of clay content. The terrain, wind and water-driven deposition, erosion and transportation sorting processes of soil particles primarily shape the pattern of silt. The findings provide clues for modeling future soil evolution and for national soil security management under the background of global and regional environmental changes.

Radiomics-Based Machine Learning Technology Enables Better Differentiation Between Glioblastoma and Anaplastic Oligodendroglioma.

Purpose: The aim of this study was to test whether radiomics-based machine learning can enable the better differentiation between glioblastoma (GBM) and anaplastic oligodendroglioma (AO).Methods: This retrospective study involved 126 patients histologically diagnosed as GBM (n = 76) or AO (n = 50) in our institution from January 2015 to December 2018. A total number of 40 three-dimensional texture features were extracted from contrast-enhanced T1-weighted images using LIFEx package. Six diagnostic models were established with selection methods and classifiers. The optimal radiomics features were separately selected into three datasets with three feature selection methods [distance correlation, least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GBDT)]. Then datasets were separately adopted into linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Specificity, sensitivity, accuracy, and area under curve (AUC) of each model were calculated to evaluate their diagnostic performances.Results: The diagnostic performance of machine learning models was superior to human readers. Both classifiers showed promising ability in discrimination with AUC more than 0.900 when combined with suitable feature selection method. For LDA-based models, the AUC of models were 0.986, 0.994, and 0.970 in the testing group, respectively. For the SVM-based models, the AUC of models were 0.923, 0.817, and 0.500 in the testing group, respectively. The over-fitting model was GBDT + SVM, suggesting that this model was too volatile that unsuitable for classification.Conclusion: This study indicates radiomics-based machine learning has the potential to be utilized in clinically discriminating GBM from AO.

Light transmission and internal scattering in pulsed laser-etched partially-transparent silicon wafers

Continuing trend in silicon wafer thickness directed at cost reduction approaches basic boundaries created by: (a) mismatch between Al paste and Si wafer thermal expansion and (b) incomplete optical absorption. With its symmetrical front and back electrical contacts, the bifacial solar cell setup reduces stress due to mismatch thermal expansion, decreases metal use and increases high temperature efficiency. Efficiency improvement is accomplished in bifacial solar cells by capturing light from the back surface. Partially transparent wafers provide an option to improve near-infrared radiation absorption within Si wafer. To fully absorb optical radiation, three-dimensional texture of these kinds of wafers is essential. Pulsed laser interactions, thermal oxidation, and wet chemical etching are included in this research. A feature of its energy and pattern setup is the interaction of pulsed laser with Si, running at 1.064 μm wavelength and micro-second length. Two experimental settings were explored: (a) post-laser chemical etching with potassium hydro-oxide etching with thermal oxide as etching mask and (b) post-laser heat Si surface oxidation. Due to fast melting and recrystallization, laser pulsed processing inherently produces its own texture. Some of these spherically-shaped, randomly focused characteristics improve inner scattering and boost near-infrared absorption within the wafer. These characteristics are separated during chemical etching with the thermally-grown oxide layer as an etch mask. Comparison of optical absorption in both surfaces shows almost a rise in the magnitude of absorption in non-etched surfaces. Detailed optical (optical microscope and IR absorption), morphological (field emission scanning electron microscope) and heat imaging (far IR camera) analyses were performed to comprehend physical processes that contribute to near-IR absorption improvement. Such kinds of partially-transparent, three-dimensional textured Si wafers are anticipated to discover applications for bifacial solar cells as substrates.

Prognostic Value of Texture Analysis Based on Pretreatment DWI-Weighted MRI for Esophageal Squamous Cell Carcinoma Patients Treated With Concurrent Chemo-Radiotherapy.

Purpose: The purpose of the research was to assess the prognostic value of three-dimensional (3D) texture features based on diffusion-weighted magnetic resonance imaging (DWI) for esophageal squamous cell carcinoma (ESCC) patients undergoing concurrent chemo-radiotherapy (CRT).Methods: We prospectively enrolled 82 patients with ESCC into a cohort study. Two DWI sequences (b = 0 and b = 600 s/mm2) were acquired along with axial T2WI and T1WI before CRT. Two groups of features were examined: (1) clinical and demographic features (e.g., TNM stage, age and sex) and (2) changes in spatial texture characteristics of the apparent diffusion coefficient (ADC), which characterizes gray intensity changes in tumor areas, spatial pattern and distribution, and related changes caused by CRT. Reproducible feature sets without redundancy were statistically filtered and validated. The prognostic values associated with overall survival (OS) for each parameter were studied using Kaplan-Meier and Cox regression models for univariate and multivariate analyses, respectively.Results: Both univariate and multivariate Cox model analyses showed that the energy of intensity histogram texture (IHIST_energy), radiation dose, mean of the contrast in distance 1 of 26 directions (m_contrast_1), extreme difference of the homogeneity in distance 2 of 26 directions (Diff_homogeneity_2), mean of the inverse variance in distance 2 of 26 directions (m_lnversevariance_2), high-intensity small zone emphasis (HISE), and low-intensity large zone emphasis (LILE) were significantly associated with survival. The results showed that 6 texture parameters extracted from the ADC images before treatment could distinguish among high-, medium-, and low-risk groups (log-rank χ2 = 9.7; P = 0.00773). The biased C-index value was 0.715 (95% CI: 0.708 to 0.732) based on bootstrapping validation.Conclusions: The ADC 3D texture feature can be used as a useful biomarker to predict the survival of ESCC patients undergoing CRT. Combining ADC 3D texture features with conventional prognostic factors can generate reliable survival prediction models.

Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets.

.We developed machine learning methods to identify fibrolipidic and fibrocalcific A-lines in intravascular optical coherence tomography (IVOCT) images using a comprehensive set of handcrafted features. We incorporated features developed in previous studies (e.g., optical attenuation and A-line peaks). In addition, we included vascular lumen morphology and three-dimensional (3-D) digital edge and texture features. Classification methods were developed using expansive datasets (), consisting of both clinical in-vivo images and an ex-vivo dataset, which was validated using 3-D cryo-imaging/histology. Conditional random field was used to perform 3-D classification noise cleaning of classification results. We tested various multiclass approaches, classifiers, and feature selection schemes and found that a three-class support vector machine with minimal-redundancy-maximal-relevance feature selection gave the best performance. We found that inclusion of our morphological and 3-D features improved overall classification accuracy. On a held-out test set consisting of images, we obtained an overall accuracy of 81.58%, with the following (sensitivity/specificity) for each class: other (81.43/89.59), fibrolipidic (94.48/87.32), and fibrocalcific (74.82/95.28). The en-face views of classification results showed that automated classification easily captured the preponderance of a disease segment (e.g., a calcified segment had large regions of fibrocalcific classifications). Finally, we demonstrated proof-of-concept for streamlining A-line classification output with existing fibrolipidic and fibrocalcific boundary segmentation methods, to enable fully automated plaque quantification. The results suggest that our classification approach is a viable step toward fully automated IVOCT plaque classification and segmentation for live-time treatment planning and for offline assessment of drug and biologic therapeutics.

Controllable Tuning of Cobalt Nickel-Layered Double Hydroxide Arrays as Multifunctional Electrodes for Flexible Supercapattery Device and Oxygen Evolution Reaction.

The rational design and fabrication of promising electrodes with prominent energy storage property and conversion performance is crucial for supercapacitors and electrocatalysis. Herein, potato chip-like cobalt nickel-layered double hydroxide@polypyrrole-cotton pad (CoNi-LDH@PCPs) composite was synthesized by in situ polymerization, which was coupled with facile solution reaction and ion-exchange etching process. An interesting potato chip-like structure can effectively expedite the kinetics of the electrode reactions, while the three-dimensional PCPs texture affords efficient pathways for charge transport, and the voids between adjacent fibers are thoroughly accessible for electrolytes and bubble evolution. When evaluated as a positive electrode for wearable supercapattery, the hierarchical CoNi-LDH@PCPs electrode displayed high specific capacity and excellent flexibility. As an oxygen evolution reaction catalyst, this PCP-based electrode also reveals the lowest overpotential of 350 mV at 10 mA cm-2 and a Tafel slope of ∼58 mV dec-1. In addition, density functional theory calculations suggest that the synthesis strategy for controllable tuning of hollow CoNi-LDH arrays reported here represents a critical step toward high-performance electrodes for energy storage and electrochemical catalysis.

Three-Dimensional Texture Feature Analysis of Pulmonary Nodules in CT Images: Lung Cancer Predictive Models Based on Support Vector Machine Classifier.

To extract texture features of pulmonary nodules from three-dimensional views and to assess if predictive models of lung CT images from a three-dimensional texture feature could improve assessments conducted by radiologists. Clinical and CT imaging data for three dimensions (axial, coronal, and sagittal) in pulmonary nodules in 285 patients were collected from multiple centers and the Cancer Imaging Archive after ethics committee approval. Three-dimensional texture feature values (contourlets), and clinical and computed tomography (CT) imaging data were built into support vector machine (SVM) models to predict lung cancer, using four evaluation methods (disjunctive, conjunctive, voting, and synthetic); sensitivity, specificity, the Youden index, discriminant power (DP), and F value were calculated to assess model effectiveness. Additionally, diagnostic accuracy (three-dimensional model, axial model, and radiologist assessment) was assessed using the area under the curves for receiver operating characteristic (ROC) curves. Cross-sectional data from 285 patients (median age, 62 [range, 45-83] years; 115 males [40.4%]) were evaluated. Integrating three-dimensional assessments, the voting method had relatively high effectiveness based on both sensitivity (0.98) and specificity (0.79), which could improve radiologist diagnosis (maximum sensitivity, 0.75; maximum specificity, 0.51) for 23% and 28% respectively. Furthermore, the three-dimensional texture feature model of the voting method has the best diagnosis of precision rate (95.4%). Of all three-dimensional texture feature methods, the result of the voting method was the best, maintaining both high sensitivity and specificity scores. Additionally, the three-dimensional texture feature models were superior to two-dimensional models and radiologist-based assessments.

Three-Dimensional Texture Analysis Based on PET/CT Images to Distinguish Hepatocellular Carcinoma and Hepatic Lymphoma.

Objectives: This study compared the diagnostic ability of image-based parameters with texture parameters in the differentiation of hepatocellular carcinoma (HCC) and hepatic lymphoma (HL) by positron emission tomography–computed tomography (PET/CT).Methods: Patients with pathological diagnosis of HCC and HL were included in this study. Image-based and texture parameters were obtained by manual drawing of region of interest. Receiver operating characteristic (ROC) was used to test the diagnostic capacity of each parameter. Binary logistic regression was used to transform the most discriminative image-based parameters, texture parameters, and the combination of these parameters into three regression models. ROC was used to test the diagnostic capacity of these models.Result: Ninety-nine patients diagnosed with HCC (n = 76) and HL (n = 23, 10 primary HL, 13 secondary HL) by histological examination were included in this study (From 2011 to 2018, West China hospital). According to the AUC and p-value, 2 image-based parameters and five texture parameters were selected. The diagnostic ability of texture-based model was better than that of image-based model, and after combination of those two groups of parameters the diagnostic capacity improved.Conclusion: Texture parameters can differentiate HCC from HL quantitatively and improve the diagnostic ability of image-based parameters.

Collective modes of three-dimensional magnetic structures: A study of target skyrmions

• We explored the three-dimensional (3D) collective modes of target skyrmion under different external magnetic fields. • The three regimes captured by the spectrum calculations directly reflect the ground states of the target skyrmion. • All the collective modes were spatially resolved. • 3D analysis is necessary to understand the collective modes of 3D magnetic structures. Three-dimensional magnetic textures present their richness not only in variety of ground state structures, but also in their dazzling collective modes. As an example, the target skyrmion, a topological spin texture with a central skyrmion and an extra concentric helical ring, has been enabled by confining a chiral magnet in a nanodisk geometry. In this work we investigate the collective spin wave modes of the target skyrmion in the external field region of −400 mT to 400 mT. Through micromagnetic simulations we reproduced three major phases of the target skyrmion, and derived resonance modes at varying external magnetic field by the power spectral density study. All typical modes were resolved in terms of their dynamical snapshots in temporal domain and the corresponding spatial Fourier transformations. Some modes are shown to behave similarly in-plane but differently out-of-plane, and vice versa. This suggests, on a broader scope, that both responses will be necessary to fully characterize collective modes of three-dimensional spin textures.

Effect of pupil dilation on off-angle iris recognition

Due to the less constrained setup in standoff iris recognition systems, it is likely to capture nonideal iris images with gaze angle, pupil dilation, reflections, and occlusions. The combined effect of pupil dilation and gaze angle on iris recognition is examined. We first highlight the effects on synthetic images generated with a biometric eye model using a ray-tracing algorithm. Then, we quantify the effects of pupil dilation and gaze angle on the real frontal and off-angle images at different dilation levels. Our experiments reveal that the larger differences in dilation levels and gaze angles between the compared iris images increase the Hamming distance. Even if the linear rubber-sheet normalization helps to minimize the dilation effect in frontal images, it cannot fully eliminate it in off-angle iris images because of not only the pupil dilation and three-dimensional iris texture but also the corneal refraction distortion and limbus occlusion. We also observe that the gaze angle is the main reason for the performance degradation in steeper off-angle images, where the effect of the dilation is limited. In addition, since the iris region in off-angle and dilated iris images is smaller than that in frontal and constricted iris images, their interclass Hamming distance distribution is shifted toward the intraclass distribution, which may increase the false match rate.

The effect of three-dimensional loading and texture on deformation mechanism of Zircaloy-4 alloy: Using space Schmid factor model

The effect of three-dimensional loading and texture on deformation behavior (including basal <a>, prismatic <a>, pyramidal <a>, first and second order pyramidal <a+c> slips, tensile and compression twins) of Zircaloy-4 alloy sheet was systematically investigated using a new model:space Schmid factor model (SSFM). The SSFM was correlated with the results as calculated by EBSD technique. The results of compression tests and EBSD showed that yield strength was depended upon the orientation of the crystal and decreased in the sequence: TD > RD-35° > RD. Then, analysis of relationship between the loading direction and yielding anisotropy of the Zr-4 sheet revealed that the predominant deformation mode of the Zr-4 sheet was pyramidal <a+c> slip when compressed along the transverse direction (TD) and prismatic <a> slip when compressed either along the rolling direction (RD) or 35° away from the rolling direction (RD-35°). Besides this, a few {101‾2}<1‾011> tensile twins (TT) were exclusively observed in each case. However, the TT get activated easily in RD compression as compared to the other two directions. In general, the SSFM provided a well-substantiated explanation of the deformation mechanisms of the Zr-4 alloy sheet under different loading directions.

Model-based three-dimensional texture analysis of contrast-enhanced magnetic resonance imaging as a potential tool for preoperative prediction of microvascular invasion in hepatocellular carcinoma.

The purpose of the present study was to investigate the value of contrast-enhanced magnetic resonance imaging (CE-MRI) texture analysis for preoperatively predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Accordingly, a retrospective study of 142 patients with pathologically confirmed HCC was performed. The patients were divided into two cohorts: The training cohort (n=99) and the validation cohort (n=43), including the MVI-positive group (n=53) and MVI-negative group (n=89). On the basis of three-dimensional texture analysis, 58 features were extracted from the preoperative CE-MR images of arterial-phase (AP) and portal-venous-phase (PP). The t-test or Kruskal-Wallis test, univariate logistic regression analysis and Pearson correlation were applied for feature reduction. Clinical-radiological features were also analyzed. Multivariate logistic regression analysis was used to build the texture model and combined model with clinical-radiological features. The MVI-predictive performance of the models was evaluated using receiver operating characteristic (ROC) analysis and presented using nomogram. Among the clinical features, a significant difference was found in maximum tumor diameter (P=0.002), tumor differentiation (P=0.026) and α-fetoprotein level (P=0.025) between the two groups in the training cohort. Four MR texture features in AP and five in PP images were identified through feature reduction. On ROC analysis, the AP texture model showed better diagnostic performance than did the PP model in the validation cohort, with an area under the curve (AUC) of 0.773 vs. 0.623, sensitivity of 0.750 vs. 0.500, and specificity of 0.815 vs. 0.926. Together with the clinical features, the combined model of AP improved the AUC, sensitivity and specificity to 0.810, 0.811 and 0.790, respectively, which was demonstrated in nomogram. To conclude, model-based texture analysis of CE-MRI could predict MVI in HCC preoperatively and noninvasively, and the AP image shows better predictive efficiency than PP image. The combined model of AP with clinical-radiological features could improve MVI prediction ability.

Soft-tissue fat tumours: differentiating malignant from benign using proton density fat fraction quantification MRI

To evaluate if quantifying proton density fat fraction (PDFF) would be useful in separating lipoma, atypical lipomatous tumour (ALT) and liposarcoma in the extremities and trunk. In addition, differentiating ALT versus non-classical lipomas using magnetic resonance imaging (MRI)-based fatty acid composition (FAC) and three-dimensional (3D) texture analysis was tested. This prospective study (undertaken between 2014-2017; comprising 20 women, 21 men) was approved by the Regional Ethical Review Board and informed consent was obtained from all participants. For PDFF and FAC 3D spoiled gradient multi-echo images were acquired. PDFF was analysed in 16 lipomas (25-76 years), 14 ALTs (42-78 years) and 11 myxoid liposarcomas (31-68 years). The difference of mean PDFF was tested with one-way analysis of variance. A support vector machine algorithm was used to find the separating mean PDFF values. Mean PDFF for lipomas was 90% (range 76-98%), for ALT 83% (range 62-91%), and for liposarcoma 4% (range 0-21%). The difference of mean PDFF for liposarcomas versus ALT and lipoma was significant (p=0.0001, for both), and for ALT versus lipoma (p=0.021). The optimal threshold for separating liposarcoma from ALT and lipoma was 41.5%, and for ALT and lipoma 85%. Texture analysis could not separate ALT and non-classical lipomas, while the difference for FAC unsaturation degree was significant (p=0.013). Measuring PDFF is a promising complement to standard MRI, to separate liposarcomas from ALT and lipomas. Lipomas that are not solely composed of fat cannot confidently be separated from ALT using PDFF, FAC, or texture analysis.

A new method for predicting the three-dimensional surface texture transfer in the skin pass rolling of metal strips

Revealing the mechanisms of texture transfer in the skin pass of metal rolling is challenging due to the complex contact of randomly distributed surface asperities in both the roll and strip surfaces. This paper presents a novel three-dimensional characterisation method to overcome such difficulties. In this method, the plastic deformation of the random asperities was treated by the material redistribution at asperity tips. A time increment scheme was used to draw the rough surfaces of work roll and strip into the rolling bite. Within each time increment, an iterative technique was developed to predict the surface topography of the strip with the integration of the randomly-rough surface of the work roll. A systematic comparison with the finite element analysis (FEA) confirmed that the method developed can reliably predict the surface texture transfer. In addition, it was found that the surface topography of a metal strip can be significantly affected by the texture transfer from the work roll, that a larger reduction ratio results in a higher texture transfer ratio, and that the work roll surface topography is critical to the formation of desired strip surfaces.

Combined Method of Visualization of Functionally Defined Surfaces and Three-Dimensional Textures

A combined method of object visualization based on analytical and scalar perturbation functions and three-dimensional textures with the use of graphics processing units is proposed. To display the terrain and the change in levels of detail, the same method as that for color textures is applied, and vertex shaders are used in the case of scattered light. A method of real-time visualization of volumetric clouds is described. For this purpose, it is proposed to form three-dimensional textures by means of pre-processing of the cloud structure and volume-oriented visualization.

Dust affects chewing efficiency and tooth wear in forest dwelling Western chimpanzees (Pan troglodytes verus).

In humans it has been shown that abrasive particles in the diet result in increased tooth wear and less intense chewing behavior, both of which decrease chewing efficiency. This behavioral response may also exist in non-human primates as a means to reduce the wear effect of dust-laden food. Here we tested whether the periodical occurrence of abrasive dust particles in the diet of Western chimpanzees affects tooth wear and reduces chewing efficiency. We measured fecal particle size of undigested food matter as an indicator of chewing efficiency in 13 Western chimpanzees of the Taï National Park (Ivory Coast) before (wet), after (wet) and during a dust-rich (dry) period. Moreover, feeding data were compiled for a further 12 chimpanzees and matched to three-dimensional surface texture data measured on two molar facets of 26 skulls of the same population. Fecal particles were larger during the dry period, indicating a reduced chewing efficiency compared to wet periods; age and sex did not have an effect. Concomitantly, dust led to an increase of abrasive wear evidenced by smaller texture features and higher density of fine furrows on wear facets. Our findings show that a periodical increase in dust loads on foods places a dietary-physiological stress on the digestive system in chimpanzees. We suggest that the impact of extrinsic abrasive particles from globally acting periodical dust-laden winds may affect evolutionary fitness. Further studies are required to elucidate this relationship in other non-human primates and fossil hominins.