The effect of preterm birth on thalamic development based on shape and structural covariance analysis.

Acting as a central hub in regulating brain functions, the thalamus plays a pivotal role in controlling high-order brain functions. Considering the impact of preterm birth on infant brain development, traditional studies focused on the overall development of thalamus other than its subregions. In this study, we compared the volumetric growth and shape development of the thalamic hemispheres between the infants born preterm and full-term (Left volume: P = 0.027, Left normalized volume: P < 0.0001; Right volume: P = 0.070, Right normalized volume: P < 0.0001). The ventral nucleus region, dorsomedial nucleus region, and posterior nucleus region of the thalamus exhibit higher vulnerability to alterations induced by preterm birth. The structural covariance (SC) between the thickness of thalamus and insula in preterm infants (Left: corrected P = 0.0091, Right: corrected P = 0.0119) showed significant increase as compared to full-term controls. Current findings suggest that preterm birth affects the development of the thalamus and has differential effects on its subregions. The ventral nucleus region, dorsomedial nucleus region, and posterior nucleus region of the thalamus are more susceptible to the impacts of preterm birth.

The impact of mechanical stress on anatomy, morphology, and gene expression in Urtica dioica L.

MAIN CONCLUSION: Mechanical stress induces distinct anatomical, molecular, and morphological changes in Urtica dioica, affecting trichome development, gene expression, and leaf morphology under controlled conditions The experiments were performed on common nettle, a widely known plant characterized by high variability of leaf morphology and responsiveness to mechanical touch. A specially constructed experimental device was used to study the impact of mechanical stress on Urtica dioica plants under strictly controlled parameters of the mechanical stimulus (touching) and environment in the growth chamber. The general anatomical structure of the plants that were touched was similar to that of control plants, but the shape of the internodes' cross section was different. Stress-treated plants showed a distinct four-ribbed structure. However, as the internodes progressed, the shape gradually approached a rectangular form. The epidermis of control plants included stinging, glandular and simple setulose trichomes, but plants that were touched had no stinging trichomes, and setulose trichomes accumulated more callose. Cell wall lignification occurred in the older internodes of the control plants compared to stress-treated ones. Gene analysis revealed upregulation of the expression of the UdTCH1 gene in touched plants compared to control plants. Conversely, the expression of UdERF4 and UdTCH4 was downregulated in stressed plants. These data indicate that the nettle's response to mechanical stress reaches the level of regulatory networks of gene expression. Image analysis revealed reduced leaf area, increased asymmetry and altered contours in touched leaves, especially in advanced growth stages, compared to control plants. Our results indicate that mechanical stress triggers various anatomical, molecular, and morphological changes in nettle; however, further interdisciplinary research is needed to better understand the underlying physiological mechanisms.

Linear measurements and shape analysis in the calcaneus of selected dog breeds.

The vast array of dog breeds showcases a remarkable diversity that extends to osteological differences. Exploring these morphological distinctions and establishing reference data for various dog breeds are essential steps in comprehending the evolutionary changes that dogs have undergone. In this study, we conducted linear measurements of the calcaneus and performed shape analyses on selected dog breeds to elucidate distinctive characteristics among them. X-ray images of the calcaneus from six different dog breeds-Maltese Terrier, Toy Poodle, Pomeranian, Cavalier King Charles Spaniel, French Bulldog and Golden Retriever-were utilized for this investigation. Radiological images were obtained from a medio-lateral exposure, positioned 30 cm away from the x-ray device. From these images, four linear length measurements and two angle values were extracted. Additionally, a 2D geometric morphometric analysis was conducted using 32 semi landmarks placed on the radiological images. Linear measurements were assessed using ANOVA, while principal component analysis was employed to examine shape variations across all individuals. Shape differences between species were further elucidated through canonical variates analysis. The results revealed that the Golden Retriever exhibited the highest values for linear measurements, while the Pomeranian showed the lowest. Maltese Terriers displayed the highest dorsal calcaneal angle value. Notably, there were significant differences in calcaneal body length among all breeds, except for the Toy Poodle and Cavalier King Charles Spaniel. Moreover, Maltese Terriers exhibited statistically distinct angular measurements compared to other breeds. Principal component analysis unveiled that the first principal component explained 32.69% of the total variation, with the cranial edge of the calcaneal body being closer to the body in individuals with higher values. Shape variations also indicated that Golden Retrievers displayed a broader range of shapes compared to French Bulldogs, which exhibited a more conservative distribution. While there was no clear breed-specific distinction according to the first principal component, Cavalier King Charles Spaniels generally showed lower values. In canonical variates analysis, distinctions in calcaneal shape between species were apparent, with Golden Retrievers, Cavalier King Charles Spaniels and French Bulldogs displaying positive values for the first canonical variates. The highest Procrustes distance was observed between Maltese Terriers and Cavalier King Charles Spaniels. Notably, allometry was found to be statistically insignificant. This comprehensive study utilized both linear and geometric morphometric analyses based on x-ray images, yielding promising results. The integration of imaging systems in veterinary anatomy research presents numerous opportunities for studying animal welfare and health, utilizing various materials such as bones and cadavers. These advancements hold the potential for further enhancing our understanding of animal morphology and well-being.

Bayesian analysis of 1D 1H-NMR spectra.

Extracting spin system parameters from 1D high resolution 1H-NMR spectra can be an intricate task requiring sophisticate methods. With a few exceptions methods to perform such a total line shape analysis commonly rely on local optimization techniques which for increasing complexity of the underlying spin system tend to reveal local solutions. In this work we propose a full Bayesian modeling approach based on a quantum mechanical model of the spin system. The Bayesian formalism provides a global optimization strategy which allows to efficiently include prior knowledge about the spin system or to incorporate additional constraints concerning the parameters of interest. The proposed algorithm has been tested on synthetic and real 1D 1H-NMR data for various spin systems with increasing complexity. The results show that the Bayesian algorithm provides accurate estimates even for complex spectra with many overlapping regions, and that it can cope with symmetry induced local minima. By providing an unbiased estimate of the model evidence the proposed algorithm furthermore offers a way to discriminate between different spin system candidates.

Morphological changes in the cerebellum during aging: evidence from convolutional neural networks and shape analysis.

The morphology and function of the cerebellum are associated with various developmental disorders and healthy aging. Changes in cerebellar morphology during the aging process have been extensively investigated, with most studies focusing on changes in cerebellar regional volume. The volumetric method has been used to quantitatively demonstrate the decrease in the cerebellar volume with age, but it has certain limitations in visually presenting the morphological changes of cerebellar atrophy from a three-dimensional perspective. Thus, we comprehensively described cerebellar morphological changes during aging through volume measurements of subregions and shape analysis. This study included 553 healthy participants aged 20-80 years. A novel cerebellar localized segmentation algorithm based on convolutional neural networks was utilized to analyze the volume of subregions, followed by shape analysis for localized atrophy assessment based on the cerebellar thickness. The results indicated that out of the 28 subregions in the absolute volume of the cerebellum, 15 exhibited significant aging trends, and 16 exhibited significant sex differences. Regarding the analysis of relative volume, only 11 out of the 28 subregions of the cerebellum exhibited significant aging trends, and 4 exhibited significant sex differences. The results of the shape analysis revealed region-specific atrophy of the cerebellum with increasing age. Regions displaying more significant atrophy were predominantly located in the vermis, the lateral portions of bilateral cerebellar hemispheres, lobules I-III, and the medial portions of the posterior lobe. This atrophy differed between sexes. Men exhibited slightly more severe atrophy than women in most of the cerebellar regions. Our study provides a comprehensive perspective for observing cerebellar atrophy during the aging process.

Chewing asymmetry in dogs: Exploring the importance of the fossa masseterica and first molar teeth morphology.

Dogs are animals with strong bite force. This strong bite mechanism has led to significant changes in the skeletal system such as fossa masseterica. It can be thought that one side is used more than the other side in chewing and is related to the preference of using the same side's hand, eye and foot. In the study, directional asymmetry and fluctuating asymmetry, which occurs as a result of chewing asymmetry, were examined on the first molar teeth and the fossa masseterica in 85 dog mandibles including a wide diversity of morphotypes. The association of high PC1 values for directional asymmetry with a pronounced cranial index, as evident in breeds like Pekingese, Pomeranian and Bulldog, indicates a potential evolutionary or selective breeding trend favouring brachycephaly. On the contrary, guardian breeds like the German shepherd and Bernese mountain dog, which typically require strong jaws for their roles, showcased reduced PC1 values, which might be related to their functional morphology. Similarly, the PCA results for the first molar teeth shape variations also highlighted the influence of cranial shape, with boxer dogs displaying notably higher PC1 values. The fluctuating asymmetrical distributions provided valuable insights into individualistic variations. Interestingly, no specific breed distribution trend was observed for these asymmetries, indicating a more individual-based variation rather than breed-based. It is essential to note that while these results provide valuable insights, further studies are required to understand the underlying causes better. Factors like genetic variations, developmental processes, dietary habits and external environmental factors could play pivotal roles in these observed morphological differences.

Geometric morphometric evaluation of mandibles of four sheep breeds: Bardoka, Ivesi, Polish Mountain sheep and Turcana.

The enduring relationship between humans and domestic sheep has evolved over millennia, showcasing diverse uses such as meat, milk, wool, leather and fur, shaped by geographical, historical, cultural and social factors. The sheep breeds discussed include the Ivesi from Southeastern Anatolia, known for its varied animal products; the resilient Turcana breed of Romania; Kosovo's Bardoka, valued for its triple-purpose characteristics; and Poland's Polish Mountain Sheep, uniquely utilized for milk production in cheese making. Sheep, with their enduring relationship with humans and significant economic importance, have attracted scientific interest in morphometric studies of their mandibles, yielding valuable data applicable across various fields including basic anatomy, veterinary clinical anatomy, zooarchaeology and veterinary forensic medicine. Traditional morphometric studies rely on statistical methods to compare length, depth and angular ratios between anatomical formations, often highlighting differences between specific points but not fully revealing shape variations between distinct groups. Geometric morphometric analysis has emerged as a preferred method in recent years, enabling shape analyses using coordinate data from various imaging techniques, facilitating a comprehensive examination of mandibular morphometrics among sheep breeds across different countries. This study involved four sheep breeds from different countries, namely Ivesi from Turkey, Bardoka from Kosovo, Polish Mountain Sheep from Poland and Turcana from Romania, with a total of 70 mandibles sourced from various veterinary faculties. Mandibular photographs were meticulously captured, focusing on the right side of mandible pairs and placing landmarks and semi-landmarks along the entire edge, enabling geometric morphometric analysis using tpsUtil, tpsDig2 and MorphoJ software. The analysis included principal component analysis, canonical variate analysis and discriminant function analysis for pairwise comparisons, facilitating a comprehensive examination of mandibular shape variations among the different sheep breeds. Using geometric morphometric methods, this study analysed mandibles from four distinct sheep breeds sourced from different countries, revealing notable variations in regions such as the ramus mandibula, angulus mandibula and incisive areas, attributed to genetic, geographical and dietary influences, highlighting the importance of continued research to better comprehend these shape differences.

ShapeAXI: Shape Analysis Explainability and Interpretability.

ShapeAXI represents a cutting-edge framework for shape analysis that leverages a multi-view approach, capturing 3D objects from diverse viewpoints and subsequently analyzing them via 2D Convolutional Neural Networks (CNNs). We implement an automatic N-fold cross-validation process and aggregate the results across all folds. This ensures insightful explainability heat-maps for each class across every shape, enhancing interpretability and contributing to a more nuanced understanding of the underlying phenomena. We demonstrate the versatility of ShapeAXI through two targeted classification experiments. The first experiment categorizes condyles into healthy and degenerative states. The second, more intricate experiment, engages with shapes extracted from CBCT scans of cleft patients, efficiently classifying them into four severity classes. This innovative application not only aligns with existing medical research but also opens new avenues for specialized cleft patient analysis, holding considerable promise for both scientific exploration and clinical practice. The rich insights derived from ShapeAXI's explainability images reinforce existing knowledge and provide a platform for fresh discovery in the fields of condyle assessment and cleft patient severity classification. As a versatile and interpretative tool, ShapeAXI sets a new benchmark in 3D object interpretation and classification, and its groundbreaking approach hopes to make significant contributions to research and practical applications across various domains. ShapeAXI is available in our GitHub repository https://github.com/DCBIA-OrthoLab/ShapeAXI.

Statistical shape analysis of the lentiform nucleus of children of different age groups: a retrospective study.

The basal nuclei are important during infancy because of the significant development of motor skills. The main aim of this study was to evaluate the shape differences of the lentiform nucleus between different age and gender groups. A total of 126 children's axial magnetic resonance image series were included in the presented study. These images were grouped between 1 and 5 yr old. Right and left lentiform nuclei are marked with selected landmarks using TPSDIG v2.04. Statistical shape analyses were examined by a Generalized Procrustes Analysis. Our results showed that there was no statistically significant difference in lentiform nucleus shape between genders. However, there was a difference between the shapes of the right and left lentiform nuclei between the 1-yr and 5-yr age groups. These results demonstrated the shape changes in the lentiform nucleus during the first 5 yr of life. Further clinical studies based on our results may be used to gather more detailed information about movement disorders and neuronal development.

DCES-PA: Deformation-controllable elastic shape model for 3D bone proliferation analysis using hand HR-pQCT images.

Bone proliferation is an important pathological feature of inflammatory rheumatic diseases. Although recent advance in high-resolution peripheral quantitative computed tomography (HR-pQCT) enables physicians to study microarchitectures, physicians' annotation of proliferation suffers from slice inconsistency and subjective variations. Also, there are only few effective automatic or semi-automatic tools for proliferation detection. In this study, by integrating pathological knowledge of proliferation formation with the advancement of statistical shape analysis theory, we present an unsupervised method, named Deformation-Controllable Elastic Shape model, for 3D bone Proliferation Analysis (DCES-PA). Unlike previous shape analysis methods that directly regularize the smoothness of the displacement field, DCES-PA regularizes the first and second-order derivative of the displacement field and decomposes these vector fields according to different deformations. For the first-order elastic metric, DCES-PA orthogonally decomposes the first-order derivative of the displacement field by shearing, scaling and bending deformation, and then penalize deformations triggering proliferation formation. For the second-order elastic metric, DCES-PA encodes both intrinsic and extrinsic surface curvatures into the second-order derivative of the displacement field to control the generation of high-curvature regions. By integrating the elastic shape metric with the varifold distances, DCES-PA achieves correspondence-free shape analysis. Extensive experiments on both simulated and real clinical datasets demonstrate that DCES-PA not only shows an improved accuracy than other state-of-the-art shape-based methods applied to proliferation analysis but also produces highly sensitive proliferation annotations to assist physicians in proliferation analysis.

Hardware Acceleration of Digital Pulse Shape Analysis Using FPGAs.

The BC501A sensor is a liquid scintillator frequently used in nuclear physics for detecting fast neutrons. This paper describes a hardware implementation of digital pulse shape analysis (DPSA) for real-time analysis. DPSA is an algorithm that extracts the physically relevant parameters from the detected BC501A signals. The hardware solution is implemented in a MicroTCA system that provides the physical, mechanical, electrical, and cooling support for an AMC board (NAMC-ZYNQ-FMC) with a Xilinx ZYNQ Ultrascale-MP SoC. The Xilinx FPGA programmable logic implements a JESD204B interface to high-speed ADCs. The physical and datalink JESD204B layers are implemented using hardware description language (HDL), while the Xilinx high-level synthesis language (HLS) is used for the transport and application layers. The DPSA algorithm is a JESD204B application layer that includes a FIR filter and a constant fraction discriminator (CFD) function, a baseline calculation function, a peak detection function, and an energy calculation function. This architecture achieves an analysis mean time of less than 100 µs per signal with an FPGA resource utilization of about 50% of its most used resources. This paper presents a high-performance DPSA embedded system that interfaces with a 1 GS/s ADC and performs accurate calculations with relatively low latency.

Geodesic shape regression based deep learning segmentation for assessing longitudinal hippocampal atrophy in dementia progression.

Longitudinal hippocampal atrophy is commonly used as progressive marker assisting clinical diagnose of dementia. However, precise quantification of the atrophy is limited by longitudinal segmentation errors resulting from MRI artifacts across multiple independent scans. To accurately segment the hippocampal morphology from longitudinal 3T T1-weighted MR images, we propose a diffeomorphic geodesic guided deep learning method called the GeoLongSeg to mitigate the longitudinal variabilities that unrelated to diseases by enhancing intra-individual morphological consistency. Specifically, we integrate geodesic shape regression, an evolutional model that estimates smooth deformation process of anatomical shapes, into a two-stage segmentation network. We adopt a 3D U-Net in the first-stage network with an enhanced attention mechanism for independent segmentation. Then, a hippocampal shape evolutional trajectory is estimated by geodesic shape regression and fed into the second network to refine the independent segmentation. We verify that GeoLongSeg outperforms other four state-of-the-art segmentation pipelines in longitudinal morphological consistency evaluated by test-retest reliability, variance ratio and atrophy trajectories. When assessing hippocampal atrophy in longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), results based on GeoLongSeg exhibit spatial and temporal local atrophy in bilateral hippocampi of dementia patients. These features derived from GeoLongSeg segmentation exhibit the greatest discriminatory capability compared to the outcomes of other methods in distinguishing between patients and normal controls. Overall, GeoLongSeg provides an accurate and efficient segmentation network for extracting hippocampal morphology from longitudinal MR images, which assist precise atrophy measurement of the hippocampus in early stage of dementia.

Quantifying dysmorphologies of the neurocranium using artificial neural networks.

BACKGROUND: Craniosynostosis, a congenital condition characterized by the premature fusion of cranial sutures, necessitates objective methods for evaluating cranial morphology to enhance patient treatment. Current subjective assessments often lead to inconsistent outcomes. This study introduces a novel, quantitative approach to classify craniosynostosis and measure its severity. METHODS: An artificial neural network was trained to classify normocephalic, trigonocephalic, and scaphocephalic head shapes based on a publicly available dataset of synthetic 3D head models. Each 3D model was converted into a low-dimensional shape representation based on the distribution of normal vectors, which served as the input for the neural network, ensuring complete patient anonymity and invariance to geometric size and orientation. Explainable AI methods were utilized to highlight significant features when making predictions. Additionally, the Feature Prominence (FP) score was introduced, a novel metric that captures the prominence of distinct shape characteristics associated with a given class. Its relationship with clinical severity scores was examined using the Spearman Rank Correlation Coefficient. RESULTS: The final model achieved excellent test accuracy in classifying the different cranial shapes from their low-dimensional representation. Attention maps indicated that the network's attention was predominantly directed toward the parietal and temporal regions, as well as toward the region signifying vertex depression in scaphocephaly. In trigonocephaly, features around the temples were most pronounced. The FP score showed a strong positive monotonic relationship with clinical severity scores in both scaphocephalic (ρ = 0.83, p < 0.001) and trigonocephalic (ρ = 0.64, p < 0.001) models. Visual assessments further confirmed that as FP values rose, phenotypic severity became increasingly evident. CONCLUSION: This study presents an innovative and accessible AI-based method for quantifying cranial shape that mitigates the need for adjustments due to age-specific size variations or differences in the spatial orientation of the 3D images, while ensuring complete patient privacy. The proposed FP score strongly correlates with clinical severity scores and has the potential to aid in clinical decision-making and facilitate multi-center collaborations. Future work will focus on validating the model with larger patient datasets and exploring the potential of the FP score for broader applications. The publicly available source code facilitates easy implementation, aiming to advance craniofacial care and research.

ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.

Analyzing anatomic shapes of tissues and organs is pivotal for accurate disease diagnostics and clinical decision-making. One prominent disease that depends on anatomic shape analysis is osteoarthritis, which affects 30 million Americans. To advance osteoarthritis diagnostics and prognostics, we introduce ShapeMed-Knee, a 3D shape dataset with 9,376 high-resolution, medical-imaging-based 3D shapes of both femur bone and cartilage. Besides data, ShapeMed-Knee includes two benchmarks for assessing reconstruction accuracy and five clinical prediction tasks that assess the utility of learned shape representations. Leveraging ShapeMed-Knee, we develop and evaluate a novel hybrid explicit-implicit neural shape model which achieves up to 40% better reconstruction accuracy than a statistical shape model and implicit neural shape model. Our hybrid models achieve state-of-the-art performance for preserving cartilage biomarkers; they're also the first models to successfully predict localized structural features of osteoarthritis, outperforming shape models and convolutional neural networks applied to raw magnetic resonance images and segmentations. The ShapeMed-Knee dataset provides medical evaluations to reconstruct multiple anatomic surfaces and embed meaningful disease-specific information. ShapeMed-Knee reduces barriers to applying 3D modeling in medicine, and our benchmarks highlight that advancements in 3D modeling can enhance the diagnosis and risk stratification for complex diseases. The dataset, code, and benchmarks will be made freely accessible.

Three-dimensional magnetic resonance imaging-based statistical shape analysis and machine learning-based prediction of patellofemoral instability.

This study performed three-dimensional (3D) magnetic resonance imaging (MRI)-based statistical shape analysis (SSA) by comparing patellofemoral instability (PFI) and normal femur models, and developed a machine learning (ML)-based prediction model. Twenty (19 patients) and 31 MRI scans (30 patients) of femurs with PFI and normal femurs, respectively, were used. Bone and cartilage segmentation of the distal femurs was performed and subsequently converted into 3D reconstructed models. The pointwise distance map showed anterior elevation of the trochlea, particularly at the central floor of the proximal trochlea, in the PFI models compared with the normal models. Principal component analysis examined shape variations in the PFI group, and several principal components exhibited shape variations in the trochlear floor and intercondylar width. Multivariate analysis showed that these shape components were significantly correlated with the PFI/non-PFI distinction after adjusting for age and sex. Our ML-based prediction model for PFI achieved a strong predictive performance with an accuracy of 0.909 ± 0.015, and an area under the curve of 0.939 ± 0.009 when using a support vector machine with a linear kernel. This study demonstrated that 3D MRI-based SSA can realistically visualize statistical results on surface models and may facilitate the understanding of complex shape features.

A geometric morphometric analysis of wing variations in shape and size of the blue bottle fly, Calliphora vicina (Diptera: Calliphoridae).

A geometric morphometric analysis was performed on the right wing of adult Calliphora vicina (Robineau-Desvoidy) collected across 4 altitudinal levels in Sicily. The objective of this study was to assess differences in shape and centroid size (CS) between females and males and across elevations. The wings analyzed in this study were removed from adults of C. vicina collected with baited traps at 20, 700, 1,153, and 1,552; for this study, 19 landmarks were identified in each wing. The coordinates of the landmarks were aligned and superimposed to prevent variations due to position, orientation, and scale; they were then scaled to the same CS and recentered. CS and Procrustes differences were, respectively, used to assess variations in size and shape. Significant differences were observed in wing shape between males and females but not between all altitudinal levels. Female wings were found to be significantly larger than males (P < 0.01). Wings of flies collected at the highest altitudinal level resulted in significantly larger wings than those collected at lower altitudes (P < 0.001), with CS values ranging from 12.1 to 14.1. Variation in wing shape can impact thermal regulation, and therefore, oxygen content, temperature, atmospheric pressure, and solar radiation can have an effect on an insect's body and activity levels. At high elevations and lower temperatures, larger wings could mean less energy expenditure when flying to increase body temperature.

Quantitative analysis of similarity between cerebral arterial blood volume and intracranial pressure pulse waveforms during intracranial pressure plateau waves.

Introduction: Both intracranial pressure (ICP) and cerebral arterial blood volume (CaBV) have a pulsatile character related to the cardiac cycle. The evolution of the shape of ICP pulses under increasing ICP or decreasing intracranial compliance is well documented. Nevertheless, the exact origin of the alterations in the ICP morphology remains unclear. Research question: Does ICP pulse waveform become similar to non-invasively estimated CaBV pulse during ICP plateau waves. Material and methods: A total of 15 plateau waves recorded in 15 traumatic brain injured patients were analyzed. CaBV pulse waveforms were calculated using global cerebral blood flow model from transcranial Doppler cerebral blood flow velocity (CBFV) signals. The difference index (DI) was used to quantify the similarity between ICP and CaBV waveforms. DI was calculated as the sum of absolute sample-by-sample differences between ICP and CaBV waveforms, representing the area between the pulses. Results: ICP increased (19.4 mm Hg [Q1-Q3: 18.2-23.4 mm Hg] vs. 42.7 mm Hg [Q1-Q3: 36.5-45.1 mm Hg], p < 0.001) while CBFV decreased (44.2 cm/s [Q1-Q3: 34.8-69.5 cm/s] vs. 32.9 cm/s [Q1-Q3: 24.7-68.2 cm/s], p = 0.002) during plateau waves. DI was smaller during the plateau waves (20.4 [Q1-Q3: 15.74-23.0]) compared to the baselines (26.3 [Q1-Q3: 24.2-34.7], p < 0.001). Discussion and conclusion: The area between corresponding ICP and CaBV pulse waveforms decreased during the plateau waves which suggests they became similar in shape. CaBV may play a significant role in determining the shape of ICP pulses during the plateau waves and might be a driving force in formulating ICP elevation.

Normal and equivolumetric coordinate systems for cortical areas.

We describe coordinate systems adapted for the space between two surfaces, such as those delineating the highly folded cortex in mammalian brains. These systems are estimated in order to satisfy geometric priors, including streamline normality or equivolumetric conditions on layers. We give a precise mathematical formulation of these problems, and present numerical simulations based on diffeomorphic registration methods, comparing them with recent approaches. Our method involves•Diffeomorphic registration of inner and outer folded folded surfaces.•Followed by equivolumetric reparametrization of layers to yield coordinate system.

New insights into morphological adaptation in common mole-rats (Cryptomys hottentotus hottentotus) along an aridity gradient.

Morphological adaptation is the change in the form of an organism that benefits the individual in its current habitat. Mole-rats (family Bathyergidae), despite being subterranean, are impacted by both local and broad-scale environmental conditions that occur above ground. Common mole-rats (Cryptomys hottentotus hottentotus) present an ideal mammalian model system for the study of morphological variation in response to ecology, as this species is found along an aridity gradient and thus can be sampled from geographically non-overlapping populations of the same species along an environmental longitudinal cline. Using the mass of five internal organs, ten skeletal measurements and 3D morphometric analyses of skulls, we assessed the morphology of wild non-breeding individuals from five common mole-rat populations in South Africa. We found that the body mass and mean relative mass of the spleen and kidneys in arid populations was larger, and individuals from arid regions possessed shorter legs and larger inter-shoulder widths compared to individuals from mesic regions. Additionally, arid populations demonstrated greater skull depth, and shape change of features such as angular processes of the lower jaw than mesic individuals, indicating that these distinct geographic populations show differences corresponding to the aridity gradient, potentially in response to environmental factors such as the variation in food sources found between different habitats, in addition to different soil compositions found in the different regions. Arid populations potentially require a stronger jaw and neck musculature associated with mastication to chew xeric-adapted plants and to dig through hard soil types, whereas mesic populations excavate through soft, looser soil and may make use of their front limbs to aid the movement of soils when digging. Aridity influences the morphology of this species and could indicate the impact of environmental changes on speciation and mammalian skull morphology.

Measures of angularity in digital images.

In light of the growing interest in studying the affective and aesthetic attributes of curvature, the present paper describes four digital image processing techniques that can be used to objectively discriminate between angular and curvilinear stimuli. MATLAB scripts for each of the techniques accompany the paper. Three studies are then reported that evaluate the efficacy of five metrics, derived from the four techniques, at quantifying the degree of angularity depicted in an image. Images of simple polygons (Study 1), artistic drawings of everyday objects (Study 2), and real-world objects, typefaces, and abstract patterns (Study 3) were analyzed. Logistic regression models were used to determine the relative importance of the metrics at distinguishing between angular and curvilinear items. With one exception, all of the metrics were capable of distinguishing between angular and curvilinear items at a level above chance, but some metrics were better at doing so than others, and their discriminative capacity was influenced by the characteristics of the image. The strengths and limitations of the metrics are discussed, as well as some practical recommendations.