Images Of Transverse Sections Research Articles

Disease suppression, growth promotion and colonization attributes of resident endophytic bacteria against white root rot (Dematophora necatrix Hartig) of apple.

The inherent potential of apple plants was investigated to explore bacterial endophytes and their role in suppressing Dematophora necatrix,the causative pathogen of white root rot disease. Resultantly 34 endophytic bacteria isolated from healthy apple plants, and subsequently 6 most efficient isolates viz., Bacillus megaterium strain EA3, Enterobacter sp. strain EA7, Bacillus megaterium strain EK2, Stenotrophomonas maltophilia strain EK6, Acinetobacter nosocomialis strain ES2 and Pseudomonas aeruginosa strain ES8 depicting anti-pathogen interactions through preliminary screening were assessed further under in vitro, glasshouse and field conditions against white root rot pathogen/disease. Maximum mycelial growth inhibition (80.37%) was obtained with S. maltophilia strain EK6 encouraging for its seed treatment and soil application thereby providing significant effective control (87.91%) of white root rot under glasshouse conditions to other five bacterial endophytes evaluated simultaneously, followed by field efficacy of 83.70%. In addition, it has significantly enhanced the growth parameters of apple trees under both glasshouse and field conditions. The inoculated healthy plants were assessed for endophytic colonization which revealed maximum endosphere colonialism by S. maltophilia strain EK6. Additionally, confocal microscopic images of transverse sections of root cells colonized by bacterial endophytes as compared to untreated control implied their persistence and establishment in endosphere of apple seedlings. The study provides the first report on interaction between apple associated bacterial root endophytes and D. necatrix. The obtained endophytic strains could be employed as alternative for mitigating white root rot disease in future.

Structural Reorganization of the Third Metatarsal Bone Shaft After Autogenous Plasty of the Tibial Portion of the Sciatic Nerve

Background. Previous research has shown that neurectomy of the sciatic nerve leads to a reduction in bone density in the femur and tibia of laboratory mice and rats. However, the impact of surgeries aimed at restoring nerve innervation on the bones of distal limb parts has not been studied.
 Aim of the study to identify structural changes in the shaft of the third metatarsal bone after primary autogenous plasty of the resection defect of the tibial portion of the sciatic nerve in rats.
 Methods. Autologous neuroplasty of the tibial portion of the sciatic nerve was performed on 16 Wistar rats (aged 8-10 months). The animals were euthanized at 4 and 6 months after the surgery, and a control group of 7 intact rats of similar age was included. Histomorphometric analysis was conducted on a dissected fragment of the forefoot at the level of the metatarsal bones. The ratio of fuchsinophilic and anilinophilic structures of the cortical plate was determined using point-count volumetry on Masson-stained images of transverse sections of the third metatarsal bone shaft. The thickness of the cortical plate, numerical density, area, and diameter of osteons and Haversian canals were measured.
 Results. After 4 months, compared to the control group, a 15% decrease (p = 0.0001) in the proportion of mineralized structures of the cortical plate and a 12.7% reduction (p = 0.0184) in its thickness were observed. Osteolysis signs were present in the osteonal layer, along with decreased numerical density and dimensional characteristics of osteons, and the presence of osteons with dilated Haversian canals. At 6 months, the thickness of the cortical plate did not significantly differ from the norm (p = 0.2067), but there was a progressive 33.6% decrease (p = 0.0001) in the proportion of mineralized structures. Reduced values of numerical density, area, and diameter of osteons persisted in the osteonal layer, while the diameters of Haversian canals in osteons increased over time.
 Conclusion. From 4 to 6 months, the thickness of the cortical layer in the compact bone of the third metatarsal bone shaft was restored. However, changes in the numerical and dimensional composition of osteons, along with decreased mineralization of the extracellular matrix and erosion of the subperiosteal bone layer, continued to progress. The developed experimental 2D model can be used to assess denervation osteopenia in distal limb parts and further explore rehabilitation interventions that enhance and improve reinnervation.

[Quality of Images Reconstructed by Deep Learning Reconstruction Algorithm for Head and Neck CT Angiography at 100 kVp].

Objective To evaluate the impact of deep learning reconstruction algorithm on the image quality of head and neck CT angiography (CTA) at 100 kVp. Methods CT scanning was performed at 100 kVp for the 37 patients who underwent head and neck CTA in PUMC Hospital from March to April in 2021.Four sets of images were reconstructed by three-dimensional adaptive iterative dose reduction (AIDR 3D) and advanced intelligent Clear-IQ engine (AiCE) (low,medium,and high intensity algorithms),respectively.The average CT value,standard deviation (SD),signal-to-noise ratio (SNR),and contrast-to-noise ratio (CNR) of the region of interest in the transverse section image were calculated.Furthermore,the four sets of sagittal maximum intensity projection images of the anterior cerebral artery were scored (1 point:poor,5 points:excellent). Results The SNR and CNR showed differences in the images reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D (all P<0.01).The quality scores of the image reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D were 4.78±0.41,4.92±0.27,4.97±0.16,and 3.92±0.27,respectively,which showed statistically significant differences (all P<0.001). Conclusion AiCE outperformed AIDR 3D in reconstructing the images of head and neck CTA at 100 kVp,being capable of improving image quality and applicable in clinical examinations.

Deep machine learning for cell segmentation and quantitative analysis of radial plant growth.

Plants produce the major part of terrestrial biomass and are long-term deposits of atmospheric carbon. This capacity is to a large extent due to radial growth of woody species - a process driven by cambium stem cells located in distinct niches of shoot and root axes. In the model species Arabidopsis thaliana, thousands of cells are produced by the cambium in radial orientation generating a complex organ anatomy enabling long-distance transport, mechanical support and protection against biotic and abiotic stressors. These complex organ dynamics make a comprehensive and unbiased analysis of radial growth challenging and asks for tools for automated quantification. Here, we combined the recently developed PlantSeg and MorphographX image analysis tools, to characterize tissue morphogenesis of the Arabidopsis hypocotyl. After sequential training of segmentation models on ovules, shoot apical meristems and adult hypocotyls using deep machine learning, followed by the training of cell type classification models, our pipeline segments complex images of transverse hypocotyl sections with high accuracy and classifies central hypocotyl cell types. By applying our pipeline on both wild type and phloem intercalated with xylem (pxy) mutants, we also show that this strategy faithfully detects major anatomical aberrations. Collectively, we conclude that our established pipeline is a powerful phenotyping tool comprehensively extracting cellular parameters and providing access to tissue topology during radial plant growth.

Micro image classification of 19 high-value hardwood species based on texture feature fusion

For classification of wood species with similar microstructure, 19 high-value hardwood species of Papilionaceae, Ebenaceae, and Caesalpiniaceae were used as experimental objects. Images of transverse sections, radial sections, and tangential sections were collected by Micro CT. Local binary patterns (LBP) are often used for feature extraction. LBP deformed forms such as uniform LBP, rotation-invariant LBP, and rotation-invariant uniform LBP were fused with Gray-Level Co-Occurrence Matrix (GLCM) to form three fusion features. The fusion features were combined with support vector machine (SVM) or BP neural network to realize wood classification. The texture feature fusion method was found to be better than the single feature classification. Among them, the effect of uniform LBP and GLCM fusion was the best, and the classification accuracy combined with SVM was the highest. The evaluation of the classification of 19 kinds of hardwood mainly depended on transverse sections, and its accuracy was higher than that of the radial and tangential sections. Therefore, the classification results of transverse sections should be taken as the main evaluation basis for the classification of the 19 high-value hardwood species.

Automated Classification of Selected Philippine Wood Species Using Image Analysis and Artificial Neural Networks

This research aimed to address the need of the wood-based sector for a straightforward, rapid, and reliable wood identification tool. This sector includes agencies like the Department of Environment and Natural Resources, wood processing plants, and state universities and colleges. A model using artificial neural networks was developed to automatically perform image- based identification of 20 selected Philippine wood species. It banks on a progressive database containing numerous macroscopic transverse section images taken from authentic samples of the species included in this study. The model has an F1 score of 87.9%. A system usability survey (SUS) was performed to assess the effectiveness of the web application by deploying it to stakeholders who are engaged in wood identification. The SUS results showed that majority of the respondents rated the web application as either good or excellent. An average of 75.6 SUS score or a grade of “B” (good and acceptable) was obtained from the responses received. All 27 respondents indicated that they would recommend the application to other users. For future directions, inclusion of additional species for identification is recommended, given the fact that there are hundreds of species in the Philippines. This will strengthen the capability of the application to have a more precise and accurate wood identification result. Furthermore, the creation of a mobile application and an offline version of this wood identification app will be taken into consideration.

Ex vivo ultrasonographic and histological morphometry of small intestinal wall layers in horses.

Ultrasonographic morphometry of wall layers is commonly used in veterinary patients with suspected small intestinal disease, however published studies comparing this method with histopathology in horses are limited. This prospective, methods comparison study compared the qualitative and quantitative characteristics of small intestinal wall layers using ex vivo high‐frequency ultrasound versus histopathology in a sample of 16 horses. Transverse section images of duodenum, distal jejunum, and ileum were acquired with a high‐frequency linear transducer (7–15 MHz). Transverse histological cryosections were obtained at the same level. Appearance and measurements of the intestinal wall layers were assessed on the ultrasonographic and histological images. High‐frequency scanning with the probe in close contact with the serosal surface of the equine intestinal wall allowed a clear and detailed definition of wall layers. A hyperechoic line was consistently detected within the tunica muscularis in all the intestinal tracts, corresponding histologically to the interface between its longitudinal and circular muscle layers. The overall trend of the values for wall layers thickness was comparable between ex vivo ultrasonography and histology. However, a poor agreement was found between the two methods for all layers. The ultrasonographic measurements were thicker compared to histological measurements, with the exception of the total wall and the muscular layer thicknesses. These layers were thinner on ultrasonography in the duodenum and in all the intestinal segments, respectively. Findings from the current study can be used as background for future ultrasonographic investigations of small intestinal diseases in horses.

Quantitative structural imaging of keratoconic corneas using polarization-resolved SHG microscopy.

The human cornea is mainly composed of collagen fibrils aligned together within stacked lamellae. This lamellar structure can be affected in pathologies such as keratoconus, which is characterized by progressive corneal thinning and local steepening. In this study, we use polarization-resolved second harmonic generation (P-SHG) microscopy to characterize 8 control and 6 keratoconic human corneas. Automated processing of P-SHG images of transverse sections provides the collagen orientation in every pixel with sub-micrometer resolution. Series of P-SHG images recorded in the most anterior region of the stroma evidence sutural lamellae inclined at 22° ± 5° to the corneal surface, but show no significant difference between control and keratoconic corneas. In contrast, series of P-SHG images acquired along the full thickness of the stroma show a loss of order in the lamellar structure of keratoconic corneas, in agreement with their defective mechanical properties. This structural difference is analyzed quantitatively by computing the entropy and the orientation index of the collagen orientation distribution and significant differences are obtained along the full thickness of the stroma. This study shows that P-SHG is an effective tool for automatic quantitative analysis of structural defects of human corneas and should be applied to other collagen-rich tissues.

Modeling of bending and torsional stiffnesses of bone at sub-microscale: Effect of curved mineral lamellae

Recent transmission electron microscopy images of transverse sections of human cortical bone showed that mineral lamellae (polycrystalline sheets of apatite crystals) form arcuate multi-radius patterns around collagen fibrils. The 3–6 nm thick mineral lamellae are arranged in stacks of 3–20 layers and curve around individual fibrils, few fibrils, and higher numbers of collagen fibrils. We evaluate the effect of these stacked mineral lamellae with various radius of curvature patterns on the elastic bending and torsional responses of bone at the sub-microscale using a finite element method. We find that the curved multi-radius stack patterns increased the bending and torsional stiffnesses by 7% and 23%, respectively, compared to when the stacks of mineral lamellae only encircle individual fibrils for the idealized geometric models considered. This study provides new insights into the structure–property relations for the bone ultrastructure.

Abstract P379: Automatic Segmentation of Atherosclerotic Plaques in Transverse Carotid Ultrasound Images Using Deep Learning

Purpose: Ultrasound imaging is commonly used for patients with atheroscelerotic plaques in the carotid artery. While B-mode ultrasound can be used for detection and measurement of these plaques, interpreting these images can be a subjective and time-consuming task. Deep learning algorithms have been proven to be an effective tool for interpreting medical images, especially for classification and segmentation tasks. Here, we propose a deep learning model to automatically detect and measure plaques in transverse B-mode images of the carotid artery. Methods: The proposed automated method takes a transverse B-mode image of the carotid artery as an input and segments the vessel wall in the transverse cross section image using convolutional neural networks. To ensure that the method can perform well in clinical settings, the method has been evaluated on not only healthy subjects (max IMT below 1.2 mm) but also on patients with atheroscelerotic plaques and other vessel wall pathology. Given the B-mode transverse image as an input, the neural network first finds a region of interest (ROI) surrounding the artery and then segments both the inner and outer wall of the carotid artery. We determined the accuracy of the system by F1 Score, a common metric to evaluate the performance of machine learning algorithms. Results: The network was trained and tested on a transverse ultrasound carotid artery dataset that has 506 images, gathered from 4 hospitals. Annotations of an expert reader were used as the ground truth and the performance of the method was evaluated using 5-fold cross validation. The proposed method reaches an F1 score of 0.91 for correctly detecting the ROI and an F1 score of 0.78 for detecting and segmenting the vessel walls in transverse B-mode images. Conclusions: The results show that the proposed deep learning method can be used for accurate analysis and interpretation of carotid ultrasound scans in a clinical setting and potentially reduce the reporting time while increasing objectivity of the analysis.

3D reconstruction of structures of hatched larva and young juvenile of the larvacean Oikopleura dioica using SBF-SEM

The larvacean Oikopleura dioica is a planktonic chordate and an emerging model organism with a short life cycle of 5 days that belongs toTunicata (Urochordata), the sister clade of vertebrates. It is characterized by the rapid development of a tadpole-shaped body. Organ formation in the trunk proceeds within 7 h after the hatching of the tailbud larvae at 3 h after fertilization (hpf) and is completed at 10 hpf, giving rise to fully functional juveniles as miniature adult form. Serial block face scanning electron microscopy was used to acquire ~ 2000 serial transverse section images of a 3 hpf larva and a 10 hpf juvenile to characterize the structures and cellular composition of the trunk and organs using 3D images and movies. Germ cells were found to fuse and establish a central syncytial cell in the gonad as early as 10 hpf. Larval development gave rise to functional organs after several rounds of cell division through trunk morphogenesis. The feature would make O. dioica ideal for analyzing cellular behaviors during morphogenetic processes using live imaging. The detailed descriptions of the larvae and juveniles provided in this study can be utilized as the start and end points of organ morphogenesis in this rapidly developing organism.

Computer-assisted timber identification based on features extracted from microscopic wood sections

Abstract Wood anatomy is one of the most important methods for timber identification. However, training wood anatomy experts is time-consuming, while at the same time the number of senior wood anatomists with broad taxonomic expertise is declining. Therefore, we want to explore how a more automated, computer-assisted approach can support accurate wood identification based on microscopic wood anatomy. For our exploratory research, we used an available image dataset that has been applied in several computer vision studies, consisting of 112 — mainly neotropical — tree species representing 20 images of transverse sections for each species. Our study aims to review existing computer vision methods and compare the success of species identification based on (1) several image classifiers based on manually adjusted texture features, and (2) a state-of-the-art approach for image classification based on deep learning, more specifically Convolutional Neural Networks (CNNs). In support of previous studies, a considerable increase of the correct identification is accomplished using deep learning, leading to an accuracy rate up to 95.6%. This remarkably high success rate highlights the fundamental potential of wood anatomy in species identification and motivates us to expand the existing database to an extensive, worldwide reference database with transverse and tangential microscopic images from the most traded timber species and their look-a-likes. This global reference database could serve as a valuable future tool for stakeholders involved in combatting illegal logging and would boost the societal value of wood anatomy along with its collections and experts.

Architecture of female urethral supporting structures based on undeformed high-resolution sectional anatomical images.

Female urinary incontinence mainly relates to damage of female urethra supporting structures, while its anatomy and function specially in which the connective tissue part are still unclear and controversial. We study it based on 4 thin-sectional, high-resolution, transverse sectional anatomical images [Chinese Visible Human (CVH) images] and 10 high-resolution MRI images from volunteers. The female urethral supporting structures and its adjacent structures were segmented and three-dimensional (3D) reconstructed with Amira software. The urethral supporting structures include muscular and connective tissue supporting structures. Muscular supporting structures are composed of levator ani muslce and striated urethral sphincter, the connective tissue supporting structures are composed of anterior vaginal wall, pubovesical muscle, pubovesical ligament, lateral vesical ligament, and tendinous arch of pelvic fascia (TAPF). The anterior vaginal wall includes tight and loose connections between urethral, bladder, and vagina. The lateral vesical ligament connects the proximal part of the urethra to the TAPF. The pubovesical muscle is crescent shaped and continues with the detrusor of the bladder superior and directly connects the TAPF laterally. The TAPF is an obvious fibrous structure that originates at the middle-posterior surface of the pubis, travels onto the parietal pelvic fascia, and inserts posteriorly onto the ischial spine. The anterior vaginal wall, the pubovesical muscle, the lateral vesical ligament, and the TAPF create the "hammock" structure and supplement DeLancey's theory. Its support to the proximal urethra and neck of bladder is crucial to maintain stability and urinary continence during increased abdominal pressure.

Multiplanar reconstruction with incomplete data via enhanced fuzzy radial basis function neural networks

Slice images based on a single slicing direction often contain incomplete data and cannot be used by clinicians for diagnosis or observation. It is thus necessary to reconstruct the slices using multiplanar reconstruction technology. In the case of complete data, it is not difficult to obtain a series of clear images from other slicing directions. In the case of incomplete data, interpolation methods are commonly employed on reconstructed images to compensate for the missing information. However, such results are often not ideal. In this study, we propose a new method based on an enhanced fuzzy radial basis function neural network. First, a series of incomplete transverse section images that have been accurately registered are adopted. Then, we superpose the sequence images to obtain the three-dimensional data volume. Thereafter, we can acquire the coronal or sagittal images by reformatting this data volume. For a reconstructed image, the proposed system was applied to compensate for the lost data. We used 15 sets of proposed neural networks to obtain 15 sets of output data, with the final output data acquired via the inverse distance-weighted algorithm. We trained the system via a gravitational search algorithm. Finally, we repaired all the interpolated data. In this experiment, we used two types of datasets, i.e., images obtained via brain magnetic resonance imaging and abdominal computed tomography. Subjective observations and objective evaluations confirm the superiority and effectiveness of the proposed method compared to other state-of-the-art methods.

Expression of Maize MADS Transcription Factor ZmES22 Negatively Modulates Starch Accumulation in Rice Endosperm.

As major component in cereals grains, starch has been one of the most important carbohydrate consumed by a majority of world’s population. However, the molecular mechanism for regulation of biosynthesis of starch remains elusive. In the present study, ZmES22, encoding a MADS-type transcription factor, was modestly characterized from maize inbred line B73. ZmES22 exhibited high expression level in endosperm at 10 days after pollination (DAP) and peaked in endosperm at 20 DAP, indicating that ZmES22 was preferentially expressed in maize endosperm during active starch synthesis. Transient expression of ZmES22 in tobacco leaf revealed that ZmES22 protein located in nucleus. No transactivation activity could be detected for ZmES22 protein via yeast one-hybrid assay. Transformation of overexpressing plasmid 35S::ZmES22 into rice remarkedly reduced 1000-grain weight as well as the total starch content, while the soluble sugar was significantly higher in transgenic rice lines. Moreover, overexpressing ZmES22 reduced fractions of long branched starch. Scanning electron microscopy images of transverse sections of rice grains revealed that altered expression of ZmES22 also changed the morphology of starch granule from densely packed, polyhedral starch granules into loosely packed, spherical granules with larger spaces. Furthermore, RNA-seq results indicated that overexpressing ZmES22 could significantly influence mRNA expression levels of numerous key regulatory genes in starch synthesis pathway. Y1H assay illustrated that ZmES22 protein could bind to the promoter region of OsGIF1 and downregulate its mRNA expression during rice grain filling stages. These findings suggest that ZmES22 was a novel regulator during starch synthesis process in rice endosperm.

CT SCAN BASED EVALUATION OF SPLENIC INDEX IN PATIENTS FROM LUCKNOW AND ADJOINING AREAS

Introduction: The Indian subcontinent is an endemic zone for diseases like malaria and filaria which alter the size of spleen to a variable extent. In order to identify and examine the spleen accurately, the modes of investigation that are at our disposal are Ultrasonography, Computerised Tomography, Magnetic Resonance Imaging and Radionuclide Scan. Linear dimensions are of limited use as a guide to normal splenic size on CT because the spleen is irregular in shape and is obliquely oriented within the left upper quadrant. A more accurate approach to the assessment of splenic size is the splenic index. Sufficient and reliable data for its normal values are lacking. The objective of the present study was to generate normative data of splenic index which could be used as a research and clinical tool. Material &amp; Methods: It was an open randomized study carried out on 80 adults between 20 to 70 years of age. Spleen was identified in each transverse, coronal and sagittal section image of helical CT scan. Results: The average splenic index, calculated as a product of the three measured linear dimensions of spleen i.e. length, breadth and thickness, in males was 369.89 cm³ and in females 298.61 cm³. The mean splenic index was calculated as 342.62 cm³. Conclusion: The results can be used for evaluating patients with suspected splenomegaly, in certain clinical situations where objective measurement of splenic dimensions and comparison with normal values is required, and in drafting of government policies and programs related to malaria and filaria control where these normal values of splenic index help by identifying populations more susceptible to such infections.

Non-invasive wood identification of historical musical bows

We identified the wood of the sticks of eight bows in the historical collection of musical instruments in the Galleria dell’ Accademia in Florence. Wood identification was carried out non-invasively (i.e., without sampling wood from the original objects), because the removal of samples from fine musical instruments will affect their aesthetic integrity and/or functional quality. Identification attempts using reflected light microscopy of wood surfaces, gave only partial results due to the poor quality of the surfaces and the particular geometry of the sticks that does not have any transverse surface. Application of Synchrotron light X-ray microtomography (µCT) in phase-contrast mode to the whole sticks allowed us to obtain stacks of transverse-sectional images that, processed as virtual volumes, revealed several anatomical features. With µCT it was possible to identify three bows as Brosimum guianense (Moraceae), one bow as Caesalpinia echinata (Caesalpiniaceae), and four bows as Manilkara sp. (Sapotaceae).

Characterization of automotive paint by optical coherence tomography

Automotive paint is common trace evidence that plays a significant role in many vehicle-related criminal cases. However, the conventional methods of obtaining tomographic images tend to damage the samples. Optical coherence tomography (OCT) is a novel method to obtain high-resolution and cross-sectional images of the automotive paints in a non-destructive, and high-speed manner. In this study, OCT was applied to image and analyze the automotive paint, using scanning electron microscope (SEM) as reference. Eight automotive paint samples of different brands were examined. The images of multi-layer structures provided by OCT system with 5μm depth resolution were consistent with those by SEM. To distinguish different paints with similar visual appearance, we extracted internal structural features from the images using peak analysis and optical attenuation fit. Six characterized parameters were found to distinguish the samples including the optical path length (OPL) of base coat, the optical attenuation coefficient (OAC) of base coat, the OPL of clear coat, the back-scattering ratio (BSR) of clear coat and base coat, the OPL of primer surfacer, and the BSR of base coat and primer. Statistical differences were evaluated by an independent t-test with p<0.05. OCT was applied to analyze repainted paint as well. Three-dimensional OCT reconstruction of the paints was also implemented to create en face (transverse section) images for morphology examination and comparison. These results suggest that OCT imaging can provide additional new features for analyzing the automotive paints and thereby may be a promising supplement to traditional methods. Meanwhile, the OCT system is favorable for achieving in-situ and real-time examination at the scene of crime.

Facial pillars in dogs: an anatomical study.

This study describes the facial pillar system in dolichocephalic and brachycephalic breeds of dogs, and highlights its differences. Knowledge of facial pillar system anatomy is imperative to carry out successful fracture repair. The aim of our study was to describe the pillar system of the facial skeleton in dogs, and compare different shapes of dog skulls using native skulls, radiographs and computed tomography (CT). The heads of brachycephalic and dolichocephalic dogs were investigated following a maceration process using trans-illumination, radiography and CT. Ground sections were created from bones of the facial skeleton for investigation of osteon directions. This study has revealed that the facial skeleton of the dog has numerous sinuses supported by vertically and horizontally oriented bone pillars. These pillars can be divided into five principal regions in dolichocephalic skulls and six principal regions in brachycephalic skulls. Radiographic imaging of both the brachiocephalic and dolichocephalic skulls was not capable of discerning bone pillars. Computed tomography transverse and horizontal section images discerned all pillars observed in the illuminated skulls.

“Rolled-upness”: phenotyping leaf rolling in cereals using computer vision and functional data analysis approaches

Background The flag leaf of a wheat (Triticum aestivum L.) plant rolls up into a cylinder in response to drought conditions and then unrolls when leaf water relations improve. This is a desirable trait for extending leaf area duration and improving grain size particularly under drought. But how do we quantify this phenotype so that different varieties of wheat or different treatments can be compared objectively since this phenotype can easily be confounded with inter-genotypic differences in root-water uptake and/or transpiration at the leaf level if using traditional methods?Results We present a new method to objectively test a range of lines/varieties/treatments for their propensity of leaves to roll. We have designed a repeatable protocol and defined an objective measure of leaf curvature called “rolled-upness” which minimises confounding factors in the assessment of leaf rolling in grass species. We induced leaf rolling by immersing leaf strips in an osmoticum of known osmotic pressure. Using micro-photographs of individual leaf cross-sections at equilibrium in the osmoticum, two approaches were used to quantify leaf rolling. The first was to use some properties of the convex hull of the leaf cross-section. The second was to use cubic smoothing splines to approximate the transverse leaf shape mathematically and then use a statistic derived from the splines for comparison. Both approaches resulted in objective measurements that could differentiate clearly between breeding lines and varieties contrasting genetically in their propensity for leaf rolling under water stress. The spline approach distinguished between upward and downward curvature and allowed detailed properties of the rolling to be examined, such as the position on the strip where maximum curvature occurs.ConclusionsA method applying smoothing splines to skeletonised images of transverse wheat leaf sections enabled objective measurements of inter-genotypic variation for hydronastic leaf rolling in wheat. Mean-curvature of the leaf cross-section was the measure selected to discriminate between genotypes, as it was straightforward to calculate and easily construed. The method has broad applicability and provides an avenue to genetically dissect the trait in cereals.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-015-0095-1) contains supplementary material, which is available to authorized users.