Vegetative and Leaf Anatomical Traits for Taxonomic Delimitation of Salacia L. In Sri Lanka

Salacia comprises 200 species throughout the tropical countries. In Sri Lanka, five species of this genus S. chinensis, S. diandra, S. oblonga, S. reticulata and S. acuminatissima are recorded that are considered as the synonyms under S. diandra by Wadhwa in 1996 during the revision of genus. These species are very much similar in vegetative morphology and their flowering is irregular and rare. Due to these reasons, their proper identification and determination are difficult and identification of new combinations of taxonomic characters is necessary. A multivariate analysis was conducted for 98 distinct populations using 20 vegetative and 43 leaf anatomical characters including PCA, PCO, CA, SIMPER and correlation analysis. The results support the recognition of four phenetic groups, which corresponds to species S. chinensis, S. reticulata, S. oblonga and S. diandra. S. acuminatissima that was recognized by Kostermans (1992) was not supported by the analysis and formed a cluster together with S. diandra with no support as a separate cluster. The study failed to recognize any distinct vegetative characters to define these taxa but propose a combination of vegetative or leaf anatomical characters and also highlights the necessity of molecular data to supplement the vegetative and leaf anatomical data to resolve the ambiguity between the S. acuminatissima and S. diandra. Bangladesh J. Plant Taxon. 28(2): 429-439, 2021 (December)

What is our method of functional diagnostics of the kidneys and what does it allow

After I proposed a simple clinical method for determining the functional ability of the kidneys, I was repeatedly asked the question - how to reconcile the data I obtained with the pathological and anatomical data, or how to build a bridge between them and, say, the classification of Vоlhard and Fаhr? !

A new Heterodontosaurus specimen elucidates the unique ventilatory macroevolution of ornithischian dinosaurs

Ornithischian dinosaurs were ecologically prominent herbivores of the Mesozoic Era that achieved a global distribution by the onset of the Cretaceous. The ornithischian body plan is aberrant relative to other ornithodiran clades, and crucial details of their early evolution remain obscure. We present a new, fully articulated skeleton of the early branching ornithischian Heterodontosaurus tucki. Phase-contrast enhanced synchrotron data of this new specimen reveal a suite of novel postcranial features unknown in any other ornithischian, with implications for the early evolution of the group. These features include a large, anteriorly projecting sternum; bizarre, paddle-shaped sternal ribs; and a full gastral basket – the first recovered in Ornithischia. These unusual anatomical traits provide key information on the evolution of the ornithischian body plan and suggest functional shifts in the ventilatory apparatus occurred close to the base of the clade. We complement these anatomical data with a quantitative analysis of ornithischian pelvic architecture, which allows us to make a specific, stepwise hypothesis for their ventilatory evolution.

Graph Theory for Modeling and Analysis of the Human Lymphatic System

The human lymphatic system (HLS) is a complex network of lymphatic organs linked through the lymphatic vessels. We present a graph theory-based approach to model and analyze the human lymphatic network. Two different methods of building a graph are considered: the method using anatomical data directly and the method based on a system of rules derived from structural analysis of HLS. A simple anatomical data-based graph is converted to an oriented graph by quantifying the steady-state fluid balance in the lymphatic network with the use of the Poiseuille equation in vessels and the mass conservation at vessel junctions. A computational algorithm for the generation of the rule-based random graph is developed and implemented. Some fundamental characteristics of the two types of HLS graph models are analyzed using different metrics such as graph energy, clustering, robustness, etc.

Wing musculature reconstruction in extinct flightless auks (Pinguinus and Mancalla) reveals incomplete convergence with penguins (Spheniscidae) due to differing ancestral states

Despite longstanding interest in convergent evolution, factors that result in deviations from fully convergent phenotypes remain poorly understood. In birds, the evolution of flightless wing-propelled diving has emerged as a classic example of convergence, having arisen in disparate lineages including penguins (Sphenisciformes) and auks (Pan-Alcidae, Charadriiformes). Nevertheless, little is known about the functional anatomy of the wings of flightless auks because all such taxa are extinct, and their morphology is almost exclusively represented by skeletal remains. Here, in order to re-evaluate the extent of evolutionary convergence among flightless wing-propelled divers, wing muscles and ligaments were reconstructed in two extinct flightless auks, representing independent transitions to flightlessness: Pinguinus impennis (a crown-group alcid), and Mancalla (a stem-group alcid). Extensive anatomical data were gathered from dissections of 12 species of extant charadriiforms and 4 aequornithine waterbirds including a penguin. The results suggest that the wings of both flightless auk taxa were characterized by an increased mechanical advantage of wing elevator/retractor muscles, and decreased mobility of distal wing joints, both of which are likely advantageous for wing-propelled diving and parallel similar functional specializations in penguins. However, the conformations of individual muscles and ligaments underlying these specializations differ markedly between penguins and flightless auks, instead resembling those in each respective group’s close relatives. Thus, the wings of these flightless wing-propelled divers can be described as convergent as overall functional units, but are incompletely convergent at lower levels of anatomical organization—a result of retaining differing conditions from each group’s respective volant ancestors. Detailed investigations such as this one may indicate that, even in the face of similar functional demands, courses of phenotypic evolution are dictated to an important degree by ancestral starting points.

Gross Morphological and Morphometrical Studies on the Sternum of Asian Koel (Eudynamys scolopaceus)

Background: The sternum is one of the most important and characteristic skeletal elements in birds, highly adapted for flight and showing a diverse range of morphogenesis. The Asian koel (Eudynamys scolopaceus) is a member of the Cuckoo order of birds, found in the Indian sub-continent, China and Southeast Asia. The present study was carried out as anatomical data on the sternum of this species is scanty. Methods: Carcass of Asian koel brought to the Department of Wildlife Science, Madras Veterinary College, Chennai after the post-mortem examination was biologically macerated, cleaned, dried and morphological and morphometric studies were performed. Result: Gross morphological and morphometric data of the sternum of Asian koel was compared with other avian species. The sternum of Asian koel was large, broad, quadrilateral shaped and highly pneumatized bone with well developed sternal crest, craniolateral and caudolateral processes. These features provide protection to the viscera and facilitates superior flight.

Deep Learning Based Segmentation of Brain Tissue from Diffusion MRI

Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required for quantification of brain microstructure and for improving tractography. Current dMRI segmentation is mostly based on anatomical MRI (e.g., T1- and T2-weighted) segmentation that is registered to the dMRI space. However, such inter-modality registration is challenging due to more image distortions and lower image resolution in the dMRI data as compared with the anatomical MRI data. In this study, we present a deep learning method that learns tissue segmentation from high-quality imaging datasets from the Human Connectome Project (HCP), where registration of anatomical data to dMRI is more precise. The method is then able to predict a tissue segmentation directly from new dMRI data, including data collected with a different acquisition protocol, without requiring anatomical data and inter-modality registration. We train a convolutional neural network (CNN) to learn a tissue segmentation model using a novel augmented target loss function designed to improve accuracy in regions of tissue boundary. To further improve accuracy, our method adds diffusion kurtosis imaging (DKI) parameters that characterize non-Gaussian water molecule diffusion to the conventional diffusion tensor imaging parameters. The DKI parameters are calculated from the recently proposed mean-kurtosis-curve method that corrects implausible DKI parameter values and provides additional features that discriminate between tissue types. We demonstrate high tissue segmentation accuracy on HCP data, and also when applying the HCP-trained model on dMRI data from a clinical acquisition with lower resolution and fewer gradient directions.

Wing musculature reconstruction in extinct flightless auks (Pinguinus and Mancalla) reveals incomplete convergence with penguins (Spheniscidae) due to differing ancestral states

Despite longstanding interest in convergent evolution, factors that result in deviations from fully convergent phenotypes remain poorly understood. In birds, the evolution of flightless wing-propelled diving has emerged as a classic example of convergence, having arisen in disparate lineages including penguins (Sphenisciformes) and auks (Pan-Alcidae, Charadriiformes). Nevertheless, little is known about the functional anatomy of the wings of flightless auks because all such taxa are extinct, and their morphology is almost exclusively represented by skeletal remains. Here, in order to re-evaluate the extent of evolutionary convergence among flightless wing-propelled divers, wing muscles and ligaments were reconstructed in two extinct flightless auks, representing independent transitions to flightlessness: Pinguinus impennis (a crown-group alcid), and Mancalla (a stem-group alcid). Extensive anatomical data were gathered from dissections of 12 species of extant charadriiforms and 4 aequornithine waterbirds including a penguin. It was found that the wings of both flightless auk taxa were characterized by an increased mechanical advantage of wing elevator/retractor muscles, and decreased mobility of distal wing joints, both of which are likely advantageous for wing-propelled diving and parallel similar functional specializations in penguins. However, the conformations of individual muscles and ligaments underlying these specializations differ markedly between penguins and flightless auks, instead resembling those in each respective group’s close relatives. Thus, the wings of these flightless wing-propelled divers can be described as convergent as overall functional units, but are incompletely convergent at lower levels of anatomical organization—a result of retaining differing conditions from each group’s respective volant ancestors. Detailed investigations such as this one may indicate that, even in the face of similar functional demands, courses of phenotypic evolution are dictated to an important degree by ancestral starting points.

Development and validation of statistical shape models of the primary functional bone segments of the foot

Introduction Musculoskeletal models are important tools for studying movement patterns, tissue loading, and neuromechanics. Personalising bone anatomy within models improves analysis accuracy. Few studies have focused on personalising foot bone anatomy, potentially incorrectly estimating the foot’s contribution to locomotion. Statistical shape models have been created for a subset of foot-ankle bones, but have not been validated. This study aimed to develop and validate statistical shape models of the functional segments in the foot: first metatarsal, midfoot (second-to-fifth metatarsals, cuneiforms, cuboid, and navicular), calcaneus, and talus; then, to assess reconstruction accuracy of these shape models using sparse anatomical data. Methods Magnetic resonance images of 24 individuals feet (age = 28 ± 6 years, 52% female, height = 1.73 ± 0.8 m, mass = 66.6 ± 13.8 kg) were manually segmented to generate three-dimensional point clouds. Point clouds were registered and analysed using principal component analysis. For each bone segment, a statistical shape model and principal components were created, describing population shape variation. Statistical shape models were validated by assessing reconstruction accuracy in a leave-one-out cross validation. Statistical shape models were created by excluding a participant’s bone segment and used to reconstruct that same excluded bone using full segmentations and sparse anatomical data (i.e. three discrete points on each segment), for all combinations in the dataset. Tali were not reconstructed using sparse anatomical data due to a lack of externally accessible landmarks. Reconstruction accuracy was assessed using Jaccard index, root mean square error (mm), and Hausdorff distance (mm). Results Reconstructions generated using full segmentations had mean Jaccard indices between 0.77 ± 0.04 and 0.89 ± 0.02, mean root mean square errors between 0.88 ± 0.19 and 1.17 ± 0.18 mm, and mean Hausdorff distances between 2.99 ± 0.98 mm and 6.63 ± 3.68 mm. Reconstructions generated using sparse anatomical data had mean Jaccard indices between 0.67 ± 0.06 and 0.83 ± 0.05, mean root mean square error between 1.21 ± 0.54 mm and 1.66 ± 0.41 mm, and mean Hausdorff distances between 3.21 ± 0.94 mm and 7.19 ± 3.54 mm. Jaccard index was higher (P < 0.01) and root mean square error was lower (P < 0.01) in reconstructions from full segmentations compared to sparse anatomical data. Hausdorff distance was lower (P < 0.01) for midfoot and calcaneus reconstructions using full segmentations compared to sparse anatomical data. Conclusion For the first time, statistical shape models of the primary functional segments of the foot were developed and validated. Foot segments can be reconstructed with minimal error using full segmentations and sparse anatomical landmarks. In future, larger training datasets could increase statistical shape model robustness, extending use to paediatric or pathological populations.