Cadherin flexibility provides a key difference between desmosomes and adherens junctions.

Desmosomes and adherens junctions are intercellular adhesive structures essential for the development and integrity of vertebrate tissue, including the epidermis and heart. Their cell adhesion molecules are cadherins: type 1 cadherins in adherens junctions and desmosomal cadherins in desmosomes. A fundamental difference is that desmosomes have a highly ordered structure in their extracellular region and exhibit calcium-independent hyperadhesion, whereas adherens junctions appear to lack such ordered arrays, and their adhesion is always calcium-dependent. We present here the structure of the entire ectodomain of desmosomal cadherin desmoglein 2 (Dsg2), using a combination of small-angle X-ray scattering, electron microscopy, and solution-based biophysical techniques. This structure reveals that the ectodomain of Dsg2 is flexible even in the calcium-bound state and, on average, is shorter than the type 1 cadherin crystal structures. The Dsg2 structure has an excellent fit with the electron tomography reconstructions of human desmosomes. This fit suggests an arrangement in which desmosomal cadherins form trans interactions but are too far apart to interact in cis, in agreement with previously reported observations. Cadherin flexibility may be key to explaining the plasticity of desmosomes that maintain tissue integrity in their hyperadhesive form, but can adopt a weaker, calcium-dependent adhesion during wound healing and early development.

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks.

This research comprises experiments with a deep learning framework for fully automating the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for segmentation have progressed to the extent of Convolutional Neural Networks (CNN). We proposed and experimented with a contemporary variant of the deep learning framework based on mask region convolutional neural network (Mask-RCNN) for all anatomical orientations of brain MR images. We trained the system from scratch to build a model for classification, detection, and segmentation. It is validated by images taken from three different datasets: BrainWeb; NAMIC, and a local hospital. We opted for purposive sampling to select 2000 images of T1 modality from data volumes followed by a multi-stage random sampling technique to segregate the dataset into three batches for training (75%), validation (15%), and testing (10%) respectively. We utilized a robust backbone architecture, namely ResNet-101 and Functional Pyramid Network (FPN), to achieve optimal performance with higher accuracy. We subjected the same data to two traditional methods, namely Brain Extraction Tools (BET) and Brain Surface Extraction (BSE), to compare their performance results. Our proposed method had higher mean average precision (mAP) = 93% and content validity index (CVI) = 0.95%, which were better than comparable methods. We contributed by training Mask-RCNN from scratch for generating reusable learning weights known as transfer learning. We contributed to methodological novelty by applying a pragmatic research lens, and used a mixed method triangulation technique to validate results on all anatomical modalities of brain MR images. Our proposed method improved the accuracy and precision of skull stripping by fully automating it and reducing its processing time and operational cost and reliance on technicians. This research study has also provided grounds for extending the work to the scale of explainable artificial intelligence (XAI).

Improved Repeatability of Mouse Tibia Volume Segmentation in Murine Myelofibrosis Model Using Deep Learning.

A murine model of myelofibrosis in tibia was used in a co-clinical trial to evaluate segmentation methods for application of image-based biomarkers to assess disease status. The dataset (32 mice with 157 3D MRI scans including 49 test-retest pairs scanned on consecutive days) was split into approximately 70% training, 10% validation, and 20% test subsets. Two expert annotators (EA1 and EA2) performed manual segmentations of the mouse tibia (EA1: all data; EA2: test and validation). Attention U-net (A-U-net) model performance was assessed for accuracy with respect to EA1 reference using the average Jaccard index (AJI), volume intersection ratio (AVI), volume error (AVE), and Hausdorff distance (AHD) for four training scenarios: full training, two half-splits, and a single-mouse subsets. The repeatability of computer versus expert segmentations for tibia volume of test-retest pairs was assessed by within-subject coefficient of variance (%wCV). A-U-net models trained on full and half-split training sets achieved similar average accuracy (with respect to EA1 annotations) for test set: AJI = 83-84%, AVI = 89-90%, AVE = 2-3%, and AHD = 0.5 mm-0.7 mm, exceeding EA2 accuracy: AJ = 81%, AVI = 83%, AVE = 14%, and AHD = 0.3 mm. The A-U-net model repeatability wCV [95% CI]: 3 [2, 5]% was notably better than that of expert annotators EA1: 5 [4, 9]% and EA2: 8 [6, 13]%. The developed deep learning model effectively automates murine bone marrow segmentation with accuracy comparable to human annotators and substantially improved repeatability.

The TriTryp phosphatome: analysis of the protein phosphatase catalytic domains.

BACKGROUND: The genomes of the three parasitic protozoa Trypanosoma cruzi, Trypanosoma brucei and Leishmania major are the main subject of this study. These parasites are responsible for devastating human diseases known as Chagas disease, African sleeping sickness and cutaneous Leishmaniasis, respectively, that affect millions of people in the developing world. The prevalence of these neglected diseases results from a combination of poverty, inadequate prevention and difficult treatment. Protein phosphorylation is an important mechanism of controlling the development of these kinetoplastids. With the aim to further our knowledge of the biology of these organisms we present a characterisation of the phosphatase complement (phosphatome) of the three parasites. RESULTS: An ontology-based scan of the three genomes was used to identify 86 phosphatase catalytic domains in T. cruzi, 78 in T. brucei, and 88 in L. major. We found interesting differences with other eukaryotic genomes, such as the low proportion of tyrosine phosphatases and the expansion of the serine/threonine phosphatase family. Additionally, a large number of atypical protein phosphatases were identified in these species, representing more than one third of the total phosphatase complement. Most of the atypical phosphatases belong to the dual-specificity phosphatase (DSP) family and show considerable divergence from classic DSPs in both the domain organisation and sequence features. CONCLUSION: The analysis of the phosphatome of the three kinetoplastids indicates that they possess orthologues to many of the phosphatases reported in other eukaryotes, including humans. However, novel domain architectures and unusual combinations of accessory domains, suggest distinct functional roles for several of the kinetoplastid phosphatases, which await further experimental exploration. These distinct traits may be exploited in the selection of suitable new targets for drug development to prevent transmission and spread of the diseases, taking advantage of the already extensive knowledge on protein phosphatase inhibitors.