First Record of the Belemnite Genus Hibolithes from Late Jurassic–Early Cretaceous Turbidites from Malaysia

Discovery of the remains of belemnites referred to the Hibolithes sp.from the Jurassic–Cretaceous Pedawan Formation in Sarawak,on the island of Borneo(Malaysia)comprises four fragments of belemnite rostra.The specimens are characterized by multiple fractures and vein filling.Two fragments measuring about 130 mm are relatively intact,with only part of the alveolar region missing;a third piece represents the middle part of a rostrum,and the fourth specimen is too fragmentary to be assigned to any specific part of the rostrum.Based on specimen characteristics,a Tithonian–Hauterivian age is plausible.The sedimentary succession that yielded the belemnite material comprises thick shale that reflects the Te division of Bouma sequence.The occurrence of a Hibolithes taxon in the uppermost Jurassic to lowermost Cretaceous Pedawan Formation sediments in Borneo reflects a near to global palaeobiogeographic distribution of this genus.

Epithelial Layer Estimation Using Curvatures and Textural Features for Dysplastic Tissue Detection

Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process.The morphological pattern of an epithelial layer is greatly affected.Theoretically,the shape deformation model can normalise the distortions,but it needs a 2D image.Curvatures theory,on the other hand,is not yet tested on digital pathology images.Therefore,this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial layer.The boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight line.The epithelial layer’s depth is estimated from the tissue edges and the connected nucleolus only.Then,the textural and spatial features along the estimated layer are used for dysplastic tissue detection.The proposed method achieved better performance compared to the whole tissue regions in terms of detecting dysplastic tissue.The result shows a leap of kappa points from fair to a substantial agreement with the expert’s ground truth classification.The improved results demonstrate that curvatures have been effective in reducing the boundary effects on the epithelial layer of tissue.Thus,quantifying and classifying the morphological patterns for dysplasia can be automated.The textural and spatial features on the detected epithelial layer can capture the changes in tissue.