Reconstruction of the Hirnantian (Late Ordovician) Palaeotopography in the Upper Yangtze Region

Reconstruction of the Hirnantian (Late Ordovician) palaeotopography in South China is important for understanding the distribution pattern of the Hirnantian marine depositional environment. In this study, we reconstructed the Hirnantian palaeotopography in the Upper Yangtze region based on the rankings of the palaeo-water depths, which were inferred according to the lithofacies and biofacies characteristics of the sections. Data from 374 Hirnantian sections were collected and standardized through the online Geobiodiversity Database. The Ordinary Kriging interpolation method in the ArcGIS software was applied to create the continuous surface of the palaeo-water depths, i.e. the Hirnantian palaeotopography. Meanwhile, the line transect analysis was used to further observe the terrain changes along two given directions. The reconstructed palaeotopographic map shows a relatively flat and shallow epicontinental sea with three local depressions and a submarine high on the Upper Yangtze region during the Hirnantian. The water depth is mostly less than 60 m and the Yangtze Sea gradually deepens towards the north

Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews

Identification of fossil species is crucial to evolutionary studies. Recent advances from deep learning have shown promising prospects in fossil image identification. However, the quantity and quality of labeled fossil images are often limited due to fossil preservation, conditioned sampling, and expensive and inconsistent label annotation by domain experts, which pose great challenges to the training of deep learning based image classification models. To address these challenges, we follow the idea of the wisdom of crowds and propose a novel multiview ensemble framework, which collects multiple views of each fossil specimen image reflecting its different characteristics to train multiple base deep learning models and then makes final decisions via soft voting. We further develop OGS method that integrates original, gray, and skeleton views under this framework to demonstrate the effectiveness. Experimental results on the fusulinid fossil dataset over five deep learning based milestone models show that OGS using three base models consistently outperforms the baseline using a single base model, and the ablation study verifies the usefulness of each selected view. Besides, OGS obtains the superior or comparable performance compared to the method under well-known bagging framework. Moreover, as the available training data decreases, the proposed framework achieves more performance gains compared to the baseline. Furthermore, a consistency test with two human experts shows that OGS obtains the highest agreement with both the labels of dataset and the two experts. Notably, this methodology is designed for general fossil identification and it is expected to see applications on other fossil datasets. The results suggest the potential application when the quantity and quality of labeled data are particularly restricted, e.g., to identify rare fossil images.Comment: preprint submitted to Methods in Ecology and Evolutio

Segment Anything Model for Medical Images?

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It designed a novel promotable segmentation task, ensuring zero-shot image segmentation using the pre-trained model via two main modes including automatic everything and manual prompt. SAM has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging due to the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. SAM has achieved impressive results on various natural image segmentation tasks. Meanwhile, zero-shot and efficient MIS can well reduce the annotation time and boost the development of medical image analysis. Hence, SAM seems to be a potential tool and its performance on large medical datasets should be further validated. We collected and sorted 52 open-source datasets, and build a large medical segmentation dataset with 16 modalities, 68 objects, and 553K slices. We conducted a comprehensive analysis of different SAM testing strategies on the so-called COSMOS 553K dataset. Extensive experiments validate that SAM performs better with manual hints like points and boxes for object perception in medical images, leading to better performance in prompt mode compared to everything mode. Additionally, SAM shows remarkable performance in some specific objects and modalities, but is imperfect or even totally fails in other situations. Finally, we analyze the influence of different factors (e.g., the Fourier-based boundary complexity and size of the segmented objects) on SAM's segmentation performance. Extensive experiments validate that SAM's zero-shot segmentation capability is not sufficient to ensure its direct application to the MIS.Comment: 23 pages, 14 figures, 12 table

Supplementary Data for "Synchronous asteroid breakup paused the Great Ordovician Biodiversification Event"

<p>Supplementary Data includes a detailed comparison of the YW2 borehole with the well-studied Puxi River section, the U-Pb isotopic data of the bentonite, and coupled carbon isotope data of the YW2 borehole. </p&gt

Pangaean aggregation and disaggregation with evidence from global climate belts

A study of using climate sensitive deposits as a compiled climatic data to locate global climatic belt boundaries through time is developed by the present authors since the 1990s. Global latitudinal belts were presented from Cambrian to Permian as well as the interval from the early Late Cretaceous to the present. However, during the later Permian and into the Early Cretaceous we noted that the failure of the tropical-subtropical belt to penetrate into the interior of Pangaean resulted in the merging of the two arid belts associated with the northern and southern Hadley Cells into one vast, interior arid region. A Pangaeanic paleogeography dominates and obviously affects the climatic distribution from the Late Permian to Early Cretaceous. We employ the dismission and reoccurrence of the global latitudinal climate belts to determine the aggregation and disaggregation of the Pangaean

Reconstruction of the mid-Hirnantian palaeotopography in the Upper Yangtze region, South China

Reconstruction of the Hirnantian (Late Ordovician) palaeotopography in South China is important for understanding the distribution pattern of the Hirnantian marine depositional environment. In this study, we reconstructed the Hirnantian palaeotopography in the Upper Yangtze region based on the rankings of the palaeo-water depths, which were inferred according to the lithofacies and biofacies characteristics of the sections. Data from 374 Hirnantian sections were collected and standardized through the online Geobiodiversity Database. The Ordinary Kriging interpolation method in the ArcGIS software was applied to create the continuous surface of the palaeo-water depths, i.e. the Hirnantian palaeotopography. Meanwhile, the line transect analysis was used to further observe the terrain changes along two given directions.The reconstructed palaeotopographic map shows a relatively flat and shallow epicontinental sea with three local depressions and a submarine high on the Upper Yangtze region during the Hirnantian. The water depth is mostly less than 60 m and the Yangtze Sea gradually deepens towards the north

Progress towards the establishment of the IUGS Deep-time Digital Earth (DDE) programme

The Deep-time Digital Earth (DDE) programme of the International Union of Geological Sciences (IUGS) has been developed to address the formidable challenge of so called ‘long tail’ data in the geosciences - the unstructured and inherently heterogeneous geoscience data that resides in institutions, universities and on individual geoscientists’ computers. DDE’s vision is to transform Earth science by connecting and harmonising long tail deep-time data ‘islands’ to support broad-based scientific studies relevant to the entire Earth system. The results of these and other studies will help us understand Earth’s natural environment and help in the wise use of natural resources for the prosperity of nations and the quality of human life. This harmonisation is now possible through the digital revolution, but new protocols, platforms and programs are needed to secure compatible and interoperable databases, so that the vast amounts of existing (and new) deep-time geoscience data can be linked. Since the first DDE meeting in January 2019, great progress has been made in defining statutes and byelaws, governance structures and preliminary informatics and scientific aims