By about 2.0 billion years ago (Ga), there is evidence for a period best known for its extended, apparent geochemical stability expressed famously in the carbonate-carbon isotope data. Despite the first appearance and early innovation among eukaryotic organisms, this period is also known for a rarity of eukaryotic fossils and an absence of organic biomarker fingerprints for those organisms, suggesting low diversity and relatively small populations compared to the Neoproterozoic era. Nevertheless, the search for diagnostic biomarkers has not been performed with guidance from paleoenvironmental redox constrains from inorganic geochemistry that should reveal the facies that were most likely hospitable to these organisms. Siltstones and shales obtained from drill core of the ca. 1.3-1.4 Ga Roper Group from the McArthur Basin of northern Australia provide one of our best windows into the mid-Proterozoic redox landscape. The group is well dated and minimally metamorphosed (of oil window maturity), and previous geochemical data suggest a relatively strong connection to the open ocean compared to other mid-Proterozoic records. Here, we present one of the first integrated investigations of Mesoproterozoic biomarker records performed in parallel with established inorganic redox proxy indicators. Results reveal a temporally variable paleoredox structure through the Velkerri Formation as gauged from iron mineral speciation and trace-metal geochemistry, vacillating between oxic and anoxic. Our combined lipid biomarker and inorganic geochemical records indicate at least episodic euxinic conditions sustained predominantly below the photic zone during the deposition of organic-rich shales found in the middle Velkerri Formation. The most striking result is an absence of eukaryotic steranes (4-desmethylsteranes) and only traces of gammacerane in some samples-despite our search across oxic, as well as anoxic, facies that should favor eukaryotic habitability and in low maturity rocks that allow the preservation of biomarker alkanes. The dearth of Mesoproterozoic eukaryotic sterane biomarkers, even within the more oxic facies, is somewhat surprising but suggests that controls such as the long-term nutrient balance and other environmental factors may have throttled the abundances and diversity of early eukaryotic life relative to bacteria within marine microbial communities. Given that molecular clocks predict that sterol synthesis evolved early in eukaryotic history, and (bacterial) fossil steroids have been found previously in 1.64 Ga rocks, then a very low environmental abundance of eukaryotes relative to bacteria is our preferred explanation for the lack of regular steranes and only traces of gammacerane in a few samples. It is also possible that early eukaryotes adapted to Mesoproterozoic marine environments did not make abundant steroid lipids or tetrahymanol in their cell membranes.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/gbi.12329 | DOI Listing |
Sci Rep
December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
View Article and Find Full Text PDFNat Commun
December 2024
Laboratory of Biochemistry, Wageningen University, Stippeneng 4, 6708WE, Wageningen, the Netherlands.
The Auxin Response Factors (ARFs) family of transcription factors are the central mediators of auxin-triggered transcriptional regulation. Functionally different classes of extant ARFs operate as antagonistic auxin-dependent and -independent regulators. While part of the evolutionary trajectory to the present auxin response functions has been reconstructed, it is unclear how ARFs emerged, and how early diversification led to functionally different proteins.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Pediatrics and Child Health Nursing, College of Medicine and Health Sciences, Bahir Dar, Ethiopia.
Introduction: Insecticide-treated bed nets are often used as a physical barrier to prevent infection of malaria. In Sub-Saharan Africa, one of the most important ways of reducing the malaria burden is the utilization of insecticide-treated bed nets. However, there is no sufficient information on the utilization of insecticide-treated bed nets and their associated factors in Ethiopia.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!