Manganese mineralogy and diagenesis in the sedimentary rock record

Oxidation of manganese(II) to manganese(III,IV) demands oxidants with very high redox potentials; consequently, manganese oxides are both excellent proxies for molecular oxygen and highly favorable electron acceptors when oxygen is absent. The first of these features results in manganese-enriched sedimentary rocks (manganese deposits, commonly Mn ore deposits), which generally correspond to the availability of molecular oxygen in Earth surface environments. And yet because manganese reduction is promoted by a variety of chemical species, these ancient manganese deposits are often significantly more reduced than modern environmental manganese-rich sediments. We document the impacts of manganese reduction and the mineral phases that form stable manganese deposits from seven sedimentary examples spanning from modern surface environments to rocks over 2 billion years old. Integrating redox and coordination information from synchrotron X-ray absorption spectroscopy and X-ray microprobe imaging with scanning electron microscopy and energy and wavelength-dispersive spectroscopy, we find that unlike the Mn(IV)-dominated modern manganese deposits, three manganese minerals dominate these representative ancient deposits: kutnohorite (CaMn(CO_3)_2), rhodochrosite (MnCO_3), and braunite (Mn(III)_6Mn(II)O_8SiO_4). Pairing these mineral and textural observations with previous studies of manganese geochemistry, we develop a paragenetic model of post-depositional manganese mineralization with kutnohorite and calcian rhodochrosite as the earliest diagenetic mineral phases, rhodochrosite and braunite forming secondarily, and later alteration forming Mn-silicates

Low‐Fe(III) Greenalite Was a Primary Mineral From Neoarchean Oceans

Banded iron formations (BIFs) represent chemical precipitation from Earth’s early oceans and therefore contain insights into ancient marine biogeochemistry. However, BIFs have undergone multiple episodes of alteration, making it difficult to assess the primary mineral assemblage. Nanoscale mineral inclusions from 2.5 billion year old BIFs and ferruginous cherts provide new evidence that iron silicates were primary minerals deposited from the Neoarchean ocean, contrasting sharply with current models for BIF inception. Here we used multiscale imaging and spectroscopic techniques to characterize the best preserved examples of these inclusions. Our integrated results demonstrate that these early minerals were low‐Fe(III) greenalite. We present potential pathways in which low‐Fe(III) greenalite could have formed through changes in saturation state and/or iron oxidation and reduction. Future constraints for ancient ocean chemistry and early life’s activities should include low‐Fe(III) greenalite as a primary mineral in the Neoarchean ocean.Plain Language SummaryChemical precipitates from Earth’s early oceans hold clues to ancient seawater chemistry and biological activities, but we first need to understand what the original minerals were in ancient marine deposits. We characterized nanoscale mineral inclusions from 2.5 billion year old banded iron formations and determined that the primary minerals were iron‐rich silicate minerals dominated by reduced iron, challenging current hypotheses for banded iron formation centered on iron oxides. Our results suggest that our planet at this time had a very reducing ocean and further enable us to present several biogeochemical mineral formation hypotheses that can now be tested to better understand the activities of early life on ancient Earth.Key PointsNeoarchean nanoparticle silicate inclusions appear to be the earliest iron mineral preserved in cherts from Australia and South AfricaOur multiscale analyses indicate that the particles are greenalite that are dominantly Fe(II) with have low and variable Fe(III) contentWe present four (bio)geochemical hypotheses that could produce low‐Fe(III) greenalitePeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143747/1/grl57046_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143747/2/grl57046.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143747/3/grl57046-sup-0001-2017GL076311-SI.pd

Prioritizing research for integrated implementation of early childhood development and maternal, newborn, child and adolescent health and nutrition platforms

Background: Existing health and nutrition services present potential platforms for scaling up delivery of early childhood development (ECD) interventions within sensitive windows across the life course, especially in the first 1000 days from conception to age 2 years. However, there is insufficient knowledge on how to optimize implementation for such strategies in an integrated manner. In light of this knowledge gap, we aimed to systematically identify a set of integrated implementation research priorities for health, nutrition and early child development within the 2015 to 2030 timeframe of the Sustainable Development Goals (SDGs).Methods: We applied the Child Health and Nutrition Research Initiative method, and consulted a diverse group of global health experts to develop and score 57 research questions against five criteria: answerability, effectiveness, deliverability, impact, and effect on equity. These questions were ranked using a research priority score, and the average expert agreement score was calculated for each question.Findings: The research priority scores ranged from 61.01 to 93.52, with a median of 82.87. The average expert agreement scores ranged from 0.50 to 0.90, with a median of 0.75. The top-ranked research question were: i) How can interventions and packages to reduce neonatal mortality be expanded to include ECD and stimulation interventions? ; ii) How does the integration of ECD and MNCAH&N interventions affect human resource requirements and capacity development in resource-poor settings? ; and iii) How can integrated interventions be tailored to vulnerable refugee and migrant populations to protect against poor ECD and MNCAH&N outcomes? . Most highly-ranked research priorities varied across the life course and highlighted key aspects of scaling up coverage of integrated interventions in resource-limited settings, including: workforce and capacity development, cost-effectiveness and strategies to reduce financial barriers, and quality assessment of programs.Conclusions: Investing in ECD is critical to achieving several of the SDGs, including SDG 2 on ending all forms of malnutrition, SDG 3 on ensuring health and well-being for all, and SDG 4 on ensuring inclusive and equitable quality education and promotion of life-long learning opportunities for all. The generated research agenda is expected to drive action and investment on priority approaches to integrating ECD interventions within existing health and nutrition services

Reply to Jones and Crowe: Correcting mistaken views of sedimentary geology, Mn-oxidation rates, and molecular clocks

Jones and Crowe (1) raise issues already addressed in our article (2) based on an inaccurate grasp of the literature and several logical misconceptions. The authors suggest that inputs we chose in our kinetic calculations are unsuitable because we used values only from the Black Sea. As described, we made an extremely conservative estimate because the Black Sea is the most rapid Mn-oxidizing environment in the literature. Other locations have oxidation rates orders-of-magnitude lower (3). Jones and Crowe also propose sedimentation rates in our Mn-oxidation calculations were too high, citing a reference for incorrect rocks: different lithologies, environments, process sedimentology, geodynamic setting, and age

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm’s pa- rameters and data-related modeling choices are also both crucial and challenging

Are fewer cases of diabetes mellitus diagnosed in the months after SARS-CoV-2 infection? A population-level view in the EHR-based RECOVER program

Long-term sequelae of severe acute respiratory coronavirus-2 (SARS-CoV-2) infection may include increased incidence of diabetes. Here we describe the temporal relationship between new type 2 diabetes and SARS-CoV-2 infection in a nationwide database. We found that while the proportion of newly diagnosed type 2 diabetes increased during the acute period of SARS-CoV-2 infection, the mean proportion of new diabetes cases in the 6 months post-infection was about 83% lower than the 6 months preinfection. These results underscore the need for further investigation to understand the timing of new diabetes after COVID-19, etiology, screening, and treatment strategies

Are fewer cases of diabetes mellitus diagnosed in the months after SARS-CoV-2 infection? A population-level view in the EHR-based RECOVER program

Long-term sequelae of severe acute respiratory coronavirus-2 (SARS-CoV-2) infection may include increased incidence of diabetes. Here we describe the temporal relationship between new type 2 diabetes and SARS-CoV-2 infection in a nationwide database. We found that while the proportion of newly diagnosed type 2 diabetes increased during the acute period of SARS-CoV-2 infection, the mean proportion of new diabetes cases in the 6 months post-infection was about 83% lower than the 6 months preinfection. These results underscore the need for further investigation to understand the timing of new diabetes after COVID-19, etiology, screening, and treatment strategies

Sedimentology, chemostratigraphy, and stromatolites of lower Paleoproterozoic carbonates, Turee Creek Group, Western Australia

The ca. 2.45–2.22 Ga Turee Creek Group, Western Australia, contains carbonate- rich horizons that postdate earliest Proterozoic iron formations, bracket both Paleoproterozoic glaciogenic beds and the onset of the Great Oxidation Event (GOE), and predate ca. 2.2–2.05 Ga Lomagundi-Jatuli C-isotopic excursion(s). As such, Turee Creek carbonate strata provide an opportunity to characterize early Paleoproterozoic carbonate sedimentation and carbon cycle dynamics in the context of significant global change. Here, we report on the stratigraphy, sedimentology, petrology, carbon isotope chemostratigraphy, and stromatolite development for carbonate-rich successions within the pre-glacial part of the Kungarra Formation and the postglacial Kazput Formation. Kungarra carbonate units largely occur as laterally discontinuous beds within a thick, predominantly siliciclastic shelf deposit. While this succession contains thin microbialite horizons, most carbonates consist of patchy calcite overgrowths within a siliciclastic matrix. C-isotopic values show marked variation along a single horizon and even within hand samples, reflecting spatially and temporally variable mixing between dissolved inorganic carbon in seawater and isotopically light inorganic carbon generated via syn- and post-depositional remineralization of organic matter. In contrast, the Kazput carbonates consist of subtidal stromatolites, grainstones, and micrites deposited on a mixed carbonate-siliciclastic shelf. These carbonates exhibit moderate δ13 C values of -2‰ to +1.5‰ and likely preserve a C-isotopic signature of seawater. Kazput carbonates, thus, provide some of the best available evidence that an interval of unexceptional C-isotopic values separates the Lomagundi-Jatuli C-isotopic excursion(s) from the initiation of the GOE as inferred from multiple sulfur isotopes (loss 4 of mass independent fractionation). The Kazput Formation also contains unusual, m-scale stromatolitic buildups, which are composed of sub-mm laminae and discontinuous, convex upward lenticular precipitates up to a few mm in maximum thickness. Laminae, interpreted as microbial mat layers, contain quartz and clay minerals as well as calcite, whereas precipitate lenses consist of interlocking calcite anhedra, sometimes showing faint mm-scale banding. These cements formed either as infillings of primary voids formed by gas emission within penecontemporaneously lithified mats, or as local seafloor precipitates that formed on, or within, surface mats. It is possible that both mechanisms interacted to form the unique Kazput stromatolites. These microbialites speak to a distinctive interaction between life and environment early in the Paleoproterozoic Era.Earth and Planetary Science

Spin dynamics in semiconductors

This article reviews the current status of spin dynamics in semiconductors which has achieved a lot of progress in the past years due to the fast growing field of semiconductor spintronics. The primary focus is the theoretical and experimental developments of spin relaxation and dephasing in both spin precession in time domain and spin diffusion and transport in spacial domain. A fully microscopic many-body investigation on spin dynamics based on the kinetic spin Bloch equation approach is reviewed comprehensively.Comment: a review article with 193 pages and 1103 references. To be published in Physics Reports