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The past ∼200 million years of Earth's geomagnetic field behavior have been recorded within oceanic basalts, many of which are only accessible via scientific ocean drilling. Obtaining the best possible paleomagnetic measurements from such valuable samples requires an a priori understanding of their magnetic mineralogies when choosing the most appropriate protocol for stepwise demagnetization experiments (either alternating field or thermal). Here, we present a quick, and non‐destructive method that utilizes the amplitude‐dependence of magnetic susceptibility to screen submarine basalts prior to choosing a demagnetization protocol, whenever conducting a pilot study or other detailed rock‐magnetic characterization is not possible. We demonstrate this method using samples acquired during International Ocean Discovery Program Expedition 391. Our approach is rooted in the observation that amplitude‐dependent magnetic susceptibility is observed in basalt samples whose dominant magnetic carrier is multidomain titanomagnetite (∼TM 60–65 , (Ti 0.60–0.65 Fe 0.35–0.40 )Fe 2 O 4 ). Samples with low Ti contents within titanomagnetite or samples that have experienced a high degree of oxidative weathering do not display appreciable amplitude dependence. Due to their low Curie temperatures, basalts that possess amplitude‐dependence should ideally be demagnetized either using alternating fields or via finely‐spaced thermal demagnetization heating steps below 300°C. Our screening method can enhance the success rate of paleomagnetic studies of oceanic basalt samples. Plain Language Summary Oceanic basalts are ideal recorders of the Earth's magnetic field. To decipher magnetic histories recorded in rocks, paleomagnetists need to isolate the magnetization directions and intensities within rocks by one of two possible methods. One method typically involves progressively heating the samples to high temperatures. The other method involves exposing samples to alternating magnetic fields wit

Valdivia Bank (VB) is a Late Cretaceous oceanic plateau formed by volcanism from the Tristan-Gough hotspot at the Mid-Atlantic Ridge (MAR). To better understand its origin and evolution, magnetic data were used to generate a magnetic anomaly grid, which was inverted to determine crustal magnetization. The magnetization model reveals quasi-linear polarity zones crossing the plateau and following expected MAR paleo-locations, implying formation by seafloor spreading over ∼4 Myr during the formation of anomalies C34n-C33r. Paleomagnetism and biostratigraphy data from International Ocean Discovery Program Expedition 391 confirm the magnetic interpretation. Anomaly C33r is split into two negative bands, likely by a westward ridge jump. One of these negative anomalies coincides with deep rift valleys, indicating their age and mechanism of formation. These findings imply that VB originated by seafloor spreading-type volcanism during a plate reorganization, not from a vertical stack of lava flows as expected for a large volcano. Key Points - Valdivia Bank is characterized by quasi-linear magnetic anomalies that are parallel to the inferred paleo-Mid-Atlantic Ridge - Magnetic anomalies imply that the plateau becomes younger E-W consistent with formation via seafloor spreading during anomalies C34n-C33r - Rift valleys, division of C33r, and anomaly curvature imply complex ridge tectonics and a ridge jump

The magnitude of future waves of Covid19 in a population will depend, in part, on the percentage of that population already infected, recovered, and presumably immune. Sero-epidemiological surveys can define the prevalence of SARS-CoV-2 antibodies in various populations. However, sero-surveys are resource-intensive and methodologically challenging, limiting widespread use. We propose a relatively simple method for calculating the percentage of a population infected, which depends on the number of reported Covid19 deaths, a figure usually more reliable and less dependent on variable testing practices than the total number of reported Covid19 cases, and the infection fatality rate, a figure that is relatively stable in similar populations. The method can be applied in different sized areas, such as states, districts, or cities. Such an approach can provide useful, real-time estimates of probable population immunity in settings unable to undertake multiple sero-surveys. This method is applicable to low- and lower-middle-income country (LMIC) settings where sero-survey data will likely be limited; however, better estimates of infection fatality rates and Covid19 death counts in LMICs are needed to improve the method's accuracy. Information on the percentage of a population infected will help public health authorities in planning for future waves of Covid19, including where to most effectively deploy vaccines.

BACKGROUND: The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) provides an up-to-date analysis of the burden of diarrhoeal diseases. This study assesses cases, deaths, and aetiologies spanning the past 25 years and informs the changing picture of diarrhoeal disease worldwide. METHODS: We estimated diarrhoeal mortality by age, sex, geography, and year using the Cause of Death Ensemble Model (CODEm), a modelling platform shared across most causes of death in the GBD 2015 study. We modelled diarrhoeal morbidity, including incidence and prevalence, using a meta-regression platform called DisMod-MR. We estimated aetiologies for diarrhoeal diseases using a counterfactual approach that incorporates the aetiology-specific risk of diarrhoeal disease and the prevalence of the aetiology in diarrhoea episodes. We used the Socio-demographic Index, a summary indicator derived from measures of income per capita, educational attainment, and fertility, to assess trends in diarrhoeal mortality. The two leading risk factors for diarrhoea-childhood malnutrition and unsafe water, sanitation, and hygiene-were used in a decomposition analysis to establish the relative contribution of changes in diarrhoea disability-adjusted life-years (DALYs). FINDINGS: Globally, in 2015, we estimate that diarrhoea was a leading cause of death among all ages (1·31 million deaths, 95% uncertainty interval [95% UI] 1·23 million to 1·39 million), as well as a leading cause of DALYs because of its disproportionate impact on young children (71·59 million DALYs, 66·44 million to 77·21 million). Diarrhoea was a common cause of death among children under 5 years old (499 000 deaths, 95% UI 447 000-558 000). The number of deaths due to diarrhoea decreased by an estimated 20·8% (95% UI 15·4-26·1) from 2005 to 2015. Rotavirus was the leading cause of diarrhoea deaths (199 000, 95% UI 165 000-241 000), followed by Shigella spp (164 300, 85 000-278 700) and Salmonella spp (90 300, 95% UI 3