Cadmium Isotope Variations in the Oceans

A number of previous studies have identified considerable mass dependent variations in the Cd isotope compositions of both terrestrial and extraterrestrial samples. On Earth, stable isotope effects for Cd are particularly prominent in the oceans, and the largest natural terrestrial Cd isotope fractionations of about 4‰ have been reported for Cd-depleted surface seawater. These effects have generally been attributed to reflect isotope fractionation of Cd that occurs during biological uptake and utilization of dissolved seawater Cd. This finding confirms studies, which identified Cd as an essential marine micronutrient. This was first inferred from the phosphate-like distribution of the metal in the oceans and more recently demonstrated by work, which confirmed that Cd can act as catalytic metal ion in carbonic anhydrase, an enzyme which plays a central role in inorganic carbon acquisition of phytoplankton in the oceans. The marine Cd isotope fractionations are thus of interest, as they can be used to study the cycling of the micronutrient Cd as well as its impact on ocean productivity and the global carbon cycle. As part of this PhD project, I have developed a new procedure for Cd isotope analyses of seawater, which is suitable for samples as large as 20 L and Cd concentrations as low as 1 pmol/L. The procedure involves use of a 111Cd-113Cd double spike, co-precipitation of Cd from seawater with Al(OH)3 Cd purification by column chromatography, and subsequent isotope analysis by MC-ICP-MS (multiple collector inductively coupled plasma mass spectrometry). The methodology can routinely provide є114=110Cd data with a precision of about ± 0:5є (2sd) when at least 20-30 ng of natural Cd are available for analysis. However, even seawater samples with Cd contents of only 1-3 ng can be analysed with a reproducibility of about ±3 to ± 5є. The new methodology was applied to investigate Cd isotope variations in about 150 seawater samples from the North Atlantic Ocean, the Southern Ocean HNLC (high nutrient low chlorophyll) region, and the Peruvian oxygen minimum zone in the Eastern Pacific Ocean. The samples exhibited variable but highly systematic Cd isotope variations that were comprehensively interpreted in the context of previously published oceanographic and biogeochemical data

Isotopic evidence for complex biogeochemical cycling of Cd in the eastern tropical South Pacific

Over the past decades, observations have confirmed decreasing oxygen levels and shoaling of oxygen minimum zones (OMZs) in the tropical oceans. Such changes impact the biogeochemical cycling of micronutrients such as Cd, but the potential consequences are only poorly constrained. Here, we present seawater Cd concentrations and isotope compositions for 12 depth profiles at coastal, nearshore and offshore stations from 4◦S to 14◦S in the eastern tropical South Pacific, where one of the world’s strongest OMZs prevails. The depth profiles of Cd isotopes display high δ114/110Cd at the surface and decreasing δ114/110Cd with increasing water depth, consistent with preferential utilization of lighter Cd isotopes during biological uptake in the euphotic zone and subsequent remineralization of the sinking biomass. In the surface and subsurface ocean, seawater displays similar δ114/110Cd signatures of 0.47 ± 0.23‰ to 0.82 ± 0.05‰ across the entire eastern tropical South Pacific despite highly variable Cd concentrations between 0.01 and 0.84 nmol/kg. This observation, best explained by an open system steady-state fractionation model, contrasts with previous studies of the South Atlantic and South Pacific Oceans, where only Cd-deficient waters have a relatively constant Cd isotope signature. For the subsurface to about 500 m depth, the variability of seawater Cd isotope compositions can be modeled by mixing of remineralized Cd with subsurface water from the base of the mixed layer. In the intermediate and deep eastern tropical South Pacific (>500 m), seawater [Cd] and δ114/110Cd appear to follow the distribution and mixing of major water masses. We identified modified AAIW of the ETSP to be more enriched in [Cd] than AAIW from the source region, whilst both water masses have similar δ114/110Cd. A mass balance estimate thus constrains a δ114/110Cd of between 0.38‰ and 0.56‰ for the accumulated remineralized Cd in the ETSP. Nearly all samples show a tight coupling of Cd and PO4 concentrations, whereby surface and deeper waters define two distinct linear trends. However, seawater at a coastal station located within a pronounced plume of H2S, is depleted in [Cd] and features significantly higher δ114/110Cd. This signature is attributed to the formation of authigenic CdS with preferential incorporation of lighter Cd isotopes. The process follows a Rayleigh fractionation model with a fractionation factor of α114/110Cdseawater-CdS = 1.00029. Further deviations from the deep Cd–PO4 trend were observed for samples with O2 < 10 μmol/kg and are best explained by in situ CdS precipitation within the decaying organic matter even though dissolved H2S was not detectable in ambient seawater

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

The GEOTRACES Intermediate Data Product 2014

The GEOTRACES Intermediate Data Product 2014 (IDP2014) is the first publicly available data product of the international GEOTRACES programme, and contains data measured and quality controlled before the end of 2013. It consists of two parts: (1) a compilation of digital data for more than 200 trace elements and isotopes (TEIs) as well as classical hydrographic parameters, and (2) the eGEOTRACES Electronic Atlas providing a strongly inter-linked on-line atlas including more than 300 section plots and 90 animated 3D scenes. The IDP2014 covers the Atlantic, Arctic, and Indian oceans, exhibiting highest data density in the Atlantic. The TEI data in the IDP2014 are quality controlled by careful assessment of intercalibration results and multi-laboratory data comparisons at cross-over stations. The digital data are provided in several formats, including ASCII spreadsheet, Excel spreadsheet, netCDF, and Ocean Data View collection. In addition to the actual data values the IDP2014 also contains data quality flags and 1-? data error values where available. Quality flags and error values are useful for data filtering. Metadata about data originators, analytical methods and original publications related to the data are linked to the data in an easily accessible way. The eGEOTRACES Electronic Atlas is the visual representation of the IDP2014 data providing section plots and a new kind of animated 3D scenes. The basin-wide 3D scenes allow for viewing of data from many cruises at the same time, thereby providing quick overviews of large-scale tracer distributions. In addition, the 3D scenes provide geographical and bathymetric context that is crucial for the interpretation and assessment of observed tracer plumes, as well as for making inferences about controlling processes

A convergence analysis of SOR iterative methods for linear systems with weak H-matrices

It is well known that SOR iterative methods are convergent for linear systems, whose coefficient matrices are strictly or irreducibly diagonally dominant matrices and strong H-matrices (whose comparison matrices are nonsingular M-matrices). However, the same can not be true in case of those iterative methods for linear systems with weak H-matrices (whose comparison matrices are singular M-matrices). This paper proposes some necessary and sufficient conditions such that SOR iterative methods are convergent for linear systems with weak H-matrices. Furthermore, some numerical examples are given to demonstrate the convergence results obtained in this paper

Optimal Dimensionality of Metric Space for Classification

In many real-world applications, Euclidean distance in the original space is not good due to the curse of dimensionality. In this paper, we propose a new method, called Discriminant Neighborhood Embedding (DNE), to learn an appropriate metric space for classification given finite training samples. We define a discriminant adjacent matrix in favor of classification task, i.e., neighboring samples in the same class are squeezed but those in different classes are separated as far as possible. The optimal dimensionality of the metric space can be estimated by spectral analysis in the proposed method, which is of great significance for high-dimensional patterns. Experiments with various datasets demonstrate the effectiveness of our method. 1

Development and validation of a predictive model for endocervical curettage in patients referred for colposcopy:A multicenter retrospective diagnostic study in China

This study aimed to develop a nomogram that can predict occult high-grade squamous intraepithelial lesions or worse (HSIL+) and determine the need for endocervical curettage (ECC) in patients referred for colposcopy. This retrospective multicenter study included 4,149 patients who were referred to any one of six tertiary hospitals in China for colposcopy between January 2020 and November 2021 because of abnormal screening results. ECC data were extracted from the medical records. Univariate and multivariate logistic regression analyses were performed to identify factors that could predict HSIL+ on ECC. Patients were randomly assigned to a training set or to an internal validation set for performance and comparability testing. The model was externally validated and tested in patients from two additional hospitals. The nomogram was assessed in terms of discrimination and calibration and subjected to decision curve analysis. HSIL+ was found on ECC in 38.8% (n=388) of cases. Our predictive nomogram included age group, cytology, human papillomavirus (HPV) status, visibility of the cervix and colposcopic impression. The nomogram had good overall discrimination, which was internally validated [area under the receiver-operator characteristic (AUC), 0.839; 95% confidence interval (95% CI), 0.773-0.904]. In terms of external validation, the AUC was 0.843 (95% CI, 0.773-0.912) for the consecutive sample and 0.843 (95% CI, 0.783-0.902) for the comparative sample. Calibration analysis suggested good consistency between predicted and observed probabilities. Decision curve analysis suggested this nomogram would be clinically useful with almost the entire range of threshold probabilities. This internally and externally validated nomogram can be easily applied and incorporates multiple clinically relevant variables that can be used to identify patients with occult HSIL+ who need ECC

Dissolved Cd isotope ratios from seawater samples during cruises M77/3 and M77/4

The depth profiles of Cd isotopes display high δ114/110Cd at the surface and decreasing δ114/110Cd with increasing water depth, consistent with preferential utilization of lighter Cd isotopes during biological uptake in the euphotic zone and subsequent remineralization of the sinking biomass. In the surface and subsurface ocean, seawater displays similar δ114/110Cd signatures of 0.47 ±0.23‰ to 0.82 ±0.05‰ across the entire eastern tropical South Pacific despite highly variable Cd concentrations between 0.01 and 0.84 nmol/kg. This observation, best explained by an open system steady-state fractionation model, contrasts with previous studies of the South Atlantic and South Pacific Oceans, where only Cd-deficient waters have a relatively constant Cd isotope signature. For the subsurface to about 500 m depth, the variability of seawater Cd isotope compositions can be modeled by mixing of remineralized Cd with subsurface water from the base of the mixed layer. In the intermediate and deep eastern tropical South Pacific (>500 m), seawater [Cd] and δ114/110Cd appear to follow the distribution and mixing of major water masses. We identified modified AAIW of the ETSP to be more enriched in [Cd] than AAIW from the source region, whilst both water masses have similar δ114/110Cd. A mass balance estimate thus constrains a δ114/110Cd of between 0.38‰ and 0.56‰ for the accumulated remineralized Cd in the ETSP. Nearly all samples show a tight coupling of Cd and PO4 concentrations, whereby surface and deeper waters define two distinct linear trends. However, seawater at a coastal station located within a pronounced plume of H2S, is depleted in [Cd] and features significantly higher δ114/110Cd. This signature is attributed to the formation of authigenic CdS with preferential incorporation of lighter Cd isotopes. The process follows a Rayleigh fractionation model with a fractionation factor of α114/110Cd(seawater-CdS)=1.00029. Further deviations from the deep Cd-PO4 trend were observed for samples with O2<10μmol/kg and are best explained by in situ CdS precipitation within the decaying organic matter even though dissolved H2S was not detectable in ambient seawater

Efficient Feature Extraction for Image Classification

In many image classification applications, input feature space is often high-dimensional and dimensionality reduction is necessary to alleviate the curse of dimensionality or to reduce the cost of computation. In this paper, we extract discriminant features for image classification by learning a low-dimensional embedding from finite labeled samples. In the new feature space, intra-class compactness and extraclass separability are achieved simultaneously. Target dimensionality of the embedding is selected by spectral analysis. Our method is designed suitable for data with both uni- and multi-modal class distributions. We also develop its two-dimensional variant which makes use of the matrix representation of images. Experimental results on three real image datasets demonstrate the efficacy of our method compared to the state of the art. 1