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

Fast and label-free 3D virtual H&E histology via active modulation-assisted dynamic full-field OCT

Pathological features are the gold standard for tumor diagnosis, guiding treatment and prognosis. However, standard histopathological process is labor-intensive and time-consuming, while frozen sections have lower accuracy. Dynamic full-field optical coherence tomography (D-FFOCT) offers rapid histologic information by measuring the subcellular dynamics of fresh, unprocessed tissues. However, D-FFOCT images suffer from abrupt shifts in hue and brightness, which is confusing for pathologists and diminish their interpretability and reliability. Here, we present active phase modulation-assisted D-FFOCT (APMD-FFOCT) to improve the imaging stability and enhance the contrast of static tissues. This enables us to further employ an unsupervised deep learning to convert APMD-FFOCT images into virtual hematoxylin and eosin (H&E) stained images for the first time. Three-dimensional (3D) virtual H&E-stained images have been obtained at a scanning rate of 1 frame per second, as demonstrated in cancer diagnosis for human central nervous system and breast. The results prove that this new method will play a unique and important role in intraoperative histology

Optical biomarker of metabolism for breast tumor diagnosis: Insights from subcellular dynamics

Label-free metabolic dynamics contrast is highly appealing but difficult to achieve in biomedical imaging. Interference offers a highly sensitive mechanism for capturing the metabolic dynamics of the subcellular scatterers. However, traditional interference detection methods fail to isolate pure metabolic dynamics, as the dynamic signals are coupled with scatterer reflectivity and other uncontrollable imaging factors. Here, we demonstrate active phase modulation-assisted dynamic full-field optical coherence tomography (APMD-FFOCT) that decouples and quantifies the metabolic dynamics by adding a reference movement for all interferential scatterers. This novel technique enables imaging and dynamic analysis of subcellular structures along with their changes during the apoptotic process in tumor tissues. Furthermore, the nucleus-to-cytoplasm dynamic intensity ratio could serve as an optical biomarker for breast tumor grading, enhancing intraoperative diagnosis

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

Bearing Fault Diagnosis Based on Image Information Fusion and Vision Transformer Transfer Learning Model

In order to improve the accuracy of bearing fault diagnosis under a small sample, variable load, and noise conditions, a new fault diagnosis method based on an image information fusion and Vision Transformer (ViT) transfer learning model is proposed in this paper. Firstly, the method applies continuous wavelet transform (CWT), Gramian angular summation field (GASF), and Gramian angular difference field (GADF) to the time series data, and generates three grayscale images. Then, the generated three grayscale images are merged into an information fusion image (IFI) using image processing techniques. Finally, the obtained IFIs are fed into the advanced ViT model and trained based on transfer learning. In order to verify the effectiveness and superiority of the proposed method, the rolling bearing dataset from Case Western Reserve University (CWRU) is used to carry out experimental studies under different working conditions. Experimental results show that the method proposed in this paper is superior to other traditional methods in terms of accuracy, and the effect of ViT model based on transfer learning (TLViT) training is better than that of the Resnet50 model based on transfer learning training (TLResnet50) under variable loads and small sample conditions. In addition, the experimental results also prove that the IFI with multiple image information has better anti-noise ability than the single information image. Therefore, the method proposed in this paper can improve the accuracy of bearing fault diagnosis under small sample, variable load and noise conditions, and provide a new method for bearing fault diagnosis

A quantitative study of pathologists' perceptions towards artificial intelligence-assisted diagnostic system.

The successful implementation of artificial intelligence-assisted diagnostic system (AIADS) in pathology relies not only on the maturity of AI technology but also on pathologists' cognition and acceptance of AI. However, research on pathologists' perceptions towards AIADS is limited. This study aims to explore pathologists' knowledge, attitudes, and practice toward AIADS and identify key factors influencing their willingness to use it, providing insights for the effective integration of AI technology in pathology. An online, nationwide, cross-sectional survey is to investigate pathologists' knowledge, attitudes and behavioral intention/practice regarding AIADS with a 5-point Likert scale. Descriptive analysis is used to present the results, while logistic regression examines factors influencing AIADS adoption. The mediating effect of attitude in the association between knowledge and behavioral intention is also explored. A total of 224 pathologists were surveyed, with 85 (37.9%) having used AIADS and 139 (62.1%) not using it. The mean scores for knowledge, attitude, and behavioral intention were 3.42 ± 0.97, 3.48 ± 0.44, and 3.47 ± 0.44, respectively. Pathologists who had used AIADS scored higher in knowledge, attitude, and behavioral intention, with clearer attitudes toward AIADS. Over 80% of pathologists supported the use of AIADS in clinical diagnostics, citing improved diagnostic speed and reduced workload as key reasons. The main concerns about AIADS were its diagnostic accuracy. Logistic regression analysis indicated that a greater likelihood of willingness to use AIADS was associated with not having used it before (OR=2.462, 95%CI 1.087-5.573), as well as with higher knowledge scores (OR=1.140, 95%CI 1.076-1.208) and more positive attitude scores (OR=1.119, 95%CI 1.053-1.189). Mediation analysis indicated an indirect path from knowledge to behavioral intention through attitude among individuals who have used AIADS, with the mediation effect accounting for 59.4%. In conclusion, most pathologists support the use of AIADS in clinical practice, but improvements in diagnostic performance are necessary. Enhancing pathologists' knowledge, attitudes, and user experience is crucial for the broader adoption of AIADS