Derivative analysis of spectral absorption by photosynthetic pigments in the western Sargasso Sea

Concurrent measurements of the spectral absorption coefficient and photosynthetic pigmentation of natural particulates were performed to determine the principal pigments responsible for the absorption of spectral irradiance in seawater. The spectral absorption coefficient, Ap(λ), was then analyzed by taking the second and fourth derivatives with respect to wavelength. The wavelength and magnitude of these derivative values provide useful information regarding the identification and quantification of phytoplankton pigments responsible for a given spectral signature. Linear relationships were examined and established between derivative values at selected wavelengths and concentrations of the major tetrapyrrole pigments, specifically chlorophylls a, b, and c. The correlation between derivative values near 526 nm and concentrations of photosynthetic carotenoids was poor and presumably caused by the broad absorption spectra of these pigments. A comparison of the measured particulate absorption coefficient with the absorption coefficient reconstructed for the phytoplankton component revealed that detritus can be a major source of light absorption. The method described here provides a rapid means of obtaining estimates of photosynthetic pigment concentrations in natural samples where absorption can be strongly influenced by detrital matter

Imaging geometry through dynamics: the observable representation

For many stochastic processes there is an underlying coordinate space, $V$, with the process moving from point to point in $V$ or on variables (such as spin configurations) defined with respect to $V$. There is a matrix of transition probabilities (whether between points in $V$ or between variables defined on $V$) and we focus on its ``slow'' eigenvectors, those with eigenvalues closest to that of the stationary eigenvector. These eigenvectors are the ``observables,'' and they can be used to recover geometrical features of $V$

Elevated protein kinase C alpha expression may be predictive of tamoxifen treatment failure

We previously reported that stable transfection of protein kinase C alpha (PKCα) into T47D human breast cancer cells results in tamoxifen (TAM)-resistant tumour growth. Relevance of PKCα expression in clinical specimens was determined by comparing PKCα expression in tumours from patients exhibiting disease recurrence with patients remaining disease-free following TAM treatment. Our results suggest that PKCα expression may predict TAM treatment failure

Enhanced hydrogen storage in Ni/Ce composite oxides

The properties of dried (but not calcined) coprecipitated nickel ceria systems have been investigated in terms of their hydrogen emission characteristics following activation in hydrogen. XRD and BET data obtained on the powders show similarities to calcined ceria but it is likely that the majority of the material produced by the coprecipitation process is largely of an amorphous nature. XPS data indicate very little nickel is present on the outermost surface of the particles. Nevertheless, the thermal analytical techniques (TGA, DSC and TPD-MS) indicate that the hydrogen has access to the catalyst present and the nickel is able to generate hydrogen species capable of interacting with the support. Both unactivated and activated materials show two hydrogen emission features, viz. low temperature and high temperature emissions (LTE and HTE, respectively) over the temperature range 50 and 500 °C. A clear effect of hydrogen interaction with the material is that the activated sample not only emits much more hydrogen than the corresponding unactivated one but also at lower temperatures. H2 dissociation occurs on the reduced catalyst surface and the spillover mechanism transfers this active hydrogen into the ceria, possibly via the formation and migration of OH− species. The amount of hydrogen obtained (0.24 wt%) is 10× higher than those observed for calcined materials and would suggest that the amorphous phase plays a critical role in this process. The affiliated emissions of CO and CO2 with that of the HTE hydrogen (and consumption of water) strongly suggests a proportion of the hydrogen emission at this point arises from the water gas shift type reaction. It has not been possible from the present data to delineate between the various hydrogen storage mechanisms reported for ceria

Perceived barriers towards healthy eating and their association with fruit and vegetable consumption

Acknowledgements The authors would like to thank the anonymous reviewer, staff at the Health Economics Research Unit and the Rowett Institute of Nutrition and Health for helpful comments on the manuscript. Funding This work was supported by the Scottish Government Rural and Environment Science and Analytical Services (RESAS) division.Peer reviewedPostprin

How to validate machine-learned interatomic potentials

Machine learning (ML) approaches enable large-scale atomistic simulations with near-quantum-mechanical accuracy. With the growing availability of these methods there arises a need for careful validation, particularly for physically agnostic models - that is, for potentials which extract the nature of atomic interactions from reference data. Here, we review the basic principles behind ML potentials and their validation for atomic-scale materials modeling. We discuss best practice in defining error metrics based on numerical performance as well as physically guided validation. We give specific recommendations that we hope will be useful for the wider community, including those researchers who intend to use ML potentials for materials "off the shelf"