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Cellular response to stimulation governs tissue scale processes ranging from growth and development to maintaining tissue health and initiating disease. To determine how cells coordinate their response to such stimuli, it is necessary to simultaneously track and measure the spatiotemporal distribution of their behaviors throughout the tissue. Here, we report on a novel SpatioTemporal Response AnalysisIN Situ(STRAINS) tool that uses fluorescent micrographs, cell tracking, and machine learning to measure such behavioral distributions. STRAINS is broadly applicable to any tissue where fluorescence can be used to indicate changes in cell behavior. For illustration, we use STRAINS to simultaneously analyze the mechanotransduction response of 5000 chondrocytes—over 20 million data points—in cartilage during the 50 ms to 4 hours after the tissue was subjected to local mechanical injury, known to initiate osteoarthritis. We find that chondrocytes exhibit a range of mechanobiological responses indicating activation of distinct biochemical pathways with clear spatial patterns related to the induced local strains during impact. These results illustrate the power of this approach. 

When utilizing double-beam laser interferometry to assess the piezoelectric coefficient of a film on a substrate, probing both top and bottom sample surfaces is expected to correct the erroneous bending contribution by canceling the additional path length from the sample height change. However, when the bending deformation becomes extensive and uncontrolled, as in the case of membranes or fully released piezoelectric films, the double-beam setup can no longer account for the artifacts, thus resulting in inflated film displacement data and implausibly large piezoelectric coefficient values. This work serves to identify these challenges by demonstrating d33,f measurements of fully released PZT films using a commercial double-beam laser interferometer. For a 1 μm thick randomly oriented PZT film on a 10 μm thick polyimide substrate, a large apparent d33,f of 9500 pm/V was measured. The source of error was presumably a distorted interference pattern due to the erroneous phase shift of the measurement laser beam caused by extensive deformation of the released sample structure. This effect has unfortunately been mistaken as enhanced piezoelectric responses by some reports in the literature. Finite element models demonstrate that bending, laser beam alignment, and the offset between the support structure and the electrode under test have a strong influence on the apparent film d33,f. 

Tunable mechanics and fracture resistance are hallmarks of biological tissues whose properties arise from extracellular matrices comprised of double networks. To elucidate the origin of these desired properties, we study the shear modulus and fracture properties of a rigidly percolating double network model comprised of a primary network of stiff fibers and a secondary network of flexible fibers. We find that when the primary network density is just above its rigidity percolation threshold, the secondary network density can be tuned to facilitate stress relaxation via non-affine deformations and provide mechanical reinforcement. In contrast, when the primary network is far above its rigidity threshold, the double network is always stiff and brittle. These results highlight an important mechanism behind the tunability and resilience of biopolymer double networks: the secondary network can dramatically alter mechanical properties from compliant and ductile to stiff and brittle only when the primary network is marginally rigid. 

We present a new framework to understand how changes to the microstructure of cartilage lead to a mechanical phase transition. 

Remote sensing observations from satellites and global biogeochemical models have combined to revolutionize the study of ocean biogeochemical cycling, but comparing the two data streams to each other and across time remains challenging due to the strong spatial-temporal structuring of the ocean. Here, we show that the Wasserstein distance provides a powerful metric for harnessing these structured datasets for better marine ecosystem and climate predictions. The Wasserstein distance complements commonly used point-wise difference methods such as the root-mean-squared error, by quantifying differences in terms of spatial displacement in addition to magnitude. As a test case, we consider chlorophyll (a key indicator of phytoplankton biomass) in the northeast Pacific Ocean, obtained from model simulations, in situ measurements, and satellite observations. We focus on two main applications: (i) comparing model predictions with satellite observations, and (ii) temporal evolution of chlorophyll both seasonally and over longer time frames. The Wasserstein distance successfully isolates temporal and depth variability and quantifies shifts in biogeochemical province boundaries. It also exposes relevant temporal trends in satellite chlorophyll consistent with climate change predictions. Our study shows that optimal transport vectors underlying the Wasserstein distance provide a novel visualization tool for testing models and better understanding temporal dynamics in the ocean. 

Abstract. A global in situ data set for validation of ocean colour productsfrom the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented.This version of the compilation, starting in 1997, now extends to 2021,which is important for the validation of the most recent satellite opticalsensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprisesin situ observations of the following variables: spectral remote-sensingreflectance, concentration of chlorophyll-a, spectral inherent opticalproperties, spectral diffuse attenuation coefficient, and total suspendedmatter. Data were obtained from multi-project archives acquired via openinternet services or from individual projects acquired directly from dataproviders. Methodologies were implemented for homogenization, qualitycontrol, and merging of all data. Minimal changes were made on the originaldata, other than conversion to a standard format, elimination of some points,after quality control and averaging of observations that were close in timeand space. The result is a merged table available in text format. Overall,the size of the data set grew with 148 432 rows, with each row representing aunique station in space and time (cf. 136 250 rows in previous version;Valente et al., 2019). Observations of remote-sensing reflectance increasedto 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There wasalso a near tenfold increase in chlorophyll data since 2016. Metadata ofeach in situ measurement (original source, cruise or experiment, principalinvestigator) are included in the final table. By making the metadataavailable, provenance is better documented and it is also possible toanalyse each set of data separately. The compiled data are available athttps://doi.org/10.1594/PANGAEA.941318 (Valente et al., 2022). 

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.