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The impact of ocean acidification and increased water temperature on marine ecosystems, in particular those involving calcifying organisms, has been gradually recognised. We examined the individual and combined effects of increased pCO2 (180 ppmV CO2, 380 ppmV CO2 and 750 ppmV CO2 corresponding to past, present and future CO2 conditions, respectively) and temperature (13 °C and 18 °C) during the exponential growth phase of the coccolithophore E. huxleyi using batch culture experiments. We showed that cellular production rate of Particulate Organic Carbon (POC) increased from the present to the future CO2 treatments at 13 °C. A significant effect of pCO2 and of temperature on calcification was found, manifesting itself in a lower cellular production rate of Particulate Inorganic Carbon (PIC) as well as a lower PIC:POC ratio at future CO2 levels and at 18 °C. Coccosphere-sized particles showed a size reduction with both increasing temperature and CO2 concentration. The influence of the different treatments on coccolith morphology was studied by categorizing SEM coccolith micrographs. The number of well-formed coccoliths decreased with increasing pCO2 while temperature did not have a significant impact on coccolith morphology. No interacting effects of pCO2 and temperature were observed on calcite production, coccolith morphology or on coccosphere size. Finally, our results suggest that ocean acidification might have a larger adverse impact on coccolithophorid calcification than surface water warming. [ABSTRACT FROM AUTHOR]

The impact of ocean acidification and increased water temperature on marine ecosystems, in particular those involving calcifying organisms, has been gradually recognised. We examined the individual and combined effects of increased pCO2 (180 ppm V CO2, 380 ppm V CO2 and 750 ppm V CO2 corresponding to past, present and future CO2 conditions, respectively) and temperature (13°C and 18°C) during the calcification phase of the coccolithophore E. huxleyi using batch culture experiments. We showed that the cell abundance-normalized particulate organic carbon concentration (POC) increased from the present to the future CO2 treatments. A significant effect of pCO2 and of temperature on calcification was found, manifesting itself in a lower cell abundance- normalized particulate inorganic carbon (PIC) content as well as a lower PIC:POC ratio at future CO2 levels and at 18°C. Coccosphere-sized particles showed a size reduction trend with both increasing temperature and CO2 concentration. The influence of the different treatments on coccolith morphology was studied by categorizing SEM coccolith micrographs. The number of well-formed coccoliths decreased with increasing pCO2 while temperature did not have a significant impact on coccolith morphology. No interacting effect of pCO2 and temperature was observed on calcite production, coccolith morphology or on coccosphere size. Finally, our results suggest that ocean acidification might have a larger adverse impact on coccolithophorid calcification than surface water warming. [ABSTRACT FROM AUTHOR]

Dimension reduction (DR) computes faithful low-dimensional (LD) representations of high-dimensional (HD) data. Outstanding performances are achieved by recent neighbor embedding (NE) algorithms such as t -SNE, which mitigate the curse of dimensionality. The single-scale or multiscale nature of NE schemes drives the HD neighborhood preservation in the LD space (LDS). While single-scale methods focus on single-sized neighborhoods through the concept of perplexity, multiscale ones preserve neighborhoods in a broader range of sizes and account for the global HD organization to define the LDS. For both single-scale and multiscale methods, however, their time complexity in the number of samples is unaffordable for big data sets. Single-scale methods can be accelerated by relying on the inherent sparsity of the HD similarities they involve. On the other hand, the dense structure of the multiscale HD similarities prevents developing fast multiscale schemes in a similar way. This article addresses this difficulty by designing randomized accelerations of the multiscale methods. To account for all levels of interactions, the HD data are first subsampled at different scales, enabling to identify small and relevant neighbor sets for each data point thanks to vantage-point trees. Afterward, these sets are employed with a Barnes-Hut algorithm to cheaply evaluate the considered cost function and its gradient, enabling large-scale use of multiscale NE schemes. Extensive experiments demonstrate that the proposed accelerations are, statistically significantly, both faster than the original multiscale methods by orders of magnitude, and better preserving the HD neighborhoods than state-of-the-art single-scale schemes, leading to high-quality LD embeddings. Public codes are freely available at https://github.com/cdebodt.

Thermosensation is crucial for humans to probe the environment and detect threats arising from noxious heat or cold. Over the last years, EEG frequency-tagging using long-lasting periodic radiant heat stimulation has been proposed as a means to study the cortical processes underlying tonic heat perception. This approach is based on the notion that periodic modulation of a sustained stimulus can elicit synchronized periodic activity in the neuronal populations responding to the stimulus, known as a steady-state response (SSR). In this paper, we extend this approach using a contact thermode to generate both heat- and cold-evoked SSRs. Furthermore, we characterize the temporal dynamics of the elicited responses, relate these dynamics to perception, and assess the effects of displacing the stimulated skin surface to gain insight on the heat- and cold-sensitive afferents conveying these responses. Two experiments were conducted in healthy volunteers. In both experiments, noxious heat and innocuous cool stimuli were applied during 75 seconds to the forearm using a Peltier-based contact thermode, with intensities varying sinusoidally at 0.2 Hz. Displacement of the thermal stimulation on the skin surface was achieved by independently controlling the Peltier elements of the thermal probe. Continuous intensity ratings to sustained heat and cold stimulation were obtained in the first experiment with 14 subjects, and the EEG was recorded in the second experiment on 15 subjects. Both contact heat and cool stimulation elicited periodic EEG responses and percepts. Compared to heat stimulation, the responses to cool stimulation had a lower magnitude and shorter latency. All responses tended to habituate along time, and this response attenuation was most pronounced for cool compared to warm stimulation, and for stimulation delivered using a fixed surface compared to a variable surface., Competing Interests: The authors have declared that no competing interests exist.

Dimensionality reduction (DR) aims at faithfully and meaningfully representing high-dimensional (HD) data into a low-dimensional (LD) space. Recently developed neighbor embedding DR methods lead to outstanding performances, thanks to their ability to foil the curse of dimensionality. Unfortunately, they cannot be directly employed on incomplete data sets, which become ubiquitous in machine learning. Discarding samples with missing features prevents their LD coordinates computation and deteriorates the complete samples treatment. Common missing data imputation schemes are not appropriate in the nonlinear DR context either. Indeed, even if they model the data distribution in the feature space, they can, at best, enable the application of a DR scheme on the expected data set. In practice, one would, instead, like to obtain the LD embedding with the closest cost function value on average with respect to the complete data case. As the state-of-the-art DR techniques are nonlinear, the latter embedding results from minimizing the expected cost function on the incomplete database, not from considering the expected data set. This paper addresses these limitations by developing a general methodology for nonlinear DR with missing data, being directly applicable with any DR scheme optimizing some criterion. In order to model the feature dependences, an HD extension of Gaussian mixture models is first fitted on the incomplete data set. It is afterward employed under the multiple imputation paradigms to obtain a single relevant LD embedding, thus minimizing the cost function expectation. Extensive experiments demonstrate the superiority of the suggested framework over alternative approaches.