Pedotransfer in soil physics: trends and outlook — A review —

Parameters governing the retention and movement of water and chemicals in soils are notorious for the difficulties and high labor costs involved in measuring them. Often, there is a need to resort to estimating these parameters from other, more readily available data, using pedotransfer relationships. This work is a mini-review that focuses on trends in pedotransfer development across the World, and considers trends regarding data that are in demand, data we have, and methods to build pedotransfer relationships. Recent hot topics are addressed, including estimating the spatial variability of water contents and soil hydraulic properties, which is needed in sensitivity analysis, evaluation of the model performance, multimodel simulations, data assimilation from soil sensor networks and upscaling using Monte Carlo simulations. Ensembles of pedotransfer functions and temporal stability derived from “big data” as a source of soil parameter variability are also described. Estimating parameter correlation is advocated as the pathway to the improvement of synthetic datasets. Upscaling of pedotransfer relationships is demonstrated for saturated hydraulic conductivity. Pedotransfer at coarse scales requires a different type of input variables as compared with fine scales. Accuracy, reliability, and utility have to be estimated independently. Persistent knowledge gaps in pedotransfer development are outlined, which are related to regional soil degradation, seasonal changes in pedotransfer inputs and outputs, spatial correlations in soil hydraulic properties, and overland flow parameter estimation. Pedotransfer research is an integral part of addressing grand challenges of the twenty-first century, including carbon stock assessments and forecasts, climate change and related hydrological weather extreme event predictions, and deciphering and managing ecosystem services. Overall, pedotransfer functions currently serve as an essential instrument in the science-based toolbox for diagnostics, monitoring, predictions, and management of the changing Earth and soil as a life-supporting Earth system

Inverse Current Source Density Method in Two Dimensions: Inferring Neural Activation from Multielectrode Recordings

The recent development of large multielectrode recording arrays has made it affordable for an increasing number of laboratories to record from multiple brain regions simultaneously. The development of analytical tools for array data, however, lags behind these technological advances in hardware. In this paper, we present a method based on forward modeling for estimating current source density from electrophysiological signals recorded on a two-dimensional grid using multi-electrode rectangular arrays. This new method, which we call two-dimensional inverse Current Source Density (iCSD 2D), is based upon and extends our previous one- and three-dimensional techniques. We test several variants of our method, both on surrogate data generated from a collection of Gaussian sources, and on model data from a population of layer 5 neocortical pyramidal neurons. We also apply the method to experimental data from the rat subiculum. The main advantages of the proposed method are the explicit specification of its assumptions, the possibility to include system-specific information as it becomes available, the ability to estimate CSD at the grid boundaries, and lower reconstruction errors when compared to the traditional approach. These features make iCSD 2D a substantial improvement over the approaches used so far and a powerful new tool for the analysis of multielectrode array data. We also provide a free GUI-based MATLAB toolbox to analyze and visualize our test data as well as user datasets

The Natural Statistics of Audiovisual Speech

Humans, like other animals, are exposed to a continuous stream of signals, which are dynamic, multimodal, extended, and time varying in nature. This complex input space must be transduced and sampled by our sensory systems and transmitted to the brain where it can guide the selection of appropriate actions. To simplify this process, it's been suggested that the brain exploits statistical regularities in the stimulus space. Tests of this idea have largely been confined to unimodal signals and natural scenes. One important class of multisensory signals for which a quantitative input space characterization is unavailable is human speech. We do not understand what signals our brain has to actively piece together from an audiovisual speech stream to arrive at a percept versus what is already embedded in the signal structure of the stream itself. In essence, we do not have a clear understanding of the natural statistics of audiovisual speech. In the present study, we identified the following major statistical features of audiovisual speech. First, we observed robust correlations and close temporal correspondence between the area of the mouth opening and the acoustic envelope. Second, we found the strongest correlation between the area of the mouth opening and vocal tract resonances. Third, we observed that both area of the mouth opening and the voice envelope are temporally modulated in the 2–7 Hz frequency range. Finally, we show that the timing of mouth movements relative to the onset of the voice is consistently between 100 and 300 ms. We interpret these data in the context of recent neural theories of speech which suggest that speech communication is a reciprocally coupled, multisensory event, whereby the outputs of the signaler are matched to the neural processes of the receiver