Soil phosphate detection and archaeology : in-stride phosphate detection and the elimination of arsenate interference to the malachite green method.

Archaeologists use soil analysis to detect chemicals, like phosphate, to indicate areas of anthropogenic activity. Phosphate detection is a multi-step process, which makes standard techniques time consuming. Kinetic studies decreased the analysis time for the malachite green (MG) method of phosphate detection. The 3-minute method allows extraction and analysis to be complete in 15 minutes. Continued studies resulted in two-color spectral monitoring, which provided values instantaneously. Arsenate (As(V)) interfere with the MG method and results in overestimation of phosphate. As(V) must be reduced to non-interfering arsenite. Two As(V) reducing agents--L-Cysteine and thiosulfate--were investigated. The thiosulfate method was suitable for field implementation with the 3-minute malachite green method. L-Cysteine is compatible with both MG time scales, but pre-reduction could not be improved beyond 20 minutes. The 3-minute malachite green method was utilized at an archaeological site in Virginia. The survey led to delineation of the site boundaries

Can we identify non-stationary dynamics of trial-to-trial variability?"

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings

Transcriptional analysis of temporal gene expression in germinating Clostridium difficile 630 endospores.

Clostridium difficile is the leading cause of hospital acquired diarrhoea in industrialised countries. Under conditions that are not favourable for growth, the pathogen produces metabolically dormant endospores via asymmetric cell division. These are extremely resistant to both chemical and physical stress and provide the mechanism by which C. difficile can evade the potentially fatal consequences of exposure to heat, oxygen, alcohol, and certain disinfectants. Spores are the primary infective agent and must germinate to allow for vegetative cell growth and toxin production. While spore germination in Bacillus is well understood, little is known about C. difficile germination and outgrowth. Here we use genome-wide transcriptional analysis to elucidate the temporal gene expression patterns in C. difficile 630 endospore germination. We have optimized methods for large scale production and purification of spores. The germination characteristics of purified spores have been characterized and RNA extraction protocols have been optimized. Gene expression was highly dynamic during germination and outgrowth, and was found to involve a large number of genes. Using this genome-wide, microarray approach we have identified 511 genes that are significantly up- or down-regulated during C. difficile germination (p≤0.01). A number of functional groups of genes appeared to be co-regulated. These included transport, protein synthesis and secretion, motility and chemotaxis as well as cell wall biogenesis. These data give insight into how C. difficile re-establishes its metabolism, re-builds the basic structures of the vegetative cell and resumes growth

Reinforcement learning or active inference?

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain

An International Laboratory for Systems and Computational Neuroscience

The neural basis of decision-making has been elusive and involves the coordinated activity of multiple brain structures. This NeuroView, by the International Brain Laboratory (IBL), discusses their efforts to develop a standardized mouse decision-making behavior, to make coordinated measurements of neural activity across the mouse brain, and to use theory and analyses to uncover the neural computations that support decision-making. The neural basis of decision-making has been elusive and involves the coordinated activity of multiple brain structures. This NeuroView, by the International Brain Laboratory (IBL), discusses their efforts to develop a standardized mouse decision-making behavior, to make coordinated measurements of neural activity across the mouse brain, and to use theory and analyses to uncover the neural computations that support decision-making

Spatial Intuition in Elementary Arithmetic: A Neurocomputational Account

Elementary arithmetic (e.g., addition, subtraction) in humans has been shown to exhibit spatial properties. Its exact nature has remained elusive, however. To address this issue, we combine two earlier models for parietal cortex: A model we recently proposed on number-space interactions and a modeling framework of parietal cortex that implements radial basis functions for performing spatial transformations. Together, they provide us with a framework in which elementary arithmetic is based on evolutionarily more basic spatial transformations, thus providing the first implemented instance of Dehaene and Cohen's recycling hypothesis

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons

Paradoxical Evidence Integration in Rapid Decision Processes

Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. Here, we show that one-stage models cannot explain psychophysical experiments on feature fusion, where two visual stimuli are presented in rapid succession. Paradoxically, the second stimulus biases decisions more strongly than the first one, contrary to predictions of one-stage models and intuition. We present a two-stage model where sensory information is integrated and buffered before it is fed into a drift diffusion process. The model is tested in a series of psychophysical experiments and explains both accuracy and reaction time distributions

The weight of representing the body: addressing the potentially indefinite number of body representations in healthy individuals

There is little consensus about the characteristics and number of body representations in the brain. In the present paper, we examine the main problems that are encountered when trying to dissociate multiple body representations in healthy individuals with the use of bodily illusions. Traditionally, task-dependent bodily illusion effects have been taken as evidence for dissociable underlying body representations. Although this reasoning holds well when the dissociation is made between different types of tasks that are closely linked to different body representations, it becomes problematic when found within the same response task (i.e., within the same type of representation). Hence, this experimental approach to investigating body representations runs the risk of identifying as many different body representations as there are significantly different experimental outputs. Here, we discuss and illustrate a different approach to this pluralism by shifting the focus towards investigating task-dependency of illusion outputs in combination with the type of multisensory input. Finally, we present two examples of behavioural bodily illusion experiments and apply Bayesian model selection to illustrate how this different approach of dissociating and classifying multiple body representations can be applied