Toxoplasma gondii proliferates and organizes within a parasitophorous vacuole in rosettes around a residual body and is surrounded by a membranous nanotubular network whose function remains unclear. Here, we characterized structure and function of the residual body in intracellular tachyzoites of the RH strain. Our data showed the residual body as a body limited by a membrane formed during proliferation of tachyzoites probably through the secretion of components and a pinching event of the membrane at the posterior end. It contributes in the intravacuolar parasite organization by the membrane connection between the tachyzoites posterior end and the residual body membrane to give place to the rosette conformation. Radial distribution of parasites in rosettes favors an efficient exteriorization. Absence of the network and presence of atypical residual bodies in a ΔGRA2-HXGPRT knock-out mutant affected the intravacuolar organization of tachyzoites and their exteriorization.

The analysis of multi-unit extracellular recordings of brain activity has led to the development of numerous tools, ranging from signal processing algorithms to electronic devices and applications. Currently, the evaluation and optimisation of these tools are hampered by the lack of ground-truth databases of neural signals. These databases must be parameterisable, easy to generate and bioinspired, i.e. containing features encountered in real electrophysiological recording sessions. Towards that end, this article introduces an original computational approach to create fully annotated and parameterised benchmark datasets, generated from the summation of three components: neural signals from compartmental models and recorded extracellular spikes, non-stationary slow oscillations, and a variety of different types of artefacts. We present three application examples. Electrical recording of extracellular action potentials is the "gold standard" technique widely used in electrophysiology 1 , where the signals are exploited to correlate neural activity with a behavioural output and/or the electrophysiological consequences of brain lesions or drug infusion, etc. The emergence of novel methods for neural analysis together with high-throughput data acquisition technologies 2 provide new possibilities for the exploitation of brain activity at the single unit level, for example, giving instantaneous feedback for closed-loop interactions with brain circuits when abnormal neural signals are detected 3 . This approach has proven effective for several pathological conditions such as Epilepsy, Parkinson's disease, or Essential Tremor 4-7 . From a more fundamental perspective, novel algorithms have been recently proposed to process these large amounts of neural data, such as semi-automatic and automatic clustering techniques, to distinguish different neural sources in multi-unit extracellular recordings [8][9][10][11][12] . In order to validate the performance and accuracy of these different algorithms or devices, reliable datasets, where the majority of the signal content is known, are essential. Ideally, this ground-truth reference should be a completely annotated and parameterised dataset, in which three levels of information should be modifiable and known in detail: the recording environment (e.g. density of active population of neurons or distance from neurons to recording sites), the population dynamics (e.g. firing rate, spike timing of each neuron and spike waveforms) and the noise content (e.g. background noise level contribution and number of artefacts).There are several applications (Fig. 1) where using a parameterised dataset can be advantageous, ranging from algorithm design to development and evaluation of electronic devices. Moreover, parameterised datasets are needed to evaluate the efficiency of unsupervised classification algorithms. In recent years, several spike sorting algorithms have been proposed [8][9][10][11][12] , however, it is difficult to assess their sorting efficiency since the datasets used to evalu...

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