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Abstract The serotonergic system plays a key modulatory role in the brain. This system is critical in many drug developments for brain disorders via interactions with the 14 subtypes of 5-HT receptors or through reuptake blockade. Positron emission tomography (PET) is an efficient tool for in vivo studies of physiological and pathological processes. Because of its pertinent radiochemical properties, fluorine-18 is one of the most used radioisotopes in PET imaging. This chapter will propose an overview of the 18F-radioligands targeting serotonin receptors, which have been developed over the last few years. Both radiosyntheses and pharmacological properties of these radiotracers will be described, with a specific emphasis on their potential medical applications.

International audience; In this paper, we study the effect of smartphone camera exposure on the performance of optical camera communications (OCC) link. The exposure parameters of image sensor sensitivity (ISO), aperture and shutter speed are included. A static OCC link with a 8 × 8 red, green and blue (RGB) LED array employed as the transmitter and a smartphone camera as the receiver is demonstrated to verify the study. Signal-to-noise ratio (SNR) analysis at different ISO values, the effect of aperture and shutter speed on communication link quality is performed. While SNRs of 20.6 dB and 16.9 dB are measured at 1 m and 2 m transmission distance, respectively for a ISO value of 100, they are decreased to 17.4 dB and 13.32 dB for a ISO of 800. The bit error rate (BER) of a 1 m long OCC link with a camera's shutter speed of 1/6000 s is 1.3 × 10 −3 (i.e., below the forward error correction BER limit of 3.8 × 10 −3) and is dropped to 0.0125 at a shutter speed of 1/20 s. This study provides insight of the basic smartphone settings and the exposure adjustment for further complex OCC links.

Wildfire prediction from Earth Observation (EO) data has gained much attention in the past years, through the development of connected sensors and weather satellites. Nowadays, it is possible to extract knowledge from collected EO data and to learn from this knowledge without human intervention to trigger wildfire alerts. However, exploiting knowledge extracted from multiple EO data sources at run-time and predicting wildfire raise multiple challenges. One major challenge is to provide dynamic construction of service composition plans, according to the data obtained from sensors. In this paper, we present a knowledge-driven Machine Learning approach that relies on historical data related to wildfire observations to guide the collection of EO data and to automatically and dynamically compose services for triggering wildfire alerts.

Multiple theories of working memory are described in the chapters of this book and often these theories are viewed as being mutually incompatible, yet each is associated with a supporting body of empirical evidence. This chapter argues that many of these differences reflect different research questions, different levels of explanation, and differences in how participants perform their assigned tasks in different laboratories, rather than fundamental theoretical adversity. It describes a version of a multiple component working memory in which a range of specialized cognitive functions (or mental tools) act in concert, giving the impression, at a different level of explanation, of a unified cognitive system. The chapter argues that more rapid and more substantial scientific progress on the understanding of the concept of working memory would be achieved through identifying the levels of explanation explored within each theoretical framework, and attempting to integrate theoretical frameworks rather than perpetuating debate with no clear resolution in sight.

Land use intensification is widely considered to be an essential strategy for achieving global goals to eliminate poverty and to avoid damaging losses of ecosystem services. This chapter investigates whether current land use intensification activities are achieving these twin goals. To do so, it reviews a body of academic literature that reports on case studies in which both social and ecological outcomes of intensification are reported. There are two main findings. First, there are relatively few cases in which land use intensification is clearly succeeding in these twinned objectives. There are many more cases in which, for example, short-term income or productivity gains from land use intensification are resulting in long-term diminution of biodiversity and ecosystem services. Studies with longer-term perspectives are already seeing how such trade-offs are leading to negative feedbacks for human wellbeing, especially for marginalised social groups. Secondly, we learn most from those studies that a) go beyond measuring production and income to measure multiple dimensions of wellbeing and ecosystem services, b) monitor dynamics of outcomes across longer time periods and across landscapes and c) disaggregate outcome measures to identify outcomes for different social groups.

The interplay among the effects of dispersion, nonlinearity and gain/loss in optical fibre systems can be efficiently used to shape the pulses and manipulate and control the light dynamics and, hence, lead to different pulse-shaping regimes [1,2]. However, achieving a precise waveform with various prescribed characteristics is a complex issue that requires careful choice of the initial pulse conditions and system parameters. The general problem of optimisation towards a target operational regime in a complex multi-parameter space can be intelligently addressed by implementing machine-learning strategies. In this paper, we discuss a novel approach to the characterisation and optimisation of nonlinear shaping in fibre systems, which combines numerical simulations of the governing equations to identify the relevant parameters and the machine-learning method of neural networks (NNs) to make predictions across a larger range of the data domain. We illustrate this general method through application to two configurations.

International audience; Chemical synapses enable neurons to communicate rapidly, process and filter signals and to store information. However, studying their functional properties is difficult because synaptic connections typically consist of multiple synaptic contacts that release vesicles stochastically and exhibit time-dependent behavior. Moreover, most central synapses are small and inaccessible to direct measurements. Estimation of synaptic properties from responses recorded at the soma is complicated by the presence of nonuniform release probability and nonuniform quantal properties. The presence of multivesicular release and postsynaptic receptor saturation at some synapses can also complicate the interpretation of quantal parameters. Multiple-probability fluctuation analysis (MPFA; also known as variance-mean analysis) is a method that has been developed for estimating synaptic parameters from the variance and mean amplitude of synaptic responses recorded at different release probabilities. This statistical approach, which incorporates nonuniform synaptic properties, has become widely used for studying synaptic transmission. In this chapter, we describe the statistical models used to extract quantal parameters and discuss their interpretation when applying MPFA.

International audience; Synthetic biology is an emergent field of research whose aim is to make biology an engineering discipline, thus permitting to design, control, and standardize biological processes. Synthetic biology is therefore expected to boost the development of biotechnological processes such as protein production and enzyme engineering, which can be significantly relevant for lipases and esterases.

Part 2: Infrastructure Protection; International audience; State estimation is vital to the stability of control systems, especially in power systems, which rely heavily on measurement devices installed throughout wide-area power networks. Several researchers have analyzed the problems arising from bad data injection and topology errors, and have proposed protection and mitigation schemes. This chapter employs hierarchical state estimation based on the common weighted-least-squares formulation to study the propagation of faults in intermediate and top-level state estimates as a result of measurement reordering attacks on a single region in the bottom level. Although power grids are equipped with modern defense mechanisms such as those recommended by the ISO/IEC 62351 standard, reordering attacks are still possible. This chapter concentrates on how an inexpensive data swapping attack in one region in the lower level can influence the accuracy of other regions in the same level and upper levels, and force the system towards undesirable states. The results are validated using the IEEE 118-bus test case.

Aluminum based hybrid composites were produced from recycled AA7075 chips with the addition of TiC (d ≤ 3–5 micron), MoS2 and Al2O3 fiber. In the two groups of composites produced, the content of MoS2 and Al2O3 were fixed as 2 wt % and 3 wt % respectively, whereas TiC content was at two levels (5–10%). The combined method of powder metallurgy route, sintering followed by forging, was used to manufacture these composites. These composites are targeted for aeronautical and automotive industries for components subjected to static as well as cyclic and dynamic loading. In addition to mechanical properties, machinability of these composites is of importance hence, MoS2 was included in the formulation. Micro hardness, 3 point bending, low velocity impact and nanoindentation (creep and wear) tests were performed on samples manufactured by just sintering and sintering followed by forging. The results showed that, in generals, the samples that were forged after sintering yielded better properties. The microstructure analyses (matrix/interface) have been carried out by Scanning Electron Microscope (SEM).