Jing Xing

Recent experiments are reviewed that indicate that sensory signals from many modalities, as well as efference copy signals from motor structures, converge in the posterior parietal cortex in order to code the spatial locations of goals for movement. These signals are combined using a specific gain mechanism that enables the different coordinate frames of the various input signals to be combined into common, distributed spatial representations. These distributed representations can be used to convert the sensory locations of stimuli into the appropriate motor coordinates required for making directed movements. Within these spatial representations of the posterior parietal cortex are neural activities related to higher cognitive functions, including attention. We review recent studies showing that the encoding of intentions to make movements is also among the cognitive functions of this area.

In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.

Many neurons in the posterior-parietal cortex (PPC) have saccadic responses to visual and auditory targets. The responses are modulated by eye position and head position. These findings suggest that PPC integrates multisensory inputs and may provide information about saccadic targets represented in different coordinate frames. In addition to an eyecentered output representation, PPC may also project to brain areas which contain head-centered and body-centered representations of the space. In this report, possible coordinate transformations in PPC were examined by comparing several sets of models of PPC, each having different representations in the output layer: (i) an eye-centered map only; (ii) a head-centered map only; (iii) an eye-centered map and a head-centered map; and (iv) an eye-centered map, a head-centered map, and a body-centered map. These output maps correctly encoded saccades to visual and auditory targets through training. The units in the hidden layers of the models exhibited the following properties: (1) The units had gain fields (GFs) for eye position, and also for head position if the model had a body-centered output representation; (2) As the result of the GF and the nonlinear activation function of the units, the hidden layers often employed "intermediate" coding, e.g., the hidden units coded targets partially in eye-centered coordinates and, partially, in head-centered coordinates; (3) Different types of coordinate transformations in these models were carried out by different relationships between the receptive fields (RFs) and the GFs of the hidden units; and (4) The properties of PPC neurons are in better accordance with the hidden units of the models that had multiple-output representations than the models that had only one single-output representation. In conclusion, the results show that the GF is an effective mechanism for performing coordinate transformations. The models also suggest that neurons with intermediate coding are to be expected in the process of coordinate transformations.