MOUSE-III: Learning rules of conformational analysis from X-ray data

MOUSE-III is learning program that finds rules of conformational analysis from raw cristallographie data. The program perceives molecular features, finds conformational classes in the data and then learns rules that link features to classes. The rules that MOUSE learns are capable of correctly assigning conformations to ring systems that were not used for training with greater than 95% accuracy, when MOUSE was presented with sufficient data. The rules also show a compression of as much as 99% when compared to the raw data. This is accomplished through abstraction and generalization. The algorithm is presented along with a carefully worked example. An example of a learned rule is also presented and analyzed. Some conclusions about the scope and limitations of the learning process are presented.     

MOUSE (Mitochondrial and Other Useful SEquences) a compilation of population genetic markers

SUMMARY: Mitochondrial and Other Useful SEquences (MOUSE) is an integrated and comprehensive compilation of mtDNA from hypervariable regions I and II and of the low recombining nuclear loci Xq13.3 from about 11 200 humans and great apes, whose geographic and if applicable, linguistic classification is stored with their aligned sequences and publication details. The goal is to provide population geneticists and genetic epidemiologists with a comprehensive and user friendly repository of sequences and population information that is usually dispersed in a variety of other sources. AVAILABILITY: http://www.gen-epi.de/mouse. SUPPLEMENTARY INFORMATION: Documentation and detailed information on population subgroups is available on the homepage: http://www.gen-epi.de/mouse     

Book Reviews

Book reviewed in this article:
Current Topics in Microbiology and Immunology. Volume 127. THE WILD MOUSE I N IMMUNOLOGY (1986). Edited by M. Potter, J.H. Nadeau & M.P. Cancro.
Introduction to Sterilization and Disinfection (1986). J.F. Gardner & M.M. Peel.
Micro-Organisms in Foods. Volume Sampling for Microbiological Analysis: Principles and Specific Applications (1986).     

Learning generative models of molecular dynamics

We introduce three algorithms for learning generative models of molecular structures from molecular dynamics simulations. The first algorithm learns a Bayesian-optimal undirected probabilistic model over user-specified covariates (e.g., fluctuations, distances, angles, etc). L1 regularization is used to ensure sparse models and thus reduce the risk of over-fitting the data. The topology of the resulting model reveals important couplings between different parts of the protein, thus aiding in the analysis of molecular motions. The generative nature of the model makes it well-suited to making predictions about the global effects of local structural changes (e.g., the binding of an allosteric regulator). Additionally, the model can be used to sample new conformations. The second algorithm learns a time-varying graphical model where the topology and parameters change smoothly along the trajectory, revealing the conformational sub-states. The last algorithm learns a Markov Chain over undirected graphical models which can be used to study and simulate kinetics. We demonstrate our algorithms on multiple molecular dynamics trajectories.     

Selective Cytotoxicity of Anti-Kappa Serum for B Lymphocytes

MOUSE lymphocytes can be divided into two distinct populations according to the density of immunoglobulin determinants on their surface. Lymphocytes with a high density of immunoglobulin are marrow-derived, nonthymus-processed, B cells, whereas lymphocytes with little or no immunoglobulin are thymus-derived, T cells^1–3. Since more than 95% of mouse immunoglobulin light chains are of the kappa type⁴, treatment of lymphocyte suspensions with an appropriate dilution of rabbit anti-mouse kappa serum and complement should be cytotoxic for only B lymphocytes. This prediction was tested by using lymphocyte populations enriched for either T or B cells or containing the two cell types in a known proportion.     

The Development of Motor Acts with Objects in the Preschool Child

Through the influence of his educative environment, the preschool child gradually learns to subjugate his actions to voluntary control. When the child has developed so far that he is able (at least partially) consciously to subordinate his actions to a specific goal, we can say that he has made an important step toward consciously assimilating the knowledge and complex skills he learns in school.     

Improving the performance of predicting users' subjective evaluation characteristics to reduce their fatigue in IEC

Users' fatigue is the biggest technological hurdle facing Interactive Evolutionary Computation (IEC). This paper introduces the idea of "absolute scale" and "neighbour scale" to improve the performance of predicting users' subjective evaluation characteristics in IEC, and thus it will accelerate EC convergence and reduce users' fatigue. We experimentally evaluate the effect of the proposed method using two benchmark functions. The experimental results show that the convergence speed of IEC using the proposed predictor, which learns from absolute evaluation data, is much faster than the conventional one, which learns from relative data, especially in early generations. Also, IEC with predictors that use recent data are more effective than those which use all past data.     

Adaptive computational models of fast learning of motion direction discrimination

In a previous study, we found that subjects' performance in a task of direction discrimination in stochastic motion stimuli shows fast improvement in the absence of feedback and the learned ability is retained over a period of time. We model this learning using two unsupervised approaches: a clustering model that learns to accommodate the motion noise, and an averaging model that learns to ignore the noise. Extensive simulations with the models show performance similar to psychophysical results.     

Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision

Zoonotic diseases threaten human health worldwide and are often associated with anthropogenic disturbance. Predicting how disturbance influences spillover risk is critical for effective disease intervention but difficult to achieve at fine spatial scales. Here, we develop a method that learns the spatial distribution of a reservoir species from aerial imagery. Our approach uses neural networks to extract features of known or hypothesized importance from images. The spatial distribution of these features is then summarized and linked to spatially explicit reservoir presence/absence data using boosted regression trees. We demonstrate the utility of our method by applying it to the reservoir of Lassa virus, Mastomys natalensis, within the West African nations of Sierra Leone and Guinea. We show that, when trained using reservoir trapping data and publicly available aerial imagery, our framework learns relationships between environmental features and reservoir occurrence and accurately ranks areas according to the likelihood of reservoir presence.     

Insect behaviour: learning for the future

Recent studies show that what, when and how a parasitic wasp learns is tailored to its specific ecological niche.     

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.     

Goal-direction and top-down control

We review the neural mechanisms that support top-down control of behaviour and suggest that goal-directed behaviour uses two systems that work in concert. A basal ganglia-centred system quickly learns simple, fixed goal-directed behaviours while a prefrontal cortex-centred system gradually learns more complex (abstract or long-term) goal-directed behaviours. Interactions between these two systems allow top-down control mechanisms to learn how to direct behaviour towards a goal but also how to guide behaviour when faced with a novel situation.     

Damage control: a guide to dealing with an infectious break

A change in the microbial status of laboratory animals can represent a disruptive event in the research process. The author suggests a sequence of events from the time a facility learns of a potential infectious "break," through investigation of its source, and its ultimate control.     

A learning network model of the neural integrator of the oculomotor system

Certain premotor neurons of the oculomotor system fire at a rate proportional to desired eye velocity. Their output is integrated by a network of neurons to supply an eye positon command to the motoneurons of the extraocular muscles. This network, known as the neural integrator, is calibrated during infancy and then maintained through development and trauma with remarkable precision. We have modeled this system with a self-organizing neural network that learns to integrate vestibular velocity commands to generate appropriate eye movements. It learns by using current eye movement on any given trial to calculate the amount of retinal image slip and this is used as the error signal. The synaptic weights are then changed using a straightforward algorithm that is independent of the network configuration and does not necessitate backwards propagation of information. Minimization of the error in this fashion causes the network to develop multiple positive feedback loops that enable it to integrate a push-pull signal without integrating the background rate on which it rides. The network is also capable of recovering from various lesions and of generating more complicated signals to simulate induced postsaccadic drift and compensation for eye muscle mechanics.     

Book Reviews

Book reviewed in this article:
PROVISIONAL DISTRIBUTION ATLAS OF AMPHIBIANS, REPTILES AND MAMMALS IN IRELAND (2nd edn.) Edited by Eanna Ni Lamhna.
LECTURE NOTES ON VERTEBRATE ZOOLOGY By Ronald Pearson & John N. Ball
THE CAMEL. ITS EVOLUTION, ECOLOGY, BEHAVIOUR, AND RELATIONSHIP TO MAN By H. Gauthier-Pilters & A. I. Dagg
SAVE THE DOLPHINS By Horace Dobbs
NORTH AMERICAN BISON: their classification and evolution By Jerry N. McDonald
BADGERS WITHOUT BIAS. AN OBJECTIVE LOOK AT THE CONTROVERSY ABOUT TUBERCULOSIS AND CATTLE By Robert W. Howard
SYMPOSIA OF THE ZOOLOGICAL SOCIETY OF LONDON No. 47: BIOLOGY OF THE HOUSE MOUSE. Edited by R. J. Berry.
HANDBOOK OF MARINE MAMMALS, Vols 1 and 2 Edited by S. H. Ridgeway & R. J. Harrison     

Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms.     

Online Multi-Modal Robust Non-Negative Dictionary Learning for Visual Tracking

Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.     

A Psychological Study of the Development of Motor Activity in the Preschool-Age Child

During the period from 4 to 6 years, the preschooler's motor activity becomes stronger and hardier, and his movements become more dextrous and coordinated. During this period a child also acquires a number of new motor skills that will play an important role in later life. Finally, he learns to execute movements consciously and deliberately.