A Rap guanine nucleotide exchange factor enriched highly in the basal ganglia

Ras proteins, key regulators of growth, differentiation, and malignant transformation, recently have been implicated in synaptic function and region-specific learning and memory functions in the brain. Rap proteins, members of the Ras small G protein superfamily, can inhibit Ras signaling through the Ras/Raf-1/mitogen-activated protein (MAP) kinase pathway or, through B-Raf, can activate MAP kinase. Rap and Ras proteins both can be activated through guanine nucleotide exchange factors (GEFs). Many Ras GEFs, but to date only one Rap GEF, have been identified. We now report the cloning of a brain-enriched gene, CalDAG-GEFI, which has substrate specificity for Rap1A, dual binding domains for calcium (Ca2+) and diacylglycerol (DAG), and enriched expression in brain basal ganglia pathways and their axon-terminal regions. Expression of CalDAG-GEFI activates Rap1A and inhibits Ras-dependent activation of the Erk/MAP kinase cascade in 293T cells. Ca2+ ionophore and phorbol ester strongly and additively enhance this Rap1A activation. By contrast, CalDAG-GEFII, a second CalDAG-GEF family member that we cloned and found identical to RasGRP [Ebinu, J. O., Bottorff, D. A., Chan, E. Y. W., Stang, S. L., Dunn, R. J. & Stone, J. C. (1998) Science 280, 1082–1088], exhibits a different brain expression pattern and fails to activate Rap1A, but activates H-Ras, R-Ras, and the Erk/MAP kinase cascade under Ca2+ and DAG modulation. We propose that CalDAG-GEF proteins have a critical neuronal function in determining the relative activation of Ras and Rap1 signaling induced by Ca2+ and DAG mobilization. The expression of CalDAG-GEFI and CalDAG-GEFII in hematopoietic organs suggests that such control may have broad significance in Ras/Rap regulation of normal and malignant states.

Segregating the functions of human hippocampus

It is now accepted that hippocampal lesions impair episodic memory. However, the precise functional role of the hippocampus in episodic memory remains elusive. Recent functional imaging data implicate the hippocampus in processing novelty, a finding supported by human in vivo recordings and event-related potential studies. Here we measure hippocampal responses to novelty, using functional MRI (fMRI), during an item-learning paradigm generated from an artificial grammar system. During learning, two distinct types of novelty were periodically introduced: perceptual novelty, pertaining to the physical characteristics of stimuli (in this case visual characteristics), and exemplar novelty, reflecting semantic characteristics of stimuli (in this case grammatical status within a rule system). We demonstrate a left anterior hippocampal response to both types of novelty and adaptation of these responses with stimulus familiarity. By contrast to these novelty effects, we also show bilateral posterior hippocampal responses with increasing exemplar familiarity. These results suggest a functional dissociation within the hippocampus with respect to the relative familiarity of study items. Neural responses in anterior hippocampus index generic novelty, whereas posterior hippocampal responses index familiarity to stimuli that have behavioral relevance (i.e., only exemplar familiarity). These findings add to recent evidence for functional segregation within the human hippocampus during learning.

New infinite families of exact sums of squares formulas, Jacobi elliptic functions, and Ramanujan’s tau function

In this paper, we give two infinite families of explicit exact formulas that generalize Jacobi’s (1829) 4 and 8 squares identities to 4n2 or 4n(n + 1) squares, respectively, without using cusp forms. Our 24 squares identity leads to a different formula for Ramanujan’s tau function τ(n), when n is odd. These results arise in the setting of Jacobi elliptic functions, Jacobi continued fractions, Hankel or Turánian determinants, Fourier series, Lambert series, inclusion/exclusion, Laplace expansion formula for determinants, and Schur functions. We have also obtained many additional infinite families of identities in this same setting that are analogous to the η-function identities in appendix I of Macdonald’s work [Macdonald, I. G. (1972) Invent. Math. 15, 91–143]. A special case of our methods yields a proof of the two conjectured [Kac, V. G. and Wakimoto, M. (1994) in Progress in Mathematics, eds. Brylinski, J.-L., Brylinski, R., Guillemin, V. & Kac, V. (Birkhäuser Boston, Boston, MA), Vol. 123, pp. 415–456] identities involving representing a positive integer by sums of 4n2 or 4n(n + 1) triangular numbers, respectively. Our 16 and 24 squares identities were originally obtained via multiple basic hypergeometric series, Gustafson’s Cℓ nonterminating 6φ5 summation theorem, and Andrews’ basic hypergeometric series proof of Jacobi’s 4 and 8 squares identities. We have (elsewhere) applied symmetry and Schur function techniques to this original approach to prove the existence of similar infinite families of sums of squares identities for n2 or n(n + 1) squares, respectively. Our sums of more than 8 squares identities are not the same as the formulas of Mathews (1895), Glaisher (1907), Ramanujan (1916), Mordell (1917, 1919), Hardy (1918, 1920), Kac and Wakimoto, and many others.

Lack of tissue glucocorticoid reactivation in 11β-hydroxysteroid dehydrogenase type 1 knockout mice ameliorates age-related learning impairments

11β-hydroxysteroid dehydrogenase type 1 (11β-HSD-1) intracellularly regenerates active corticosterone from circulating inert 11-dehydrocorticosterone (11-DHC) in specific tissues. The hippocampus is a brain structure particularly vulnerable to glucocorticoid neurotoxicity with aging. In intact hippocampal cells in culture, 11β-HSD-1 acts as a functional 11β-reductase reactivating inert 11-DHC to corticosterone, thereby potentiating kainate neurotoxicity. We examined the functional significance of 11β-HSD-1 in the central nervous system by using knockout mice. Aged wild-type mice developed elevated plasma corticosterone levels that correlated with learning deficits in the watermaze. In contrast, despite elevated plasma corticosterone levels throughout life, this glucocorticoid-associated learning deficit was ameliorated in aged 11β-HSD-1 knockout mice, implicating lower intraneuronal corticosterone levels through lack of 11-DHC reactivation. Indeed, aged knockout mice showed significantly lower hippocampal tissue corticosterone levels than wild-type controls. These findings demonstrate that tissue corticosterone levels do not merely reflect plasma levels and appear to play a more important role in hippocampal functions than circulating blood levels. The data emphasize the crucial importance of local enzymes in determining intracellular glucocorticoid activity. Selective 11β-HSD-1 inhibitors may protect against hippocampal function decline with age.

Image recognition: Visual grouping, recognition, and learning

Vision extracts useful information from images. Reconstructing the three-dimensional structure of our environment and recognizing the objects that populate it are among the most important functions of our visual system. Computer vision researchers study the computational principles of vision and aim at designing algorithms that reproduce these functions. Vision is difficult: the same scene may give rise to very different images depending on illumination and viewpoint. Typically, an astronomical number of hypotheses exist that in principle have to be analyzed to infer a correct scene description. Moreover, image information might be extracted at different levels of spatial and logical resolution dependent on the image processing task. Knowledge of the world allows the visual system to limit the amount of ambiguity and to greatly simplify visual computations. We discuss how simple properties of the world are captured by the Gestalt rules of grouping, how the visual system may learn and organize models of objects for recognition, and how one may control the complexity of the description that the visual system computes.

Cue recognition and cue elaboration in learning from examples.

This paper describes the processes used by students to learn from worked-out examples and by working through problems. Evidence is derived from protocols of students learning secondary school mathematics and physics. The students acquired knowledge from the examples in the form of productions (condition-->action): first discovering conditions under which the actions are appropriate and then elaborating the conditions to enhance efficiency. Students devoted most of their attention to the condition side of the productions. Subsequently, they generalized the productions for broader application and acquired specialized productions for special problem classes.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.

Brain localization for arbitrary stimulus categories: a simple account based on Hebbian learning.

A central theme of cognitive neuroscience is that different parts of the brain perform different functions. Recent evidence from neuropsychology suggests that even the processing of arbitrary stimulus categories that are defined solely by cultural conventions (e.g., letters versus digits) can become spatially segregated in the cerebral cortex. How could the processing of stimulus categories that are not innate and that have no inherent structural differences become segregated? We propose that the temporal clustering of stimuli from a given category interacts with Hebbian learning to lead to functional localization. Neural network simulations bear out this hypothesis.