A screen for hoxb1-regulated genes identifies ppp1r14al as a regulator of the rhombomere 4 Fgf-signaling center

AbstractSegmentation of the vertebrate hindbrain into multiple rhombomeres is essential for proper formation of the cerebellum, cranial nerves and cranial neural crest. Paralog group 1 (PG1) hox genes are expressed early in the caudal hindbrain and are required for rhombomere formation. Accordingly, loss of PG1 hox function disrupts development of caudal rhombomeres in model organisms and causes brainstem defects, associated with cognitive impairment, in humans. In spite of this important role for PG1 hox genes, transcriptional targets of PG1 proteins are not well characterized. Here we use ectopic expression together with embryonic dissection to identify novel targets of the zebrafish PG1 gene hoxb1b. Of 100 genes up-regulated by hoxb1b, 54 were examined and 25 were found to represent novel hoxb1b regulated hindbrain genes. The ppp1r14al gene was analyzed in greater detail and our results indicate that Hoxb1b is likely to directly regulate ppp1r14al expression in rhombomere 4. Furthermore, ppp1r14al is essential for establishment of the earliest hindbrain signaling-center in rhombomere 4 by regulating expression of fgf3

The Non-linear Dynamics of Meaning-Processing in Social Systems

Social order cannot be considered as a stable phenomenon because it contains an order of reproduced expectations. When the expectations operate upon one another, they generate a non-linear dynamics that processes meaning. Specific meaning can be stabilized, for example, in social institutions, but all meaning arises from a horizon of possible meanings. Using Luhmann's (1984) social systems theory and Rosen's (1985) theory of anticipatory systems, I submit equations for modeling the processing of meaning in inter-human communication. First, a self-referential system can use a model of itself for the anticipation. Under the condition of functional differentiation, the social system can be expected to entertain a set of models; each model can also contain a model of the other models. Two anticipatory mechanisms are then possible: one transversal between the models, and a longitudinal one providing the modeled systems with meaning from the perspective of hindsight. A system containing two anticipatory mechanisms can become hyper-incursive. Without making decisions, however, a hyper-incursive system would be overloaded with uncertainty. Under this pressure, informed decisions tend to replace the "natural preferences" of agents and an order of cultural expectations can increasingly be shaped

Representation in the (Artificial) Immune System

Much of contemporary research in Artificial Immune Systems (AIS) has partitioned into either algorithmic machine learning and optimisation, or, modelling biologically plausible dynamical systems, with little overlap between. We propose that this dichotomy is somewhat to blame for the lack of significant advancement of the field in either direction and demonstrate how a simplistic interpretation of Perelson’s shape-space formalism may have largely contributed to this dichotomy. In this paper, we motivate and derive an alternative representational abstraction. To do so we consider the validity of shape-space from both the biological and machine learning perspectives. We then take steps towards formally integrating these perspectives into a coherent computational model of notions such as life-long learning, degeneracy, constructive representations and contextual recognition—rhetoric that has long inspired work in AIS, while remaining largely devoid of operational definition