Principles of event framing : genetic stability in grammar and discourse

Ever since Wilhelm von Humboldt’s (1836) pioneering study of Nahuatl, linguists have recurrently recognized that languages differ fundamentally in the syntactic weight they attribute to noun-phrases as the arguments of a verb. Currently, the most prominent attempts to turn this intuition into a precise hypothesis revolve around the notion of ‘configurationality’

Implicit artificial syntax processing: Genes, preference, and bounded recursion

The first objective of this study was to compare the brain network engaged by preference classification and the standard grammaticality classification after implicit artificial syntax acquisition by re-analyzing previously reported event-related fMRI data. The results show that preference and grammaticality classification engage virtually identical brain networks, including Broca’s region, consistent with previous behavioral findings. Moreover, the results showed that the effects related to artificial syntax in Broca’s region were essentially the same when masked with variability related to natural syntax processing in the same participants. The second objective was to explore CNTNAP2-related effects in implicit artificial syntax learning by analyzing behavioral and event-related fMRI data from a subsample. The CNTNAP2 gene has been linked to specific language impairment and is controlled by the FOXP2 transcription factor. CNTNAP2 is expressed in language related brain networks in the developing human brain and the FOXP2–CNTNAP2 pathway provides a mechanistic link between clinically distinct syndromes involving disrupted language. Finally, we discuss the implication of taking natural language to be a neurobiological system in terms of bounded recursion and suggest that the left inferior frontal region is a generic on-line sequence processor that unifies information from various sources in an incremental and recursive manner

Global Network Alignment

Motivation: High-throughput methods for detecting molecular interactions have lead to a plethora of biological network data with much more yet to come, stimulating the development of techniques for biological network alignment. Analogous to sequence alignment, efficient and reliable network alignment methods will improve our understanding of biological systems. Network alignment is computationally hard. Hence, devising efficient network alignment heuristics is currently one of the foremost challenges in computational biology. 

Results: We present a superior heuristic network alignment algorithm, called Matching-based GRAph ALigner (M-GRAAL), which can process and integrate any number and type of similarity measures between network nodes (e.g., proteins), including, but not limited to, any topological network similarity measure, sequence similarity, functional similarity, and structural similarity. This is efficient in resolving ties in similarity measures and in finding a combination of similarity measures yielding the largest biologically sound alignments. When used to align protein-protein interaction (PPI) networks of various species, M-GRAAL exposes the largest known functional and contiguous regions of network similarity. Hence, we use M-GRAAL’s alignments to predict functions of un-annotated proteins in yeast, human, and bacteria _C. jejuni_ and _E. coli_. Furthermore, using M-GRAAL to compare PPI networks of different herpes viruses, we reconstruct their phylogenetic relationship and our phylogenetic tree is the same as sequenced-based one

Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges faced by these methods, in particular detection of sparse and small or sub- complexes and discerning of overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.Comment: 1 Tabl

A unified approach towards the development of Swedish as L2: a processability account

This paper has two main objectives: (a) to put the vast body of research on Swedish as a second language (SSL) into one coherent framework; and (b) to test the predictions deriving from processability theory (Pienemann, 1998a, 1998b) for Swedish against this empirical database. We will survey the 14 most prominent research projects on SSL covering wide areas of syntax and morphology in longitudinal and cross-sectional studies. This survey is the first to be carried out for Swedish, and it will bring the body of two decades of research into one unified framework. We proceed in the following steps: First, a brief summary of processability theory is given. Then the theory is used to generate a unifying framework for the development of the specific L2 grammatical system (Swedish). Finally, the new framework is tested in the above-mentioned empirical studies