A deformable model for the reconstruction of the neonatal cortex

We present a method based on deformable meshes for the reconstruction of the cortical surfaces of the developing human brain at the neonatal period. It employs a brain segmentation for the reconstruction of an initial inner cortical surface mesh. Errors in the segmentation resulting from poor tissue contrast in neonatal MRI and partial volume effects are subsequently accounted for by a local edge-based refinement. We show that the obtained surface models define the cortical boundaries more accurately than the segmentation. The surface meshes are further guaranteed to not intersect and subdivide the brain volume into disjoint regions. The proposed method generates topologically correct surfaces which facilitate both a flattening and spherical mapping of the cortex

The influence of the frequency of periodic disturbances on the maintenance of phytoplankton diversity

The influence of periodic disturbances of various frequency on the maintenance of the phytoplankton diversity was studied by semicontinuous competition experiments. Disturbances consisted of dilution events, which meant both addition of fresh nutrients and elimination of organisms. The intervals between dilution events varied from 1 to 14 days. Diversity was found to increase with increasing intervals between disturbances. coexisting species belonged to different strategy types: (a) species with rapid growth under enriched conditions, (b) species with good competitive abilities under impoverished conditions, (c) species with the ability to build up storage pools of the limiting nutrient. An increase of the number of coexisting species over the number that would have coexisted in steady state was only found when the interval exceeded one generation time

Role of the mesoamygdaloid dopamine projection in emotional learning

Amygdala dopamine is crucially involved in the acquisition of Pavlovian associations, as measured via conditioned approach to the location of the unconditioned stimulus (US). However, learning begins before skeletomotor output, so this study assessed whether amygdala dopamine is also involved in earlier 'emotional' learning. A variant of the conditioned reinforcement (CR) procedure was validated where training was restricted to curtail the development of selective conditioned approach to the US location, and effects of amygdala dopamine manipulations before training or later CR testing assessed. Experiment 1a presented a light paired (CS+ group) or unpaired (CS- group) with a US. There were 1, 2 or 10 sessions, 4 trials per session. Then, the US was removed, and two novel levers presented. One lever (CR+) presented the light, and lever pressing was recorded. Experiment 1b also included a tone stimulus. Experiment 2 applied intra-amygdala R(+) 7-OH-DPAT (10 nmol/1.0 A mu l/side) before two training sessions (Experiment 2a) or a CR session (Experiment 2b). For Experiments 1a and 1b, the CS+ group preferred the CR+ lever across all sessions. Conditioned alcove approach during 1 or 2 training sessions or associated CR tests was low and nonspecific. In Experiment 2a, R(+) 7-OH-DPAT before training greatly diminished lever pressing during a subsequent CR test, preferentially on the CR+ lever. For Experiment 2b, R(+) 7-OH-DPAT infusions before the CR test also reduced lever pressing. Manipulations of amygdala dopamine impact the earliest stage of learning in which emotional reactions may be most prevalent

Solving ill-posed bilevel programs

This paper deals with ill-posed bilevel programs, i.e., problems admitting multiple lower-level solutions for some upper-level parameters. Many publications have been devoted to the standard optimistic case of this problem, where the difficulty is essentially moved from the objective function to the feasible set. This new problem is simpler but there is no guaranty to obtain local optimal solutions for the original optimistic problem by this process. Considering the intrinsic non-convexity of bilevel programs, computing local optimal solutions is the best one can hope to get in most cases. To achieve this goal, we start by establishing an equivalence between the original optimistic problem an a certain set-valued optimization problem. Next, we develop optimality conditions for the latter problem and show that they generalize all the results currently known in the literature on optimistic bilevel optimization. Our approach is then extended to multiobjective bilevel optimization, and completely new results are derived for problems with vector-valued upper- and lower-level objective functions. Numerical implementations of the results of this paper are provided on some examples, in order to demonstrate how the original optimistic problem can be solved in practice, by means of a special set-valued optimization problem

From error bounds to the complexity of first-order descent methods for convex functions

This paper shows that error bounds can be used as effective tools for deriving complexity results for first-order descent methods in convex minimization. In a first stage, this objective led us to revisit the interplay between error bounds and the Kurdyka-\L ojasiewicz (KL) inequality. One can show the equivalence between the two concepts for convex functions having a moderately flat profile near the set of minimizers (as those of functions with H\"olderian growth). A counterexample shows that the equivalence is no longer true for extremely flat functions. This fact reveals the relevance of an approach based on KL inequality. In a second stage, we show how KL inequalities can in turn be employed to compute new complexity bounds for a wealth of descent methods for convex problems. Our approach is completely original and makes use of a one-dimensional worst-case proximal sequence in the spirit of the famous majorant method of Kantorovich. Our result applies to a very simple abstract scheme that covers a wide class of descent methods. As a byproduct of our study, we also provide new results for the globalization of KL inequalities in the convex framework. Our main results inaugurate a simple methodology: derive an error bound, compute the desingularizing function whenever possible, identify essential constants in the descent method and finally compute the complexity using the one-dimensional worst case proximal sequence. Our method is illustrated through projection methods for feasibility problems, and through the famous iterative shrinkage thresholding algorithm (ISTA), for which we show that the complexity bound is of the form $O(q^{k})$ where the constituents of the bound only depend on error bound constants obtained for an arbitrary least squares objective with $\ell^1$ regularization

Isoforms of U1-70k control subunit dynamics in the human spliceosomal U1 snRNP

Most human protein-encoding genes contain multiple exons that are spliced together, frequently in alternative arrangements, by the spliceosome. It is established that U1 snRNP is an essential component of the spliceosome, in human consisting of RNA and ten proteins, several of which are post- translationally modified and exist as multiple isoforms. Unresolved and challenging to investigate are the effects of these post translational modifications on the dynamics, interactions and stability of the particle. Using mass spectrometry we investigate the composition and dynamics of the native human U1 snRNP and compare native and recombinant complexes to isolate the effects of various subunits and isoforms on the overall stability. Our data reveal differential incorporation of four protein isoforms and dynamic interactions of subunits U1-A, U1-C and Sm-B/B’. Results also show that unstructured post- ranslationally modified C-terminal tails are responsible for the dynamics of Sm-B/B’ and U1-C and that their interactions with the Sm core are controlled by binding to different U1-70k isoforms and their phosphorylation status in vivo. These results therefore provide the important functional link between proteomics and structure as well as insight into the dynamic quaternary structure of the native U1 snRNP important for its function.This work was funded by: BBSRC (OVM), BBSRC and EPSRC (HH and NM), EU Prospects (HH), European Science Foundation (NM), the Royal Society (CVR), and fellowship from JSPS and HFSP (YM and DAPK respectively)

Development of microstructural and morphological cortical profiles in the neonatal brain

Interruptions to neurodevelopment during the perinatal period may have long-lasting consequences. However, to be able to investigate deviations in the foundation of proper connectivity and functional circuits, we need a measure of how this architecture evolves in the typically developing brain. To this end, in a cohort of 241 term-born infants, we used magnetic resonance imaging to estimate cortical profiles based on morphometry and microstructure over the perinatal period (37-44 weeks postmenstrual age, PMA). Using the covariance of these profiles as a measure of inter-areal network similarity (morphometric similarity networks; MSN), we clustered these networks into distinct modules. The resulting modules were consistent and symmetric, and corresponded to known functional distinctions, including sensory-motor, limbic, and association regions, and were spatially mapped onto known cytoarchitectonic tissue classes. Posterior regions became more morphometrically similar with increasing age, while peri-cingulate and medial temporal regions became more dissimilar. Network strength was associated with age: Within-network similarity increased over age suggesting emerging network distinction. These changes in cortical network architecture over an 8-week period are consistent with, and likely underpin, the highly dynamic processes occurring during this critical period. The resulting cortical profiles might provide normative reference to investigate atypical early brain development

HSPG-Binding Peptide Corresponding to the Exon 6a-Encoded Domain of VEGF Inhibits Tumor Growth by Blocking Angiogenesis in Murine Model

Vascular endothelial growth factor VEGF165 is a critical element for development of the vascular system in physiological and pathological angiogenesis. VEGF isoforms have different affinities for heparan sulphate proteoglycan (HSPG) as well as for VEGF receptors; HSPGs are important regulators in vascular development. Therefore, inhibition of interactions between VEGF and HSPGs may prevent angiogenesis. Here, we demonstrate that an HSPG-binding synthetic peptide, corresponding to exon 6a-encoded domain of VEGF gene, has anti-angiogenic property. This 20 amino acids synthetic peptide prevents VEGF165 binding to several different cell types, mouse embryonic sections and inhibits endothelial cell migration, despite its absence in VEGF165 sequence. Our in vivo anti-tumor studies show that the peptide inhibits tumor growth in both mouse Lewis-Lung Carcinoma and human Liposarcoma tumor-bearing animal models. This is the first evidence that a synthetic VEGF fragment corresponding to exon 6a has functional antagonism both in vitro and in vivo. We conclude that the above HPSG binding peptide (6a-P) is a potent inhibitor of angiogenesis-dependent diseases