Auriculo-condylar syndrome: mapping of a first locus and evidence for genetic heterogeneity

Auriculo-condylar syndrome (ACS), an autosomal dominant disorder of first and second pharyngeal arches, is characterized by malformed ears (`question mark ears`), prominent cheeks, microstomia, abnormal temporomandibular joint, and mandibular condyle hypoplasia. Penetrance seems to be complete, but there is high inter-and intra-familial phenotypic variation, with no evidence of genetic heterogeneity. We herein describe a new multigeneration family with 11 affected individuals (F1), in whom we confirm intra-familial clinical variability. Facial asymmetry, a clinical feature not highlighted in other ACS reports, was highly prevalent among the patients reported here. The gene responsible for ACS is still unknown and its identification will certainly contribute to the understanding of human craniofacial development. No chromosomal rearrangements have been associated with ACS, thus mapping and positional cloning is the best approach to identify this disease gene. To map the ACS gene, we conducted linkage analysis in two large ACS families, F1 and F2 (F2; reported elsewhere). Through segregation analysis, we first excluded three known loci associated with disorders of first and second pharyngeal arches (Treacher Collins syndrome, oculo-auriculo-vertebral spectrum, and Townes-Brocks syndrome). Next, we performed a wide genome search and we observed evidence of linkage to 1p21.1-q23.3 in F2 (LOD max 3.01 at theta = 0). Interestingly, this locus was not linked to the phenotype segregating in F1. Therefore, our results led to the mapping of a first locus of ACS (ACS1) and also showed evidence for genetic heterogeneity, suggesting that there are at least two loci responsible for this phenotype.

Molecular Characterization of Propolis-Induced Cell Death in Saccharomyces cerevisiae

Propolis, a natural product of plant resins, is used by the bees to seal holes in their honeycombs and protect the hive entrance. However, propolis has also been used in folk medicine for centuries. Here, we apply the power of Saccharomyces cerevisiae as a model organism for studies of genetics, cell biology, and genomics to determine how propolis affects fungi at the cellular level. Propolis is able to induce an apoptosis cell death response. However, increased exposure to propolis provides a corresponding increase in the necrosis response. We showed that cytochrome c but not endonuclease G (Nuc1p) is involved in propolis-mediated cell death in S. cerevisiae. We also observed that the metacaspase YCA1 gene is important for propolis-mediated cell death. To elucidate the gene functions that may be required for propolis sensitivity in eukaryotes, the full collection of about 4,800 haploid S. cerevisiae deletion strains was screened for propolis sensitivity. We were able to identify 138 deletion strains that have different degrees of propolis sensitivity compared to the corresponding wild-type strains. Systems biology revealed enrichment for genes involved in the mitochondrial electron transport chain, vacuolar acidification, negative regulation of transcription from RNA polymerase II promoter, regulation of macroautophagy associated with protein targeting to vacuoles, and cellular response to starvation. Validation studies indicated that propolis sensitivity is dependent on the mitochondrial function and that vacuolar acidification and autophagy are important for yeast cell death caused by propolis.

Gamma Knife(A (R)) 3-D Dose Distribution Mapping by Magnetic Resonance Imaging

In medical processes where ionizing radiation is used, dose planning and dose delivery are the key elements to patient safety and treatment success, particularly, when the delivered dose in a single session of treatment can be an order of magnitude higher than the regular doses of radiotherapy. Therefore, the radiation dose should be well defined and precisely delivered to the target while minimizing radiation exposure to surrounding normal tissues [1]. Several methods have been proposed to obtain three-dimensional (3-D) dose distribution [2, 3]. In this paper, we propose an alternative method, which can be easily implemented in any stereotactic radiosurgery center with a magnetic resonance imaging (MRI) facility. A phantom with or without scattering centers filled with Fricke gel solution is irradiated with Gamma Knife(A (R)) system at a chosen spot. The phantom can be a replica of a human organ such as head, breast or any other organ. It can even be constructed from a real 3-D MR image of an organ of a patient using a computer-aided construction and irradiated at a specific region corresponding to the tumor position determined by MRI. The spin-lattice relaxation time T (1) of different parts of the irradiated phantom is determined by localized spectroscopy. The T (1)-weighted phantom images are used to correlate the image pixels intensity to the absorbed dose and consequently a 3-D dose distribution with a high resolution is obtained.

A Hopping Mechanism for Cargo Transport by Molecular Motors on Crowded Microtubules

Most models designed to study the bidirectional movement of cargos as they are driven by molecular motors rely on the idea that motors of different polarities can be coordinated by external agents if arranged into a motor-cargo complex to perform the necessary work Gross, Hither and yon: a review of bidirectional microtubule-based transport (Gross in Phys. Biol. 1:R1-R11, 2004). Although these models have provided us with important insights into these phenomena, there are still many unanswered questions regarding the mechanisms through which the movement of the complex takes place on crowded microtubules. For example (i) how does cargo-binding affect motor motility? and in connection with that-(ii) how does the presence of other motors (and also other cargos) on the microtubule affect the motility of the motor-cargo complex? We discuss these questions from a different perspective. The movement of a cargo is conceived here as a hopping process resulting from the transference of cargo between neighboring motors. In the light of this, we examine the conditions under which cargo might display bidirectional movement even if directed by motors of a single polarity. The global properties of the model in the long-time regime are obtained by mapping the dynamics of the collection of interacting motors and cargos into an asymmetric simple exclusion process (ASEP) which can be resolved using the matrix ansatz introduced by Derrida (Derrida and Evans in Nonequilibrium Statistical Mechanics in One Dimension, pp. 277-304, 1997; Derrida et al. in J. Phys. A 26: 1493-1517, 1993).

Loops, Chains, Sheets, and Networks from Variable Coordination of Cu(hfac)(2) with a Flexibly Hinged Aminoxyl Radical Ligand

One pair of reactants, Cu(hfac)(2) = M and the hinge-flexible radical ligand 5-(3-N-tert-butyl-N-aminoxylphenyl)pyrimidine (3PPN = L), yields a diverse set of five coordination complexes: a cyclic loop M(2)L(1) dimer; a 1:1 cocrystal between an M(2)L(2) loop and an ML(2) fragment; a ID chain of M(2)L(2) loops linked by M; two 2D M(3)L(2) networks of (M-L)(n) chains crosslinked by M with different repeat length pitches; a 3D M(3)L(2) network of M(2)L(2) loops cross-linking (M-L)(n)-type chains with connectivity different from those in the 2D networks. Most of the higher dimensional complexes exhibit reversible, temperature-dependent spin-state conversion of high-temperature paramagnetic states to lower magnetic moment states having antiferromagnetic exchange within Cu-ON bonds upon cooling, with accompanying bond contraction. The 3D complex also exhibited antiferromagnetic exchange between Cu(II) ions linked in chains through pyrimidine rings.

From decoherence-free channels to decoherence-free and quasi-free subspaces within bosonic dissipative networks

We present a technique to build, within a dissipative bosonic network, decoherence-free channels (DFCs): a group of normal-mode oscillators with null effective damping rates. We verify that the states protected within the DFC define the well-known decoherence-free subspaces (DFSs) when mapped back into the natural network oscillators. Therefore, our technique to build protected normal-mode channels turns out to be an alternative way to build DFSs, which offers advantages over the conventional method. It enables the computation of all the network-protected states at once, as well as leading naturally to the concept of the decoherence quasi-free subspace (DQFS), inside which a superposition state is quasi-completely protected against decoherence. The concept of the DQFS, weaker than that of the DFS, may provide a more manageable mechanism to control decoherence. Finally, as an application of the DQFSs, we show how to build them for quasi-perfect state transfer in networks of coupled quantum dissipative oscillators.

Comparison of the interactomic networks of different species in terms of accessibility

Protein-protein interaction networks were investigated in terms of outward accessibility, which quantifies the effectiveness of each protein in accessing other proteins and is related to the internality of nodes. By comparing the accessibility between 144 ortholog proteins in yeast and the fruit fly, we found that the accessibility tends to be higher among proteins in the fly than in yeast. In addition, z-scores of the accessibility calculated for different species revealed that the protein networks of less evolved species tend to be more random than those of more evolved species. The accessibility was also used to identify the border of the yeast protein interaction network, which was found to be mainly composed of viable proteins.

Modularity and robustness of bone networks

Cortical bones, essential for mechanical support and structure in many animals, involve a large number of canals organized in intricate fashion. By using state-of-the art image analysis and computer graphics, the 3D reconstruction of a whole bone (phalange) of a young chicken was obtained and represented in terms of a complex network where each canal was associated to an edge and every confluence of three or more canals yielded a respective node. The representation of the bone canal structure as a complex network has allowed several methods to be applied in order to characterize and analyze the canal system organization and the robustness. First, the distribution of the node degrees (i.e. the number of canals connected to each node) confirmed previous indications that bone canal networks follow a power law, and therefore present some highly connected nodes (hubs). The bone network was also found to be partitioned into communities or modules, i.e. groups of nodes which are more intensely connected to one another than with the rest of the network. We verified that each community exhibited distinct topological properties that are possibly linked with their specific function. In order to better understand the organization of the bone network, its resilience to two types of failures (random attack and cascaded failures) was also quantified comparatively to randomized and regular counterparts. The results indicate that the modular structure improves the robustness of the bone network when compared to a regular network with the same average degree and number of nodes. The effects of disease processes (e. g., osteoporosis) and mutations in genes (e.g., BMP4) that occur at the molecular level can now be investigated at the mesoscopic level by using network based approaches.

Storing quantum states in bosonic dissipative networks

By considering a network of dissipative quantum harmonic oscillators, we deduce and analyse the optimum topologies which are able to store quantum superposition states, protecting them from decoherence, for the longest period of time. The storage is made dynamically, in that the states to be protected evolve through the network before being retrieved back in the oscillator where they were prepared. The decoherence time during the dynamic storage process is computed and we demonstrate that it is proportional to the number of oscillators in the network for a particular regime of parameters.

Complex networks analysis of manual and machine translations

Complex networks have been increasingly used in text analysis, including in connection with natural language processing tools, as important text features appear to be captured by the topology and dynamics of the networks. Following previous works that apply complex networks concepts to text quality measurement, summary evaluation, and author characterization, we now focus on machine translation (MT). In this paper we assess the possible representation of texts as complex networks to evaluate cross-linguistic issues inherent in manual and machine translation. We show that different quality translations generated by NIT tools can be distinguished from their manual counterparts by means of metrics such as in-(ID) and out-degrees (OD), clustering coefficient (CC), and shortest paths (SP). For instance, we demonstrate that the average OD in networks of automatic translations consistently exceeds the values obtained for manual ones, and that the CC values of source texts are not preserved for manual translations, but are for good automatic translations. This probably reflects the text rearrangements humans perform during manual translation. We envisage that such findings could lead to better NIT tools and automatic evaluation metrics.

Using metrics from complex networks to evaluate machine translation

Establishing metrics to assess machine translation (MT) systems automatically is now crucial owing to the widespread use of MT over the web. In this study we show that such evaluation can be done by modeling text as complex networks. Specifically, we extend our previous work by employing additional metrics of complex networks, whose results were used as input for machine learning methods and allowed MT texts of distinct qualities to be distinguished. Also shown is that the node-to-node mapping between source and target texts (English-Portuguese and Spanish-Portuguese pairs) can be improved by adding further hierarchical levels for the metrics out-degree, in-degree, hierarchical common degree, cluster coefficient, inter-ring degree, intra-ring degree and convergence ratio. The results presented here amount to a proof-of-principle that the possible capturing of a wider context with the hierarchical levels may be combined with machine learning methods to yield an approach for assessing the quality of MT systems. (C) 2010 Elsevier B.V. All rights reserved.

Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or Virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that Could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients - batch learning and clutter detection - the NMF mechanism was capable to infer perfectly the correct object-word mapping. (C) 2009 Elsevier Ltd. All rights reserved.

New Pd(II) and Pt(II) complexes with N,S-chelated pyrazolonate ligands: Molecular and supramolecular structure and preliminary study of their in vitro antitumoral activity

New Pd(II) and Pt(II) complexes [ML2] (HL = a substituted 2,5-dihydro-5-oxo-1H-pyrazolone-1-carbothioamide) have been synthesized by reacting K2MCl4 (M = Pd, Pt) or Pd(OAc)(2) with beta-ketoester thiosemicarbazones. The structures of seven of these complexes were determined by X-ray diffraction. Although all exhibit a distorted square-planar coordination with trans- or (in one case) cis-[MN2S2] kernels, their supramolecular arrangements vary widely from isolated molecules to 3D-networks. The in vitro antitumoral assays performed with two HL ligands and their metal complexes showed significant cytostatic activity for the latter, with the most active [ML2] derivative (a palladium complex) being about sixteen times more active than cis-DDP against the cis-platinum-resistant cell line A2780cisR. (c) 2007 Elsevier Inc. All rights reserved.

Mapping the Intramolecular Vibrational Energy Flow in Proteins Reveals Functionally Important Residues

Unveiling the mechanisms of energy relaxation in biomolecules is key to our understanding of protein stability, allostery, intramolecular signaling, and long-lasting quantum coherence phenomena at ambient temperatures. Yet, the relationship between the pathways of energy transfer and the functional role of the residues involved remains largely unknown. Here, we develop a simulation method of mapping out residues that are highly efficient in relaxing an initially localized excess vibrational energy and perform site-directed mutagenesis functional assays to assess the relevance of these residues to protein function. We use the ligand binding domains of thyroid hormone receptor (TR) subtypes as a test case and find that conserved arginines, which are critical to TR transactivation function, are the most effective heat diffusers across the protein structure. These results suggest a hitherto unsuspected connection between a residue`s ability to mediate intramolecular vibrational energy redistribution and its functional relevance.

Resilience of protein-protein interaction networks as determined by their large-scale topological features

The relationship between the structure and function of biological networks constitutes a fundamental issue in systems biology. Particularly, the structure of protein-protein interaction networks is related to important biological functions. In this work, we investigated how such a resilience is determined by the large scale features of the respective networks. Four species are taken into account, namely yeast Saccharomyces cerevisiae, worm Caenorhabditis elegans, fly Drosophila melanogaster and Homo sapiens. We adopted two entropy-related measurements (degree entropy and dynamic entropy) in order to quantify the overall degree of robustness of these networks. We verified that while they exhibit similar structural variations under random node removal, they differ significantly when subjected to intentional attacks (hub removal). As a matter of fact, more complex species tended to exhibit more robust networks. More specifically, we quantified how six important measurements of the networks topology (namely clustering coefficient, average degree of neighbors, average shortest path length, diameter, assortativity coefficient, and slope of the power law degree distribution) correlated with the two entropy measurements. Our results revealed that the fraction of hubs and the average neighbor degree contribute significantly for the resilience of networks. In addition, the topological analysis of the removed hubs indicated that the presence of alternative paths between the proteins connected to hubs tend to reinforce resilience. The performed analysis helps to understand how resilience is underlain in networks and can be applied to the development of protein network models.