A Bayesian model predicts the response of axons to molecular gradients

Axon guidance by molecular gradients plays a crucial role in wiring up the nervous system. However, the mechanisms axons use to detect gradients are largely unknown. We first develop a Bayesian “ideal observer” analysis of gradient detection by axons, based on the hypothesis that a principal constraint on gradient detection is intrinsic receptor binding noise. Second, from this model, we derive an equation predicting how the degree of response of an axon to a gradient should vary with gradient steepness and absolute concentration. Third, we confirm this prediction quantitatively by performing the first systematic experimental analysis of how axonal response varies with both these quantities. These experiments demonstrate a degree of sensitivity much higher than previously reported for any chemotacting system. Together, these results reveal both the quantitative constraints that must be satisfied for effective axonal guidance and the computational principles that may be used by the underlying signal transduction pathways, and allow predictions for the degree of response of axons to gradients in a wide variety of in vivo and in vitro settings.

Predicting residue-wise contact orders in proteins by support vector regression

Background The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

The CDKN2A (p16) gene and human cancer

CDKN2A, the gene encoding the cell-cycle inhibitor p16CDKN2A, was first identified in 1994. Since then, somatic mutations have been observed in many cancers and germline alterations have been found in kindreds with familial atypical multiple mole/melanoma (FAMMM), also known as atypical mole syndrome. In this review we tabulate the known mutations in this gene and discuss specific aspects, particularly with respect to germline mutations and cancer predisposition.

FGFR2 as a molecular target in endometrial cancer

Although molecularly targeted therapies have been effective in some cancer types, no targeted therapy is approved for use in endometrial cancer. The recent identification of activating mutations in fibroblast growth factor receptor 2 (FGFR2) in endometrial tumors has generated a new avenue for the development of targeted therapeutic agents. The majority of the mutations identified are identical to germline mutations in FGFR2 and FGFR3 that cause craniosynostosis and hypochondroplasia syndromes and result in both ligand-independent and ligand-dependent receptor activation. Mutations that predominantly occur in the endometrioid subtype of endometrial cancer, are mutually exclusive with KRAS mutation, but occur in the presence of PTEN abrogation. In vitro studies have shown that endometrial cancer cell lines with activating FGFR2 mutations are selectively sensitive to a pan-FGFR inhibitor, PD173074. Several agents with activity against FGFRs are currently in clinical trials. Investigation of these agents in endometrial cancer patients with activating FGFR2 mutations is warranted.

Computational approaches for modelling intrinsic noise and delays in genetic regulatory networks

This chapter focuses on the interactions and roles between delays and intrinsic noise effects within cellular pathways and regulatory networks. We address these aspects by focusing on genetic regulatory networks that share a common network motif, namely the negative feedback loop, leading to oscillatory gene expression and protein levels. In this context, we discuss computational simulation algorithms for addressing the interplay of delays and noise within the signaling pathways based on biological data. We address implementational issues associated with efficiency and robustness. In a molecular biology setting we present two case studies of temporal models for the Hes1 gene (Monk, 2003; Hirata et al., 2002), known to act as a molecular clock, and the Her1/Her7 regulatory system controlling the periodic somite segmentation in vertebrate embryos (Giudicelli and Lewis, 2004; Horikawa et al., 2006).

A Raman spectroscopic study of the mono-hydrogen phosphate mineral dorfmanite Na2(PO3OH)•2H2O – and in comparison with brushite

Aspects of the molecular structure of the mineral dorfmanite Na2(PO3OH)•2H2O were determined by Raman spectroscopy. The mineral originated from the Kedykverpakhk Mt., Lovozero, Kola Peninsula, Russia. Raman bands are assigned to the hydrogen phosphate units. The intense Raman band at 949 cm-1 and the less intense band at 866 cm-1 are assigned to the PO3 and POH stretching vibrations. Bands at 991, 1066 and 1141 cm-1 are assigned to the ν3 antisymmetric stretching modes. Raman bands at 393, 413 and 448 cm-1 and 514, 541 and 570 cm-1 are attributed to the ν2 and ν4 bending modes of the HPO4 units, respectively. Raman bands at 3373, 3443 and 3492 cm-1 are assigned to water stretching vibrations. POH stretching vibrations are identified by bands at 2904, 3080 and 3134 cm-1. Raman spectroscopy has proven very useful for the study of the structure of the mineral dorfmanite.

On a Syntactic Characterization of Classification with a Mind Change Bound

Most learning paradigms impose a particular syntax on the class of concepts to be learned; the chosen syntax can dramatically affect whether the class is learnable or not. For classification paradigms, where the task is to determine whether the underlying world does or does not have a particular property, how that property is represented has no implication on the power of a classifier that just outputs 1’s or 0’s. But is it possible to give a canonical syntactic representation of the class of concepts that are classifiable according to the particular criteria of a given paradigm? We provide a positive answer to this question for classification in the limit paradigms in a logical setting, with ordinal mind change bounds as a measure of complexity. The syntactic characterization that emerges enables to derive that if a possibly noncomputable classifier can perform the task assigned to it by the paradigm, then a computable classifier can also perform the same task. The syntactic characterization is strongly related to the difference hierarchy over the class of open sets of some topological space; this space is naturally defined from the class of possible worlds and possible data of the learning paradigm.

The molecular structure of the mineral sarmientite Fe2(AsO4,SO4)2(OH)6•5H2O : implications for arsenic accumulation and removal

Sarmientite is an environmental mineral; its formation in soils enables the entrapment and immobilisation of arsenic. The mineral sarmientite is often amorphous making the application of X-ray diffraction difficult. Vibrational spectroscopy has been applied to the study of sarmientite. Bands are attributed to the vibrational units of arsenate, sulphate, hydroxyl and water. Raman bands at 794, 814 and 831 cm−1 are assigned to the ν3 (AsO4)3− antisymmetric stretching modes and the ν1 symmetric stretching mode is observed at 891 cm−1. Raman bands at 1003 and 1106 cm−1 are attributed to vibrations. The Raman band at 484 cm−1 is assigned to the triply degenerate (AsO4)3− bending vibration. The high intensity Raman band observed at 355 cm−1 (both lower and upper) is considered to be due to the (AsO4)3−ν2 bending vibration. Bands attributed to water and OH stretching vibrations are observed.

Classification of packet contents for malware detection

Many existing schemes for malware detection are signature-based. Although they can effectively detect known malwares, they cannot detect variants of known malwares or new ones. Most network servers do not expect executable code in their in-bound network traffic, such as on-line shopping malls, Picasa, Youtube, Blogger, etc. Therefore, such network applications can be protected from malware infection by monitoring their ports to see if incoming packets contain any executable contents. This paper proposes a content-classification scheme that identifies executable content in incoming packets. The proposed scheme analyzes the packet payload in two steps. It first analyzes the packet payload to see if it contains multimedia-type data (such as . If not, then it classifies the payload either as text-type (such as or executable. Although in our experiments the proposed scheme shows a low rate of false negatives and positives (4.69% and 2.53%, respectively), the presence of inaccuracies still requires further inspection to efficiently detect the occurrence of malware. In this paper, we also propose simple statistical and combinatorial analysis to deal with false positives and negatives.

Simple classification of walking activities using commodity smart phones

People interact with mobile computing devices everywhere, while sitting, walking, running or even driving. Adapting the interface to suit these contexts is important, thus this paper proposes a simple human activity classification system. Our approach uses a vector magnitude recognition technique to detect and classify when a person is stationary (or not walking), casually walking, or jogging, without any prior training. The user study has confirmed the accuracy.