The postfusion structure of baculovirus gp64 supports a unified view of viral fusion machines

Viral fusion proteins mediate the merger of host and viral membranes during cell entry for all enveloped viruses. Baculovirus glycoprotein gp64 (gp64) is unusual in promoting entry into both insect and mammalian cells and is distinct from established class I and class II fusion proteins. We report the crystal structure of its postfusion form, which explains a number of gp64's biological properties including its cellular promiscuity, identifies the fusion peptides and shows it to be the third representative of a new class (III) of fusion proteins with unexpected structural homology with vesicular stomatitis virus G and herpes simplex virus type 1 gB proteins. We show that domains of class III proteins have counterparts in both class I and II proteins, suggesting that all these viral fusion machines are structurally more related than previously thought.

Amino terminal interaction in the prion protein identified using fusion to green fluorescent protein

In contrast to the well-characterized carboxyl domain, the amino terminal half of the mature cellular prion protein has no defined structure. Here, following fusion of mouse prion protein fragments to green fluorescence protein as a reporter of protein stability, we report extreme variability in fluorescence level that is dependent on the prion fragment expressed. In particular, exposure of the extreme amino terminus in the context of a truncated prion protein molecule led to rapid degradation, whereas the loss of only six amino terminal residues rescued high level fluorescence. Study of the precise endpoints and residue identity associated with high fluorescence suggested a domain within the amino terminal half of the molecule defined by a long-range intramolecular interaction between 23KKRPKP28 and 143DWED146 and dependent upon the anti-parallel beta-sheet ending at residue 169 and normally associated with the structurally defined carboxyl terminal domain. This previously unreported interaction may be significant for understanding prion bioactivity and for structural studies aimed at the complete prion structure.

A mixture of experts network structure construction algorithm for modelling and control

This paper introduces a new fast, effective and practical model structure construction algorithm for a mixture of experts network system utilising only process data. The algorithm is based on a novel forward constrained regression procedure. Given a full set of the experts as potential model bases, the structure construction algorithm, formed on the forward constrained regression procedure, selects the most significant model base one by one so as to minimise the overall system approximation error at each iteration, while the gate parameters in the mixture of experts network system are accordingly adjusted so as to satisfy the convex constraints required in the derivation of the forward constrained regression procedure. The procedure continues until a proper system model is constructed that utilises some or all of the experts. A pruning algorithm of the consequent mixture of experts network system is also derived to generate an overall parsimonious construction algorithm. Numerical examples are provided to demonstrate the effectiveness of the new algorithms. The mixture of experts network framework can be applied to a wide variety of applications ranging from multiple model controller synthesis to multi-sensor data fusion.

Image fusion based on median filters and SOFM neural networks: a three-step scheme

This paper presents a new image data fusion scheme by combining median filtering with self-organizing feature map (SOFM) neural networks. The scheme consists of three steps: (1) pre-processing of the images, where weighted median filtering removes part of the noise components corrupting the image, (2) pixel clustering for each image using self-organizing feature map neural networks, and (3) fusion of the images obtained in Step (2), which suppresses the residual noise components and thus further improves the image quality. It proves that such a three-step combination offers an impressive effectiveness and performance improvement, which is confirmed by simulations involving three image sensors (each of which has a different noise structure).

Assessment of thought–shape fusion: initial validation of a short version of the trait thought–shape fusion scale

Objective: Thought–shape fusion (TSF) is a cognitive distortion that has been linked to eating pathology. Two studies were conducted to further explore this phenomenon and to establish the psychometric properties of a French short version of the TSF scale. Method: In Study 1, students (n 5 284) completed questionnaires assessing TSF and related psychopathology. In Study 2, the responses of women with eating disorders (n 5 22) and women with no history of an eating disorder (n 5 23) were compared. Results: The French short version of the TSF scale has a unifactorial structure, with convergent validity with measures of eating pathology, and good internal consistency. Depression, eating pathology, body dissatisfaction, and thought-action fusion emerged as predictors of TSF. Individuals with eating disorders have higher TSF, and more clinically relevant food-related thoughts than do women with no history of an eating disorder. Discussion: This research suggests that the shortened TSF scale can suitably measure this construct, and provides support for the notion that TSF is associated with eating pathology.