The Melbourne Family Grief Study, II: Psychosocial morbidity and grief in bereaved families.

OBJECTIVE: The aim of this study was to describe the intensity of grief, the psychosocial morbidity, and the coping patterns in members of families classified according to a typology of family functioning comprising supportive, conflict-resolving, intermediate, sullen, and hostile classes. METHOD: One hundred fifteen families were assessed longitudinally 6 weeks (time 1), 6 months (time 2), and 13 months (time 3) after the death of a parent (constituting 670 individual responses) on measures of grief intensity, psychological state, social adjustment, and family coping. A previously described typology of perceptions of family functioning was applied. Repeated measures multivariate analysis of variance based on both individuals and families and post hoc comparisons of significant results were undertaken. RESULTS: Sullen families displayed the most intense grief and the most severe psychosocial morbidity. Well-functioning families (supportive and conflict-resolving) resolved their grief and adjusted more adaptively than their dysfunctional counterparts (intermediate, sullen, and hostile families). There were no cluster-by-time interactions. The clusters accounted for 15.7% of the variance in depression (Beck Depression Inventory) and 27.9% of the variance in social functioning (Social Adjustment Scale). Well-functioning families used more family coping strategies (Family Crisis Oriented Personal Evaluation Scales). CONCLUSIONS: More intense grief and greater psychosocial morbidity are found in sullen, hostile, and intermediate bereaved families than in the more adaptive supportive and conflict-resolving types. At-risk families are identifiable and could be treated preventively to reduce morbidity.

The Melbourne Family Grief Study, I: Perceptions of family functioning in bereavement.

OBJECTIVE: The aim of this study was to identify patterns of family functioning in adult families after the death of a parent. METHOD: One hundred fifteen families completed measures of family functioning, grief, psychological state, and social adjustment 6 weeks (time 1), 6 months (time 2), and 13 months (time 3) after the death of a parent (a total of 670 individual responses). Cluster analytic methods were applied to develop a typology of perceptions of family functioning during bereavement. RESULTS: Five types of families emerged from dimensions of cohesiveness, conflict, and expressiveness on the Family Environment Scale. Thirty-six percent of the families were considered supportive because of their high cohesiveness, and another 23% resolved conflict effectively. Two types were dysfunctional: hostile families, distinguished by high conflict, low cohesiveness, and poor expressiveness, and sullen families, who had more moderate limitations in these three areas; they declined in frequency from 30% at time 1 to 15% at time 3. The remaining type (26%), termed intermediate, exhibited midrange cohesiveness, low control, and low achievement orientation. The typology at time 1 predicted typologies at time 2 and time 3. There were no age or gender differences among the family types, but offspring, as compared with spouses, were overrepresented in the hostile families. CONCLUSIONS: Family types can be identified, allowing at-risk families to be helped to prevent complications of grief. Screening with the family relationship index of the Family Environment Scale would facilitate such a family-centered approach.

An MML classification of protein structure that knows about angles and sequence.

The MML classification program, Snob, deals with mixture modelling (or clustering) of circular data. It has recently been extended to do Markov modelling of the serial correlation between clusters such as modelling the fact that a Helix cluster favours being followed by another Helix cluster. Such a model is better known as a Hidden Markov Model. The search for the most appropriate secondary structure classification of protein data is of significant importance and was addressed by Hunter and States (1992) using the Bayesian classifier, AutoClass, on Cartesian co-ordinate data of protein residues. Dowe et al. (1996) improved upon this earlier work by using Snob to cluster dihedral angle data, with the advantage that 3 x 3 = 9 Cartesian co-ordinates can be represented by the 2 orientation-invariant angles, phi and psi. The Hidden Markov Model used here is shown to be a more appropriate way again of modelling protein data and results in the selection of a simpler class model with 17 structure classes. We report on this classification, including the class transition matrix, and relate it back to the amino-acid sequence and the simple Helix, Beta, Turn classification. We find 3 types of Helix, 2 types of Beta and many types of Turn. The msot numerous Turn class defines a continuous flexible structure that is negatively correlated to all the other classes.

Discovering simple DNA sequences by compression.

An information-theoretic DNA compression scheme devised by Milosavljevic and Jurka (1993) has been used in many places in the literature for both the discovery of new genes and the compression of DNA. Their compression method applies an encoding of previously occurring runs. They use 5 different code-words: four being the DNA bases, A, C, G and T, and the other being a pointer to a previously occurring run. They advocate a code-word of length log2 5 for each of these and then encoding a run by a code-word of length 2 x log2 n, where n is the length of the sequence. This scheme encodes the start of the sequence with a code-word of length log2 n and likewise encodes the end of the sequence with a code-word of length log2 n. In this paper, we show the above coding scheme to be inefficient in various ways and improve upon it so that it can compress DNA. We discuss our implementation of various schemes some of which run in linear time.

Circular clustering of protein dihedral angles by Minimum Message Length.

Early work on proteins identified the existence of helices and extended sheets in protein secondary structures, a high-level classification which remains popular today. Using the Snob program for information-theoretic Minimum Message Length (MML) classification, we are able to take the protein dihedral angles as determined by X-ray crystallography, and cluster sets of dihedral angles into groups. Previous work by Hunter and States has applied a similar Bayesian classification method, AutoClass, to protein data with site position represented by 3 Cartesian co-ordinates for each of the alpha-Carbon, beta-Carbon and Nitrogen, totalling 9 co-ordinates. By using the von Mises circular distribution in the Snob program, we are instead able to represent local site properties by the two dihedral angles, phi and psi. Since each site can be modelled as having 2 degrees of freedom, this orientation-invariant dihedral angle representation of the data is more compact than that of nine highly-correlated Cartesian co-ordinates. Using the information-theoretic message length concepts discussed in the paper, such a more concise model is more likely to represent the underlying generating process from which the data came. We report on the results of our classification, plotting the classes in (phi, psi) space; and introducing a symmetric information-theoretic distance measure to build a minimum spanning tree between the classes. We also give a transition matrix between the classes and note the existence of three classes in the region phi approximately -1.09 rad and psi approximately -0.75 rad which are close on the spanning tree and have high inter-transition probabilities. This gives rise to a tight, abundant and self-perpetuating structure.