Searching for schizophrenia in ancient Greek and Roman literature: A systematic review

Objective: The aim of this study was to systematically examine ancient Roman and Greek texts to identify descriptions of schizophrenia and related disorders. Method: Material from Greek and Roman literature dating from the 5th Century BC to the beginning of the 2nd Century AD was systematically reviewed for symptoms of mental illness. DSM IV criteria were applied in order to identify material related to schizophrenia and related disorders. Results: The general public had an awareness of psychotic disorders, because the symptoms were described in works of fiction and in historical accounts of malingering. There were isolated instances of text related to psychotic symptoms in the residents of ancient Rome and Greece, but no written material describing a condition that would meet modern diagnostic criteria for schizophrenia. Conclusion: In contrast to many other psychiatric disorders that are represented in ancient Greek and Roman literature, there were no descriptions of individuals with schizophrenia in the material assessed in this review.

Spelling in adults: The combined influences of language skills and reading experience

One hundred and twelve university students completed 7 tests assessing word-reading accuracy, print exposure, phonological sensitivity, phonological coding and knowledge of English morphology as predictors of spelling accuracy. Together the tests accounted for 71% of the variance in spelling, with phonological skills and morphological knowledge emerging as strong predictors of spelling accuracy for words with both regular and irregular sound-spelling correspondences. The pattern of relationships was consistent with a model in which, as a function of the learning opportunities that are provided by reading experience, phonological skills promote the learning of individual word orthographies and structural relationships among words.

Indexing text and visual features for WWW images

In this paper, we present a novel indexing technique called Multi-scale Similarity Indexing (MSI) to index imagersquos multi-features into a single one-dimensional structure. Both for text and visual feature spaces, the similarity between a point and a local partitionrsquos center in individual space is used as the indexing key, where similarity values in different features are distinguished by different scale. Then a single indexing tree can be built on these keys. Based on the property that relevant images haves similar similarity values from the center of the same local partition in any feature space, certain number of irrelevant images can be fast pruned based on the triangle inequity on indexing keys. To remove the ldquodimensionality curserdquo existing in high dimensional structure, we propose a new technique called Local Bit Stream (LBS). LBS transforms imagersquos text and visual feature representations into simple, uniform and effective bit stream (BS) representations based on local partitionrsquos center. Such BS representations are small in size and fast for comparison since only bit operation are involved. By comparing common bits existing in two BSs, most of irrelevant images can be immediately filtered. Our extensive experiment showed that single one-dimensional index on multi-features improves multi-indices on multi-features greatly. Our LBS method outperforms sequential scan on high dimensional space by an order of magnitude.