Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders.

Computerized natural language processing (NLP) allows for objective and sensitive detection of speech disturbance, a hallmark of schizophrenia spectrum disorders (SSD). We explored several methods for characterizing speech changes in SSD (n = 20) compared to healthy control (HC) participants (n = 11) and approached linguistic phenotyping on three levels: individual words, parts-of-speech (POS), and sentence-level coherence. NLP features were compared with a clinical gold standard, the Scale for the Assessment of Thought, Language and Communication (TLC). We utilized Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art embedding algorithm incorporating bidirectional context. Through the POS approach, we found that SSD used more pronouns but fewer adverbs, adjectives, and determiners (e.g., "the," "a,"). Analysis of individual word usage was notable for more frequent use of first-person singular pronouns among individuals with SSD and first-person plural pronouns among HC. There was a striking increase in incomplete words among SSD. Sentence-level analysis using BERT reflected increased tangentiality among SSD with greater sentence embedding distances. The SSD sample had low speech disturbance on average and there was no difference in group means for TLC scores. However, NLP measures of language disturbance appear to be sensitive to these subclinical differences and showed greater ability to discriminate between HC and SSD than a model based on clinical ratings alone. These intriguing exploratory results from a small sample prompt further inquiry into NLP methods for characterizing language disturbance in SSD and suggest that NLP measures may yield clinically relevant and informative biomarkers.

Account and Utilization of Blood Lipid Profiles: Lipid Levels Predicted Hemosporidian Infection in Migrating Northern Saw-Whet Owls of Eastern North America.

Noting lipidomic changes following the parasitism of migrating birds, the metabolic needs of which are primarily fueled by lipids, can deepen our understanding of host-parasite interactions. We identified lipids of migrating Northern saw-whet owls (Aegolius acadicus) using collision-induced dissociation mass spectrometry, compared the lipidomic signatures of hemoparasite-infected and noninfected individuals, and performed cross-validation analyses to reveal associations between parasite infection and lipid levels. We found significantly lower levels of lipid classes phosphatidic acid (PA), phosphatidylglycerol (PG), phosphatidylinositol (PI), phosphatidylserine (PS), phosphatidylethanolamine (PE), phosphatidylcholine (PC), and sphingomyelin (SM) in infected Northern saw-whet owls than in the noninfected individuals. Conversely, we found higher levels for certain lysoPS and lysoPE species, and variable lipid level changes for free fatty acid (FFA) species. Reporting lipidomic changes observed between hemosporidian-infected and noninfected Northern saw-whet owls can strengthen our understanding of the mechanisms governing parasite proliferation in this species. Furthermore, our analysis indicated that lipidomic signatures are better predictors of parasite infection than the log-adjusted mass/wing chord body index, a metric commonly used to assess the influence of hemosporidia infection on the health of birds. Establishing a lipidomic profile for Northern saw-whet owls that provides baseline lipid levels during fall migration may assist future studies assessing causes of reductions in breeding brought about from subtle differences in behaviors such as delayed migration.