Machine learning in the prediction of postpartum depression: A review.

Background: Current screening options in the setting of postpartum depression (PPD) are firmly rooted in self-report symptom-based tools. The implementation of the modern machine learning (ML) approaches might, in this context, represent a way to refine patient screening by precisely identifying possible PPD predictors and, subsequently, a population at risk of developing the disease, in an effort to lower its morbidity, mortality and its economic burden.

Methods: We performed a bibliographic search on PubMed and Embase looking for studies aimed at the identification of PPD predictors using ML techniques.

Lexical processing and distributional knowledge in sound-spelling mapping in a consistent orthography: A longitudinal study of reading and spelling in dyslexic and typically developing children.

This study examined the ability to master lexical processing and use knowledge of the relative frequency of sound-spelling mappings in both reading and spelling. Twenty-four dyslexic and dysgraphic children and 86 typically developing readers were followed longitudinally in 3rd and 5th grades. Effects of word regularity, word frequency, and probability of sound-spelling mappings were examined in two experimental tasks: (a) spelling to dictation; and (b) orthographic judgment.

Transformed Toxoplasma gondii tachyzoites expressing the circumsporozoite protein of Plasmodium knowlesi elicit a specific immune response in rhesus monkeys.

Toxoplasma gondii tachyzoites were transformed with the coding sequence of the circumsporozoite (CS) protein of the primate malaria parasite Plasmodium knowlesi. A single inoculation of live transformed tachyzoites elicited an antibody response directed against the immunodominant repeat epitope (EQPAAGAGG)2 of the P. knowlesi CS protein in rhesus monkeys.