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RATIONALE: Fragile X syndrome (FXS) is the most common form of inherited intellectual disability (ID) and the leading monogenic cause of autism spectrum disorder (ASD). Serotonergic neurotransmission has a key role in the modulation of neuronal activity during development, and therefore, it has been hypothesized to be involved in ASD and co-occurring conditions including FXS. As serotonin is involved in synaptic remodeling and maturation, serotonergic insufficiency during childhood may have a compounding effect on brain patterning in neurodevelopmental disorders, manifesting as behavioral and emotional symptoms. Thus, compounds that stimulate serotonergic signaling such as psilocybin may offer promise as effective early interventions for developmental disorders such as ASD and FXS. OBJECTIVES: The aim of the present study was to test whether different protocols of psilocybin administration mitigate cognitive deficits displayed by the recently validated Fmr1-Δexon 8 rat model of ASD, which is also a model of FXS. RESULTS: Our results revealed that systemic and oral administration of psilocybin microdoses normalizes the aberrant cognitive performance displayed by adolescent Fmr1-Δexon 8 rats in the short-term version of the novel object recognition test-a measure of exploratory behavior, perception, and recognition. CONCLUSIONS: These data support the hypothesis that serotonin-modulating drugs such as psilocybin may be useful to ameliorate ASD-related cognitive deficits. Overall, this study provides evidence of the beneficial effects of different schedules of psilocybin treatment in mitigating the short-term cognitive deficit observed in a rat model of FXS.

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The frequency and volume of newly-published scientific literature is quickly making manual maintenance of publicly-available databases of primary data unrealistic and costly. Although machine learning (ML) can be useful for developing automated approaches to identifying scientific publications containing relevant information for a database, developing such tools necessitates manually annotating an unrealistic number of documents. One approach to this problem, active learning (AL), builds classification models by iteratively identifying documents that provide the most information to a classifier. Although this approach has been shown to be effective for related problems, in the context of scientific databases curation, it falls short. We present Virk, an AL system that, while being trained, simultaneously learns a classification model and identifies documents having information of interest for a knowledge base. Our approach uses a support vector machine (SVM) classifier with input features derived from neuroscience-related publications from the primary literature. Using our approach, we were able to increase the size of the Neuron Registry, a knowledge base of neuron-related information, by a factor of 90%, a knowledge base of neuron-related information, in 3 months. Using standard biocuration methods, it would have taken between 1 and 2 years to make the same number of contributions to the Neuron Registry. Here, we describe the system pipeline in detail, and evaluate its performance against other approaches to sampling in AL.

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The wealth and diversity of neuroscience research are inherent characteristics of the discipline that can give rise to some complications. As the field continues to expand, we generate a great deal of data about all aspects, and from multiple perspectives, of the brain, its chemistry, biology, and how these affect behavior. The vast majority of research scientists cannot afford to spend their time combing the literature to find every article related to their research, nor do they wish to spend time adjusting their neuroanatomical vocabulary to communicate with other subdomains in the neurosciences. As such, there has been a recent increase in the amount of informatics research devoted to developing digital resources for neuroscience research. Neuroinformatics is concerned with the development of computational tools to further our understanding of the brain and to make sense of the vast amount of information that neuroscientists generate (French & Pavlidis, 2007). Many of these tools are related to the use of textual data. Here, we review some of the recent developments for better using the vast amount of textual information generated in neuroscience research and publication and suggest several use cases that will demonstrate how bench neuroscientists can take advantage of the resources that are available.

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Although publicly accessible databases containing protein-protein interaction (PPI)-related information are important resources to bench and in silico research scientists alike, the amount of time and effort required to keep them up to date is often burdonsome. In an effort to help identify relevant PPI publications, text-mining tools, from the machine learning discipline, can be applied to help in this process. Here, we describe and evaluate two document classification algorithms that we submitted to the BioCreative II.5 PPI Classification Challenge Task. This task asked participants to design classifiers for identifying documents containing PPI-related information in the primary literature, and evaluated them against one another. One of our systems was the overall best-performing system submitted to the challenge task. It utilizes a novel approach to k-nearest neighbor classification, which we describe here, and compare its performance to those of two support vector machine-based classification systems, one of which was also evaluated in the challenge task.

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OBJECTIVE Free-text clinical reports serve as an important part of patient care management and clinical documentation of patient disease and treatment status. Free-text notes are commonplace in medical practice, but remain an under-used source of information for clinical and epidemiological research, as well as personalized medicine. The authors explore the challenges associated with automatically extracting information from clinical reports using their submission to the Integrating Informatics with Biology and the Bedside (i2b2) 2008 Natural Language Processing Obesity Challenge Task. DESIGN A text mining system for classifying patient comorbidity status, based on the information contained in clinical reports. The approach of the authors incorporates a variety of automated techniques, including hot-spot filtering, negated concept identification, zero-vector filtering, weighting by inverse class-frequency, and error-correcting of output codes with linear support vector machines. MEASUREMENTS Performance was evaluated in terms of the macroaveraged F1 measure. RESULTS The automated system performed well against manual expert rule-based systems, finishing fifth in the Challenge's intuitive task, and 13(th) in the textual task. CONCLUSIONS The system demonstrates that effective comorbidity status classification by an automated system is possible.

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