Long-Term Potentiation: One Kind or Many?

Do neurobiologists aim to discover natural kinds? I address this question in this chapter via a critical analysis of classification practices operative across the 43-year history of research on long-term potentiation (LTP). I argue that this 43-year history supports the idea that the structure of scientific practice surrounding LTP research has remained an obstacle to the discovery of natural kinds

Characterization of psychotic experiences in adolescence using the Specific Psychotic Experiences Questionnaire (SPEQ): findings from a study of 5000 16-year-old twins

We aimed to characterize multiple psychotic experiences, each assessed on a spectrum of severity (ie, quantitatively), in a general population sample of adolescents. Over five thousand 16-year-old twins and their parents completed the newly devised Specific Psychotic Experiences Questionnaire (SPEQ); a subsample repeated it approximately 9 months later. SPEQ was investigated in terms of factor structure, intersubscale correlations, frequency of endorsement and reported distress, reliability and validity, associations with traits of anxiety, depression and personality, and sex differences. Principal component analysis revealed a 6-component solution: paranoia, hallucinations, cognitive disorganization, grandiosity, anhedonia, and parent-rated negative symptoms. These components formed the basis of 6 subscales. Correlations between different experiences were low to moderate. All SPEQ subscales, except Grandiosity, correlated significantly with traits of anxiety, depression, and neuroticism. Scales showed good internal consistency, test-retest reliability, and convergent validity. Girls endorsed more paranoia, hallucinations, and cognitive disorganization; boys reported more grandiosity and anhedonia and had more parent-rated negative symptoms. As in adults at high risk for psychosis and with psychotic disorders, psychotic experiences in adolescents are characterized by multiple components. The study of psychotic experiences as distinct dimensional quantitative traits is likely to prove an important strategy for future research, and the SPEQ is a self- and parent-report questionnaire battery that embodies this approach

Design and manufacturing of complex optics: the dragonfly eye optic.

The ''Design and Manufacturing of Complex Optics'' LDRD sought to develop new advanced methods for the design and manufacturing of very complex optical systems. The project team developed methods for including manufacturability into optical designs and also researched extensions of manufacturing techniques to meet the challenging needs of aspherical, 3D, multi-level lenslet arrays on non-planar surfaces. In order to confirm the applicability of the developed techniques, the team chose the Dragonfly Eye optic as a testbed. This optic has arrays of aspherical micro-lenslets on both the exterior and the interior of a 4mm diameter hemispherical shell. Manufacturing of the dragonfly eye required new methods of plunge milling aspherical optics and the development of a method to create the milling tools using focused ion beam milling. The team showed the ability to create aspherical concave milling tools which will have great significance to the optical industry. A prototype dragonfly eye exterior was created during the research, and the methods of including manufacturability in the optical design process were shown to be successful as well

Criminal and Noncriminal Psychopathy: The Devil is in the Detail

Brooks, NS ORCiD: 0000-0003-1784-099XPsychopathy is prevalent and problematic in criminal populations, but is also found to be present in noncriminal populations. In 1992, Robert Hare declared that psychopaths may also “be found in the boardroom”, which has since been followed by an interest in the issue of noncriminal, or even successful, psychopathy. In this chapter, the paradox of criminal and noncriminal psychopathy is discussed with specific attention given to the similarities and differences that account for psychopathic personality across contexts. That psychopathy is a condition typified by a constellation of traits and behaviours requires wider research across diverse populations, and thus the streams of research related to criminal and noncriminal psychopathy are presented and the implications of these contrasting streams are explored

Computational platform for doctor–artificial intelligence cooperation in pulmonary arterial hypertension prognostication: a pilot study

Background Pulmonary arterial hypertension (PAH) is a heterogeneous and complex pulmonary vascular disease associated with substantial morbidity. Machine-learning algorithms (used in many PAH risk calculators) can combine established parameters with thousands of circulating biomarkers to optimise PAH prognostication, but these approaches do not offer the clinician insight into what parameters drove the prognosis. The approach proposed in this study diverges from other contemporary phenotyping methods by identifying patient-specific parameters driving clinical risk. Methods We trained a random forest algorithm to predict 4-year survival risk in a cohort of 167 adult PAH patients evaluated at Stanford University, with 20% withheld for (internal) validation. Another cohort of 38 patients from Sheffield University were used as a secondary (external) validation. Shapley values, borrowed from game theory, were computed to rank the input parameters based on their importance to the predicted risk score for the entire trained random forest model (global importance) and for an individual patient (local importance). Results Between the internal and external validation cohorts, the random forest model predicted 4-year risk of death/transplant with sensitivity and specificity of 71.0–100% and 81.0–89.0%, respectively. The model reinforced the importance of established prognostic markers, but also identified novel inflammatory biomarkers that predict risk in some PAH patients. Conclusion These results stress the need for advancing individualised phenotyping strategies that integrate clinical and biochemical data with outcome. The computational platform presented in this study offers a critical step towards personalised medicine in which a clinician can interpret an algorithm's assessment of an individual patient

In-Silico Patterning of Vascular Mesenchymal Cells in Three Dimensions

Cells organize in complex three-dimensional patterns by interacting with proteins along with the surrounding extracellular matrix. This organization provides the mechanical and chemical cues that ultimately influence a cell's differentiation and function. Here, we computationally investigate the pattern formation process of vascular mesenchymal cells arising from their interaction with Bone Morphogenic Protein-2 (BMP-2) and its inhibitor, Matrix Gla Protein (MGP). Using a first-principles approach, we derive a reaction-diffusion model based on the biochemical interactions of BMP-2, MGP and cells. Simulations of the model exhibit a wide variety of three-dimensional patterns not observed in a two-dimensional analysis. We demonstrate the emergence of three types of patterns: spheres, tubes, and sheets, and show that the patterns can be tuned by modifying parameters in the model such as the degradation rates of proteins and chemotactic coefficient of cells. Our model may be useful for improved engineering of three-dimensional tissue structures as well as for understanding three dimensional microenvironments in developmental processes.National Institutes of Health (U.S.) (GM69811)United States. Dept. of Energy (DOE CSGF fellowship

Discovery of distinct immune phenotypes using machine learning in pulmonary arterial hypertension

RATIONALE: Accumulating evidence implicates inflammation in pulmonary arterial hypertension (PAH) and therapies targeting immunity are under investigation, though it remains unknown if distinct immune phenotypes exist. OBJECTIVE: Identify PAH immune phenotypes based on unsupervised analysis of blood proteomic profiles. METHODS AND RESULTS: In a prospective observational study of Group 1 PAH patients evaluated at Stanford University (discovery cohort, n=281) and University of Sheffield (validation cohort, n=104) between 2008-2014, we measured a circulating proteomic panel of 48 cytokines, chemokines, and factors using multiplex immunoassay. Unsupervised machine learning (consensus clustering) was applied in both cohorts independently to classify patients into proteomic immune clusters, without guidance from clinical features. To identify central proteins in each cluster, we performed partial correlation network analysis. Clinical characteristics and outcomes were subsequently compared across clusters. Four PAH clusters with distinct proteomic immune profiles were identified in the discovery cohort. Cluster 2 (n=109) had low cytokine levels similar to controls. Other clusters had unique sets of upregulated proteins central to immune networks- cluster 1 (n=58)(TRAIL, CCL5, CCL7, CCL4, MIF), cluster 3 (n=77)(IL-12, IL-17, IL-10, IL-7, VEGF), and cluster 4 (n=37)(IL-8, IL-4, PDGF-β, IL-6, CCL11). Demographics, PAH etiologies, comorbidities, and medications were similar across clusters. Non-invasive and hemodynamic surrogates of clinical risk identified cluster 1 as high-risk and cluster 3 as low-risk groups. Five-year transplant-free survival rates were unfavorable for cluster 1 (47.6%, CI 35.4-64.1%) and favorable for cluster 3 (82.4%, CI 72.0-94.3%)(across-cluster p<0.001). Findings were replicated in the validation cohort, where machine learning classified four immune clusters with comparable proteomic, clinical, and prognostic features. CONCLUSIONS: Blood cytokine profiles distinguish PAH immune phenotypes with differing clinical risk that are independent of World Health Organization Group 1 subtypes. These phenotypes could inform mechanistic studies of disease pathobiology and provide a framework to examine patient responses to emerging therapies targeting immunity

Propofol induces MAPK/ERK cascade dependant expression of cFos and Egr-1 in rat hippocampal slices

Background: Propofol is a commonly used intravenous anesthetic agent, which produce rapid induction of and recovery from general anesthesia. Numerous clinical studies reported that propofol can potentially cause amnesia and memory loss in human subjects. The underlying mechanism for this memory loss is unclear but may potentially be related to the induction of memory-associated genes such as c-Fos and Egr-1 by propofol. This study explored the effects of propofol on c-Fos and Egr-1 expression in rat hippocampal slices. Findings: Hippocampal brain slices were exposed to varying concentrations of propofol at multiple time intervals. The transcription of the immediate early genes, c-Fos and Egr-1, was quantified using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). MAPK/ERK inhibitors were used to investigate the mechanism of action. We demonstrate that propofol induced the expression of c-Fos and Egr-1 within 30 and 60 min of exposure time. At 16.8 μM concentration, propofol induced a 110% increase in c-Fos transcription and 90% decrease in the transcription of Egr-1. However, at concentrations above 100 μM, propofol failed to induce expression of c-Fos but did completely inhibit the transcription of Egr-1. Propofol-induced c-Fos and Egr-1 transcription was abolished by inhibitors of RAS, RAF, MEK, ERK and p38-MAPK in the MAPK/ERK cascade. Conclusions: Our study shows that clinically relevant concentrations of propofol induce c-Fos and down regulated Egr-1 expression via an MAPK/ERK mediated pathway. We demonstrated that propofol induces a time and dose dependant transcription of IEGs c-Fos and Egr-1 in rat hippocampal slices. We further demonstrate for the first time that propofol induced IEG expression was mediated via a MAPK/ERK dependant pathway. These novel findings provide a new avenue to investigate transcription-dependant mechanisms and suggest a parallel pathway of action with an unclear role in the activity of general anesthetics