Chemical Flavorants in Vaping Products Alter Neurobiology in a Sex-Dependent Manner to Promote Vaping-Related Behaviors.

Electronic nicotine delivery systems (ENDS) are distinctly different from combustible cigarettes because of the availability of flavor options. Subjective measures have been used to demonstrate that adults and adolescents prefer flavors for various reasons; (1) they are pleasing and (2) they mask the harshness of nicotine. Despite this, there have been few investigations into the molecular interactions that connect chemical flavorants to smoking or vaping-related behaviors. Here, we investigated the effects of three chemical flavorants (hexyl acetate, ethyl acetate, and methylbutyl acetate) that are found in green apple (GA) ENDS e-liquids but are also found in other flavor categories. We used a translationally relevant vapor self-administration mouse model and observed that adult male and female mice self-administered GA flavorants in the absence of nicotine. Using α4-mCherryα6-GFP nicotinic acetylcholine receptor (nAChR) mice, we observed that mice exposed to GA flavorants exhibited a sex-specific increase (upregulation) of nAChRs that was also brain-region specific. Electrophysiology revealed that mice exposed to GA flavorants exhibited enhanced firing of ventral tegmental area dopamine neurons. Fast-scan cyclic voltammetry revealed that electrically stimulated dopamine release in the nucleus accumbens core is increased in mice that are exposed to GA flavorants. These effects were similarly observed in the medial habenula. Overall, these findings demonstrate that ENDS flavors alone change neurobiology and may promote vaping-dependent behaviors in the absence of nicotine. Furthermore, the flavorant-induced changes in neurobiology parallel those caused by nicotine, which highlights the fact that nonmenthol flavorants may contribute to or enhance nicotine reward and reinforcement.SIGNIFICANCE STATEMENT The impact of flavors on vaping is a hotly debated topic; however, few investigations have examined this in a model that is relevant to vaping. Although a full understanding of the exact mechanism remains undetermined, our observations reveal that chemical flavorants in the absence of nicotine alter brain circuits relevant to vaping-related behavior. The fact that the flavorants investigated here exist in multiple flavor categories of vaping products highlights the fact that a multitude of flavored vaping products may pose a risk toward vaping-dependent behaviors even without the impact of nicotine. Furthermore, as the neurobiological changes have an impact on neurons of the reward system, there exists the possibility that nonmenthol flavorants may enhance nicotine reward and reinforcement.

The Canadian Open Neuroscience Platform-An open science framework for the neuroscience community.

The Canadian Open Neuroscience Platform (CONP) takes a multifaceted approach to enabling open neuroscience, aiming to make research, data, and tools accessible to everyone, with the ultimate objective of accelerating discovery. Its core infrastructure is the CONP Portal, a repository with a decentralized design, where datasets and analysis tools across disparate platforms can be browsed, searched, accessed, and shared in accordance with FAIR principles. Another key piece of CONP infrastructure is NeuroLibre, a preprint server capable of creating and hosting executable and fully reproducible scientific publications that embed text, figures, and code. As part of its holistic approach, the CONP has also constructed frameworks and guidance for ethics and data governance, provided support and developed resources to help train the next generation of neuroscientists, and has fostered and grown an engaged community through outreach and communications. In this manuscript, we provide a high-level overview of this multipronged platform and its vision of lowering the barriers to the practice of open neuroscience and yielding the associated benefits for both individual researchers and the wider community.

The Impact of High or Low Doses of Nicotine in a Mouse Model of Vapor Self-Administration.

INTRODUCTION: A wide variety of nicotine concentrations and formulations are available to users of electronic nicotine delivery systems (ENDS). This is increasingly true when considering the many flavors available with ENDS products. To date, there have been few preclinical investigations into the impact of nicotine doses, with and without flavors, on vaping-related behaviors. This present study evaluated how nicotine concentrations relevant to tank-based and pod-based ENDS, with and without flavors, impact reinforcement-related behavior in a mouse model. AIMS AND METHODS: Adult male and female C57/BL6J mice were used in vapor-inhalation self-administration assays. Mice were assigned e-liquids containing 6 mg/mL or 60 mg/mL nicotine. Additional mice were assigned these nicotine doses with green apple or menthol flavorants. Mice were trained on fixed-ratio 1 for 10, 2-hour sessions, then five sessions at FR3, three progressive ratio sessions, and two FR3 sessions. RESULTS: We observed male mice exhibited higher reinforcement-related behavior to menthol-flavored 6 mg/mL nicotine when compared to female mice. Males were only observed to have a menthol-induced enhancement of self-administration at 6 mg/mL nicotine and not 60 mg/mL nicotine. However, female mice exhibited significant menthol-induced increases in reinforcement-related behaviors with 60 mg/mL nicotine. CONCLUSIONS: These data provide evidence that males and females exhibit different dose sensitivities to nicotine. These sex-dependent differences in nicotine sensitivity also indicate that flavor-induced enhancement in nicotine intake is dependent on the different doses for each sex. IMPLICATIONS: There has been much discussion recently regarding the impact of flavors on vaping-related behavior. Our current study may support prior investigations that suggest flavors enhance the palatability of nicotine-containing products. However, this current study provides evidence that males and females exhibit different sensitivities to nicotine.

Systematic comparisons of different quality control approaches applied to three large pediatric neuroimaging datasets.

INTRODUCTION: Poor quality T1-weighted brain scans systematically affect the calculation of brain measures. Removing the influence of such scans requires identifying and excluding scans with noise and artefacts through a quality control (QC) procedure. While QC is critical for brain imaging analyses, it is not yet clear whether different QC approaches lead to the exclusion of the same participants. Further, the removal of poor-quality scans may unintentionally introduce a sampling bias by excluding the subset of participants who are younger and/or feature greater clinical impairment. This study had two aims: (1) examine whether different QC approaches applied to T1-weighted scans would exclude the same participants, and (2) examine how exclusion of poor-quality scans impacts specific demographic, clinical and brain measure characteristics between excluded and included participants in three large pediatric neuroimaging samples. METHODS: We used T1-weighted, resting-state fMRI, demographic and clinical data from the Province of Ontario Neurodevelopmental Disorders network (Aim 1: n = 553, Aim 2: n = 465), the Healthy Brain Network (Aim 1: n = 1051, Aim 2: n = 558), and the Philadelphia Neurodevelopmental Cohort (Aim 1: n = 1087; Aim 2: n = 619). Four different QC approaches were applied to T1-weighted MRI (visual QC, metric QC, automated QC, fMRI-derived QC). We used tetrachoric correlation and inter-rater reliability analyses to examine whether different QC approaches excluded the same participants. We examined differences in age, mental health symptoms, everyday/adaptive functioning, IQ and structural MRI-derived brain indices between participants that were included versus excluded following each QC approach. RESULTS: Dataset-specific findings revealed mixed results with respect to overlap of QC exclusion. However, in POND and HBN, we found a moderate level of overlap between visual and automated QC approaches (rtet=0.52-0.59). Implementation of QC excluded younger participants, and tended to exclude those with lower IQ, and lower everyday/adaptive functioning scores across several approaches in a dataset-specific manner. Across nearly all datasets and QC approaches examined, excluded participants had lower estimates of cortical thickness and subcortical volume, but this effect did not differ by QC approach. CONCLUSION: The results of this study provide insight into the influence of QC decisions on structural pediatric imaging analyses. While different QC approaches exclude different subsets of participants, the variation of influence of different QC approaches on clinical and brain metrics is minimal in large datasets. Overall, implementation of QC tends to exclude participants who are younger, and those who have more cognitive and functional impairment. Given that automated QC is standardized and can reduce between-study differences, the results of this study support the potential to use automated QC for large pediatric neuroimaging datasets.

Symptom dimensions of major depression in a large community-based cohort.

BACKGROUND: Our understanding of major depression is complicated by substantial heterogeneity in disease presentation, which can be disentangled by data-driven analyses of depressive symptom dimensions. We aimed to determine the clinical portrait of such symptom dimensions among individuals in the community. METHODS: This cross-sectional study consisted of 25 261 self-reported White UK Biobank participants with major depression. Nine questions from the UK Biobank Mental Health Questionnaire encompassing depressive symptoms were decomposed into underlying factors or 'symptom dimensions' via factor analysis, which were then tested for association with psychiatric diagnoses and polygenic risk scores for major depressive disorder (MDD), bipolar disorder and schizophrenia. Replication was performed among 655 self-reported non-White participants, across sexes, and among 7190 individuals with an ICD-10 code for MDD from linked inpatient or primary care records. RESULTS: Four broad symptom dimensions were identified, encompassing negative cognition, functional impairment, insomnia and atypical symptoms. These dimensions replicated across ancestries, sexes and individuals with inpatient or primary care MDD diagnoses, and were also consistent among 43 090 self-reported White participants with undiagnosed self-reported depression. Every dimension was associated with increased risk of nearly every psychiatric diagnosis and polygenic risk score. However, while certain psychiatric diagnoses were disproportionately associated with specific symptom dimensions, the three polygenic risk scores did not show the same specificity of associations. CONCLUSIONS: An analysis of questionnaire data from a large community-based cohort reveals four replicable symptom dimensions of depression with distinct clinical, but not genetic, correlates.

Machine learning applications in tobacco research: a scoping review.

OBJECTIVE: Identify and review the body of tobacco research literature that self-identified as using machine learning (ML) in the analysis. DATA SOURCES: MEDLINE, EMABSE, PubMed, CINAHL Plus, APA PsycINFO and IEEE Xplore databases were searched up to September 2020. Studies were restricted to peer-reviewed, English-language journal articles, dissertations and conference papers comprising an empirical analysis where ML was identified to be the method used to examine human experience of tobacco. Studies of genomics and diagnostic imaging were excluded. STUDY SELECTION: Two reviewers independently screened the titles and abstracts. The reference list of articles was also searched. In an iterative process, eligible studies were classified into domains based on their objectives and types of data used in the analysis. DATA EXTRACTION: Using data charting forms, two reviewers independently extracted data from all studies. A narrative synthesis method was used to describe findings from each domain such as study design, objective, ML classes/algorithms, knowledge users and the presence of a data sharing statement. Trends of publication were visually depicted. DATA SYNTHESIS: 74 studies were grouped into four domains: ML-powered technology to assist smoking cessation (n=22); content analysis of tobacco on social media (n=32); smoker status classification from narrative clinical texts (n=6) and tobacco-related outcome prediction using administrative, survey or clinical trial data (n=14). Implications of these studies and future directions for ML researchers in tobacco control were discussed. CONCLUSIONS: ML represents a powerful tool that could advance the research and policy decision-making of tobacco control. Further opportunities should be explored.

GABA-mediated tonic inhibition differentially modulates gain in functional subtypes of cortical interneurons.

The binding of GABA (γ-aminobutyric acid) to extrasynaptic GABAA receptors generates tonic inhibition that acts as a powerful modulator of cortical network activity. Despite GABA being present throughout the extracellular space of the brain, previous work has shown that GABA may differentially modulate the excitability of neuron subtypes according to variation in chloride gradient. Here, using biophysically detailed neuron models, we predict that tonic inhibition can differentially modulate the excitability of neuron subtypes according to variation in electrophysiological properties. Surprisingly, tonic inhibition increased the responsiveness (or gain) in models with features typical for somatostatin interneurons but decreased gain in models with features typical for parvalbumin interneurons. Patch-clamp recordings from cortical interneurons supported these predictions, and further in silico analysis was then performed to seek a putative mechanism underlying gain modulation. We found that gain modulation in models was dependent upon the magnitude of tonic current generated at depolarized membrane potential-a property associated with outward rectifying GABAA receptors. Furthermore, tonic inhibition produced two biophysical changes in models of relevance to neuronal excitability: 1) enhanced action potential repolarization via increased current flow into the dendritic compartment, and 2) reduced activation of voltage-dependent potassium channels. Finally, we show theoretically that reduced potassium channel activation selectively increases gain in models possessing action potential dynamics typical for somatostatin interneurons. Potassium channels in parvalbumin-type models deactivate rapidly and are unavailable for further modulation. These findings show that GABA can differentially modulate interneuron excitability and suggest a mechanism through which this occurs in silico via differences of intrinsic electrophysiological properties.

High-dose chemotherapy for relapsed germ cell tumours: outcomes in low-volume specialized centres.

OBJECTIVE: To report treatment patterns and survival outcomes of patients with relapsed and refractory metastatic germ cell tumours (GCTs) treated with high-dose chemotherapy (HDCT) and autologous stem-cell transplantation in low-volume specialized centres within the widely dispersed populations of Australia and New Zealand between 1999 and 2019. PATIENTS AND METHODS: We conducted a retrospective analysis of 111 patients across 13 institutions. Patients were identified from the Australasian Bone Marrow Transplant Recipient Registry. We reviewed treatment regimens, survival outcomes, deliverability and toxicities. Primary endpoints included overall (OS) and progression-free survival (PFS). Cox proportional hazards models were used to test the association of survival outcomes with patient and treatment factors. RESULTS: The median (range) age was 30 (14-68) years and GCT histology was non-seminomatous in 84% of patients. International Prognostic Factors Study Group (IPFSG) prognostic risk category was very low/low, intermediate, high and very high in 18%, 36%, 25% and 21% of patients, respectively. Salvage conventional-dose chemotherapy (CDCT) was administered prior to HDCT in 59% of patients. Regimens included paclitaxel, ifosfamide, carboplatin and etoposide (50%), carboplatin and etoposide (CE; 28%), carboplatin, etoposide and ifosfamide (CEI; 6%), carboplatin, etoposide and cyclophosphamide (CEC; 5%), CEC-paclitaxel (6%) and other (5%). With a median follow-up of 4.4 years, the 1-, 2- and 5-year PFS rates were 62%, 57% and 52%, respectively, and OS rates were 73%, 65% and 61%, respectively. There were five treatment-related deaths. Progression on treatment occurred in 17%. In a univariable analysis, worse International Germ Cell Cancer Collaborative Group (IGCCCG) and IPFSG prognostic groups were associated with inferior survival outcomes. An association of inferior survival was not found with the number of high-dose cycles received nor when HDCT was delivered after salvage CDCT. CONCLUSION: This large dual-national registry-based study reinforces the efficacy and deliverability of HDCT for relapsed and refractory metastatic GCT in low-volume specialized centres in Australia and New Zealand, with survival outcomes comparable to those found in international practice.

Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank.

BACKGROUND: Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort. METHODS AND FINDINGS: In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures-bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration-were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was ß = -0.11 (95% confidence interval -0.13 to -0.10, p = 3 × 10-56, FDR = 6 × 10-55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry. CONCLUSIONS: In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.

Digitization of Measurement-Based Care Pathways in Mental Health Through REDCap and Electronic Health Record Integration: Development and Usability Study.

BACKGROUND: The delivery of standardized self-report assessments is essential for measurement-based care in mental health. Paper-based methods of measurement-based care data collection may result in transcription errors, missing data, and other data quality issues when entered into patient electronic health records (EHRs). OBJECTIVE: This study aims to help address these issues by using a dedicated instance of REDCap (Research Electronic Data Capture; Vanderbilt University)-a free, widely used electronic data capture platform-that was established to enable the deployment of digitized self-assessments in clinical care pathways to inform clinical decision making. METHODS: REDCap was integrated with the primary clinical information system to facilitate the real-time transfer of discrete data and PDF reports from REDCap into the EHR. Both technical and administrative components were required for complete implementation. A technology acceptance survey was also administered to capture physicians' and clinicians' attitudes toward the new system. RESULTS: The integration of REDCap with the EHR transitioned clinical workflows from paper-based methods of data collection to electronic data collection. This resulted in significant time savings, improved data quality, and valuable real-time information delivery. The digitization of self-report assessments at each appointment contributed to the clinic-wide implementation of the major depressive disorder integrated care pathway. This digital transformation facilitated a 4-fold increase in the physician adoption of this integrated care pathway workflow and a 3-fold increase in patient enrollment, resulting in an overall significant increase in major depressive disorder integrated care pathway capacity. Physicians' and clinicians' attitudes were overall positive, with almost all respondents agreeing that the system was useful to their work. CONCLUSIONS: REDCap provided an intuitive patient interface for collecting self-report measures and accessing results in real time to inform clinical decisions and an extensible backend for system integration. The approach scaled effectively and expanded to high-impact clinics throughout the hospital, allowing for the broad deployment of complex workflows and standardized assessments, which led to the accumulation of harmonized data across clinics and care pathways. REDCap is a flexible tool that can be effectively leveraged to facilitate the automatic transfer of self-report data to the EHR; however, thoughtful governance is required to complement the technical implementation to ensure that data standardization, data quality, patient safety, and privacy are maintained.

SCN1A gain of function in early infantile encephalopathy.

OBJECTIVE: To elucidate the biophysical basis underlying the distinct and severe clinical presentation in patients with the recurrent missense SCN1A variant, p.Thr226Met. Patients with this variant show a well-defined genotype-phenotype correlation and present with developmental and early infantile epileptic encephalopathy that is far more severe than typical SCN1A Dravet syndrome. METHODS: Whole cell patch clamp and dynamic action potential clamp were used to study T226M Nav 1.1 channels expressed in mammalian cells. Computational modeling was used to explore the neuronal scale mechanisms that account for altered action potential firing. RESULTS: T226M channels exhibited hyperpolarizing shifts of the activation and inactivation curves and enhanced fast inactivation. Dynamic action potential clamp hybrid simulation showed that model neurons containing T226M conductance displayed a left shift in rheobase relative to control. At current stimulation levels that produced repetitive action potential firing in control model neurons, depolarization block and cessation of action potential firing occurred in T226M model neurons. Fully computationally simulated neuron models recapitulated the findings from dynamic action potential clamp and showed that heterozygous T226M models were also more susceptible to depolarization block. INTERPRETATION: From a biophysical perspective, the T226M mutation produces gain of function. Somewhat paradoxically, our data suggest that this gain of function would cause interneurons to more readily develop depolarization block. This "functional dominant negative" interaction would produce a more profound disinhibition than seen with haploinsufficiency that is typical of Dravet syndrome and could readily explain the more severe phenotype of patients with T226M mutation. Ann Neurol 2019;85:514-525.

Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons.

Somatosensory thalamocortical (TC) neurons from the ventrobasal (VB) thalamus are central components in the flow of sensory information between the periphery and the cerebral cortex, and participate in the dynamic regulation of thalamocortical states including wakefulness and sleep. This property is reflected at the cellular level by the ability to generate action potentials in two distinct firing modes, called tonic firing and low-threshold bursting. Although the general properties of TC neurons are known, we still lack a detailed characterization of their morphological and electrical properties in the VB thalamus. The aim of this study was to build biophysically-detailed models of VB TC neurons explicitly constrained with experimental data from rats. We recorded the electrical activity of VB neurons (N = 49) and reconstructed morphologies in 3D (N = 50) by applying standardized protocols. After identifying distinct electrical types, we used a multi-objective optimization to fit single neuron electrical models (e-models), which yielded multiple solutions consistent with the experimental data. The models were tested for generalization using electrical stimuli and neuron morphologies not used during fitting. A local sensitivity analysis revealed that the e-models are robust to small parameter changes and that all the parameters were constrained by one or more features. The e-models, when tested in combination with different morphologies, showed that the electrical behavior is substantially preserved when changing dendritic structure and that the e-models were not overfit to a specific morphology. The models and their analysis show that automatic parameter search can be applied to capture complex firing behavior, such as co-existence of tonic firing and low-threshold bursting over a wide range of parameter sets and in combination with different neuron morphologies.

Multimolecular assemblies on high surface area metal oxides and their role in interfacial energy and electron transfer.

High surface area metal oxides offer a unique substrate for the assembly of multiple molecular components at an interface. The choice of molecules, metal oxide, and the nature of the assembly method can have a profound influence on the mechanism, rate, and efficiency of photoinduced energy and electron transfer events at the interface. Owing to their diversity and high level of control, these interfacial assemblies are of interest for numerous applications including solar energy conversion, photoelectrosynthesis, photo-writable memory, and more. Although these assemblies are generated with very different goals in mind, they rely on similar surface binding motifs and molecular structure-property relationships. Therefore, the goal of this review is to summarize the various strategies (i.e. co-deposition, axial coordination, metal ion linkages, electrostatics, host-guest interactions, etc.) for assembling chromophores, hosts, electron donors/acceptors, and insulating co-adsorbent molecules on mesoporous metal oxide substrates. The assembly, synthesis, and characterization, as well as subsequent photoinduced events (i.e. cross-surface energy/electron transfer, interchromophore energy transfer, electron injection, and others) are discussed for the various assembly strategies.

Current Trends in Breast Augmentation: Analysis of 2011-2015 Maintenance of Certification (MOC) Tracer Data.

BACKGROUND: Breast augmentation is the most common aesthetic surgery performed in the United States. Despite its popularity, there is no consensus on many aspects of the procedure. OBJECTIVES: The authors assessed current trends and changes in breast augmentation from January 1, 2011 to December 31, 2015. METHODS: A retrospective cross-sectional study of 11,756 women who underwent breast augmentation based on the American Board of Plastic Surgery (ABPS) Maintenance of Certification Tracer Database was performed. RESULTS: There were clearly dominant trends in how ABPS-certified plastic surgeons performed breast augmentations. Most surgeries were performed in freestanding outpatient (47.3%) or office operating room (33.7%). The inframammary fold incision was most popular (75.1%), followed by periareolar (17.8%) and transaxillary approaches (4.1%). Implants were more commonly placed in a submuscular pocket (30.6%) compared with dual plane (26.7%) or subglandular (6.7%). Silicone implants (66.8%) were favored over saline (25.1%), with a statistically significant increase in silicone prostheses from 2011 to 2015. Data were "not applicable" or "other" in the remainder of cases. Administration of both preoperative antibiotics (3.8% in 2011, 98.7% in 2015, P < 0.05) and deep venous thromboembolism (DVT) prophylaxis (3.8% in 2011, 90.6% in 2015, P < 0.05) dramatically increased during the study period. Overall adverse events (7.4%) and reoperation rates (2.2%) were low. CONCLUSIONS: Changes in standard of care for breast augmentation are reflected by the evolving practice patterns of plastic surgeons. This is best evidenced by the dramatic increase in use of antibiotic and DVT prophylaxis from 2011 to 2015.

Towards a supervised classification of neocortical interneuron morphologies.

BACKGROUND: The challenge of classifying cortical interneurons is yet to be solved. Data-driven classification into established morphological types may provide insight and practical value. RESULTS: We trained models using 217 high-quality morphologies of rat somatosensory neocortex interneurons reconstructed by a single laboratory and pre-classified into eight types. We quantified 103 axonal and dendritic morphometrics, including novel ones that capture features such as arbor orientation, extent in layer one, and dendritic polarity. We trained a one-versus-rest classifier for each type, combining well-known supervised classification algorithms with feature selection and over- and under-sampling. We accurately classified the nest basket, Martinotti, and basket cell types with the Martinotti model outperforming 39 out of 42 leading neuroscientists. We had moderate accuracy for the double bouquet, small and large basket types, and limited accuracy for the chandelier and bitufted types. We characterized the types with interpretable models or with up to ten morphometrics. CONCLUSION: Except for large basket, 50 high-quality reconstructions sufficed to learn an accurate model of a type. Improving these models may require quantifying complex arborization patterns and finding correlates of bouton-related features. Our study brings attention to practical aspects important for neuron classification and is readily reproducible, with all code and data available online.

New insights into the classification and nomenclature of cortical GABAergic interneurons.

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus.

Examining the role of acceptor molecule structure in self-assembled bilayers: surface loading, stability, energy transfer, and upconverted emission.

Self-assembly of sensitizer and acceptor molecules has recently emerged as a promising strategy to facilitate and harness photon upconversion via triplet-triplet annihilation (TTA-UC). In addition to the energetic requirements, the structure and relative orientation of these molecules can have a strong influence on TTA-UC rates and efficiency. Here we report the synthesis of five different acceptor molecules composed of an anthracene core functionalized with 9,10- or 2,6-phenyl, methyl, or directly bound phosphonic acid groups and their incorporation into self-assembled bilayers on a ZrO2 surface. All five films facilitate green-to-blue photon upconversion with Φuc as high as 0.0023. The efficiency of TTA, and not triplet energy transfer, fluorescence, or losses via FRET, was primarily responsible for dictating the Φuc emission. Even for molecules having similar photophysical properties, variation in the position of the phosphonic acid resulted in dramatically different ΦTTA, Ith values, γTTA, and D. Interestingly, we observed a strong linear correlation between ΦTTA and the Ith value but the cause of this relationship, if any, is unclear.

Harnessing Molecular Photon Upconversion in a Solar Cell at Sub-solar Irradiance: Role of the Redox Mediator.

Self-assembled bilayers offer a promising strategy to directly harness photon upconversion via triplet-triplet annihilation (TTA-UC) and increase maximum theoretical solar cell efficiencies from 33% to >43%. Here we demonstrate that the choice of redox mediator in these solar cells has a profound influence on both the light harvesting and TTA-UC efficiency. Devices with CoII/III(phen)3 as the redox mediator produced the highest photocurrent yet generated from TTA-UC (0.158 mA cm-2) under 1 sun. Despite generating less photocurrent, CoII/III(pz-py-pz)2 devices achieved maximum TTA-UC efficiency at excitation intensities well below solar irradiance (0.8 mW cm-2), which is on par with the lowest value yet reported for any TTA-UC system. The large variation in performance with respect to mediator is attributed to triplet excited-state quenching via (1) energy transfer or paramagnetic quenching by the CoII species and (2) excited-state electron transfer to CoIII species.

Coupling N-H Deprotonation, C-H Activation, and Oxidation: Metal-Free C(sp3)-H Aminations with Unprotected Anilines.

An intramolecular oxidative C(sp3)-H amination from unprotected anilines and C(sp3)-H bonds readily occurs under mild conditions using t-BuOK, molecular oxygen and N,N-dimethylformamide (DMF). Success of this process, which requires mildly acidic N-H bonds and an activated C(sp3)-H bond (BDE < 85 kcal/mol), stems from synergy between basic, radical, and oxidizing species working together to promote a coordinated sequence of deprotonation: H atom transfer and oxidation that forges a new C-N bond. This process is applicable for the synthesis of a wide variety of N-heterocycles, ranging from small molecules to extended aromatics without the need for transition metals or strong oxidants. Computational results reveal the mechanistic details and energy landscape for the sequence of individual steps that comprise this reaction cascade. The importance of base in this process stems from the much greater acidity of transition state and product for the 2c,3e C-N bond formation relative to the reactant. In this scenario, selective deprotonation provides the driving force for the process.

Large-scale extraction of brain connectivity from the neuroscientific literature.

MOTIVATION: In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles. One challenge for modern neuroinformatics is finding methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and the integration of such data into computational models. A key example of this is metascale brain connectivity, where results are not reported in a normalized repository. Instead, these experimental results are published in natural language, scattered among individual scientific publications. This lack of normalization and centralization hinders the large-scale integration of brain connectivity results. In this article, we present text-mining models to extract and aggregate brain connectivity results from 13.2 million PubMed abstracts and 630 216 full-text publications related to neuroscience. The brain regions are identified with three different named entity recognizers (NERs) and then normalized against two atlases: the Allen Brain Atlas (ABA) and the atlas from the Brain Architecture Management System (BAMS). We then use three different extractors to assess inter-region connectivity. RESULTS: NERs and connectivity extractors are evaluated against a manually annotated corpus. The complete in litero extraction models are also evaluated against in vivo connectivity data from ABA with an estimated precision of 78%. The resulting database contains over 4 million brain region mentions and over 100 000 (ABA) and 122 000 (BAMS) potential brain region connections. This database drastically accelerates connectivity literature review, by providing a centralized repository of connectivity data to neuroscientists.