Sensory regulation of dopaminergic cell activity: Phenomenology, circuitry and function

Dopaminergic neurons in a range of species are responsive to sensory stimuli. In the anesthetized preparation, responses to non-noxious and noxious sensory stimuli are usually tonic in nature, although long-duration changes in activity have been reported in the awake preparation as well. However, in the awake preparation, short-latency, phasic changes in activity are most common. These phasic responses can occur to unconditioned aversive and non-aversive stimuli, as well as to the stimuli which predict them. In both the anesthetized and awake preparations, not all dopaminergic neurons are responsive to sensory stimuli, however responsive neurons tend to respond to more than a single stimulus modality. Evidence suggests that short-latency sensory information is provided to dopaminergic neurons by relatively primitive subcortical structures – including the midbrain superior colliculus for vision and the mesopontine parabrachial nucleus for pain and possibly gustation. Although short-latency visual information is provided to dopaminergic neurons by the relatively primitive colliculus, dopaminergic neurons can discriminate between complex visual stimuli, an apparent paradox which can be resolved by the recently discovered route of information flow through to dopaminergic neurons from the cerebral cortex, via a relay in the colliculus. Given that projections from the cortex to the colliculus are extensive, such a relay potentially allows the activity of dopaminergic neurons to report the results of complex stimulus processing from widespread areas of the cortex. Furthermore, dopaminergic neurons could acquire their ability to reflect stimulus value by virtue of reward-related modification of sensory processing in the cortex. At the forebrain level, sensory-related changes in the tonic activity of dopaminergic neurons may regulate the impact of the cortex on forebrain structures such as the nucleus accumbens. In contrast, the short latency of the phasic responses to sensory stimuli in dopaminergic neurons, coupled with the activation of these neurons by non-rewarding stimuli, suggests that phasic responses of dopaminergic neurons may provide a signal to the forebrain which indicates that a salient event has occurred (and possibly an estimate of how salient that event is). A stimulus-related salience signal could be used by downstream systems to reinforce behavioral choices

European aerosol phenomenology - 8 : Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets

Organic aerosol (OA) is a key component of total submicron particulate matter (PM1), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM1 levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013-2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30-240 min) and long-term data coverage (9-36 months), providing essential information to improve/validate air quality, health impact, and climate models.Peer reviewe

Identity-specific coding of future rewards in the human orbitofrontal cortex

Nervous systems must encode information about the identity of expected outcomes to make adaptive decisions. However, the neural mechanisms underlying identity-specific value signaling remain poorly understood. By manipulating the value and identity of appetizing food odors in a pattern-based imaging paradigm of human classical conditioning, we were able to identify dissociable predictive representations of identity-specific reward in orbitofrontal cortex (OFC) and identity-general reward in ventromedial prefrontal cortex (vmPFC). Reward-related functional coupling between OFC and olfactory (piriform) cortex and between vmPFC and amygdala revealed parallel pathways that support identity-specific and -general predictive signaling. The demonstration of identity-specific value representations in OFC highlights a role for this region in model-based behavior and reveals mechanisms by which appetitive behavior can go awry

Real-Time Source Apportionment of Organic Aerosols in Three European Cities

97% of the urban population in the EU in 2019 were exposed to an annual fine particulate matter level higher than the World Health Organization (WHO) guidelines (5 μg/m3). Organic aerosol (OA) is one of the major air pollutants, and the knowledge of its sources is crucial for designing cost-effective mitigation strategies. Positive matrix factorization (PMF) on aerosol mass spectrometer (AMS) or aerosol chemical speciation monitor (ACSM) data is the most common method for source apportionment (SA) analysis on ambient OA. However, conventional PMF requires extensive human labor, preventing the implementation of SA for routine monitoring applications. This study proposes the source finder real-time (SoFi RT, Datalystica Ltd.) approach for efficient retrieval of OA sources. The results generated by SoFi RT agree remarkably well with the conventional rolling PMF results regarding factor profiles, time series, diurnal patterns, and yearly relative contributions of OA factor on three year-long ACSM data sets collected in Athens, Paris, and Zurich. Although the initialization of SoFi RT requires a priori knowledge of OA sources (i.e., the approximate number of factors and relevant factor profiles) for the sampling site, this technique minimizes user interactions. Eventually, it could provide up-to-date trustable information on timescales useful to policymakers and air quality modelers.ISSN:0013-936XISSN:1520-585

Real-time source apportionment of organic aerosols in three European cities

International audienceAtmospheric particulate matter (PM) has direct and indirect effects on public health, ecosystems and climate (IPCC, 2014). At the same time, European countries are still suffering from poor air quality: 70% of air quality monitoring stations within Europe exceed the annual PM2.5 value of the WHO guidelines (10 μg/m3) (European Environment Agency (EEA), 2020). Considering organic aerosol (OA) is one of the major components of PM (Jimenez et al., 2009), the knowledge of OA sources is extremely valuable for policymakers in order to design effective mitigation strategies

A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data

A new methodology for performing long-term source apportionment (SA) using positive matrix factorization (PMF) is presented. The method is implemented within the SoFi Pro software package and uses the multilinear engine (ME-2) as a PMF solver. The technique is applied to a 1-year aerosol chemical speciation monitor (ACSM) dataset from downtown Zurich, Switzerland. The measured organic aerosol mass spectra were analyzed by PMF using a small (14 d) and rolling PMF window to account for the temporal evolution of the sources. The rotational ambiguity is explored and the uncertainties of the PMF solutions were estimated. Factor-tracer correlations for averaged seasonal results from the rolling window analysis are higher than those retrieved from conventional PMF analyses of individual seasons, highlighting the improved performance of the rolling window algorithm for long-term data. In this study four to five factors were tested for every PMF window. Factor profiles for primary organic aerosol from traffic (HOA), cooking (COA) and biomass burning (BBOA) were constrained. Secondary organic aerosol was represented by either the combination of semi-volatile and low-volatility organic aerosol (SV-OOA and LV-OOA, respectively) or by a single OOA when this separation was not robust. This scheme led to roughly 40 000 PMF runs. Full visual inspection of all these PMF runs is unrealistic and is replaced by predefined user-selected criteria, which allow factor sorting and PMF run acceptance/rejection. The selected criteria for traffic (HOA) and BBOA were the correlation with equivalent black carbon from traffic (eBC(tr)) and the explained variation of m/z 60, respectively. COA was assessed by the prominence of a lunchtime concentration peak within the diurnal cycle. SV-OOA and LV-OOA were evaluated based on the fractions of m/z 43 and 44 in their respective factor profiles. Seasonal pre-tests revealed a noncontinuous separation of OOA into SV-OOA and LV-OOA, in particular during the warm seasons. Therefore, a differentiation between four-factor solutions (HOA, COA, BBOA and OOA) and five-factor solutions (HOA, COA, BBOA, SVOOA and LV-OOA) was also conducted based on the criterion for SV-OOA. HOA and COA contribute between 0.4-0.7 mu g m(-3) (7.8 %-9.0 %) and 0.7-1.2 mu g m(-3) (12.2 %-15.7 %) on average throughout the year, respectively. BBOA shows a strong yearly cycle with the lowest mean concentrations in summer (0.6 mu g m(-3), 12.0 %), slightly higher mean concentrations during spring and fall (1.0 and 1.5 mu g m(-3), or 15.6% and 18.6 %, respectively), and the highest mean concentrations during winter (1.9 mu g m(-3), 25.0 %). In summer, OOA is separated into SV-OOA and LV-OOA, with mean concentrations of 1.4 mu g m(-3) (26.5 %) and 2.2 mu g m(-3) (40.3 %), respectively. For the remaining seasons the seasonal concentrations of SV-OOA, LV-OOA and OOA range from 0.3 to 1.1 mu g m(-3) (3.4 %-15.9 %), from 0.6 to 2.2 mu g m(-3) (7.7 %33.7 %) and from 0.9 to 3.1 mu g m(-3) (13.7 %-39.9 %), respectively. The relative PMF errors modeled for this study for HOA, COA, BBOA, LV-OOA, SV-OOA and OOA are on average +/- 34 %, +/- 27 %, +/- 30 %, +/- 11 %, +/- 25 % and +/- 12 %, respectively