The Accessory Olfactory System: Innately Specialized or Microcosm of Mammalian Circuitry?

In mammals, the accessory olfactory system is a distinct circuit that has received attention for its role in detecting and responding to pheromones. While the neuroscientific investigation of this system is comparatively new, recent advances and its compact size have made it an attractive model for developing an end-to-end understanding of such questions as regulation of essential behaviors, plasticity, and individual recognition. Recent discoveries have indicated a need to reevaluate our conception of this system, suggesting that ( a) physical principles-rather than biological necessity-play an underappreciated role in its raison d'être and that ( b) the anatomy of downstream projections is not dominated by unique specializations but instead consists of an abbreviated cortical/basal ganglia motif reminiscent of other sensorimotor systems. These observations suggest that the accessory olfactory system distinguishes itself primarily by the physicochemical properties of its ligands, but its architecture is otherwise a microcosm of mammalian neurocircuitry.

Different learning aberrations relate to delusion-like beliefs with different contents.

The prediction error account of delusions has had success. However, its explanation of delusions with different contents has been lacking. Persecutory delusions and paranoia are the common unfounded beliefs that others have harmful intentions towards us. Other delusions include believing that one's thoughts or actions are under external control or that events in the world have specific personal meaning. We compare learning in two different cognitive tasks, probabilistic reversal learning and Kamin blocking, that have relationships to paranoid and non-paranoid delusion-like beliefs, respectively. We find that clinical high-risk status alone does not result in different behavioural results in the probabilistic reversal learning task but that an individual's level of paranoia is associated with excessive switching behaviour. During the Kamin blocking task, paranoid individuals learned inappropriately about the blocked cue. However, they also had decreased learning about the control cue, suggesting more general learning impairments. Non-paranoid delusion-like belief conviction (but not paranoia) was associated with aberrant learning about the blocked cue but intact learning about the control cue, suggesting specific impairments in learning related to cue combination. We fit task-specific computational models separately to behavioural data to explore how latent parameters vary within individuals between tasks and how they can explain symptom-specific effects. We find that paranoia is associated with low learning rates in the probabilistic reversal learning task and the blocking task. Non-paranoid delusion-like belief conviction is instead related to parameters controlling the degree and direction of similarity between cue updating during simultaneous cue presentation. These results suggest that paranoia and other delusion-like beliefs involve dissociable deficits in learning and belief updating, which, given the transdiagnostic status of paranoia, might have differential utility in predicting psychosis.

Triadic Halobenzene Processing Additive Combined Advantages of Both Solvent and Solid Types for Efficient and Stable Organic Solar Cells.

Solvent additives with a high boiling point (BP) and low vapor pressure (VP) have formed a key handle for improving the performance of organic solar cells (OSCs). However, it is not always clear whether they remain in the active-layer film after deposition, which can negatively affect the reproducibility and stability of OSCs. In this study, an easily removable solvent additive (4-chloro-2-fluoroiodobenzene (CFIB)) with a low BP and high VP is introduced, behaving like volatile solid additives that can be completely removed during the device fabrication process. In-depth studies of CFIB addition into the D18-Cl donor and N3 acceptor validate its dominant non-covalent intermolecular interactions with N3 through effective electrostatic interactions. Such phenomena improve charge dynamics and kinetics by optimizing the morphology, leading to enhanced performance of D18-Cl:N3-based devices with a power conversion efficiency of 18.54%. The CFIB-treated device exhibits exceptional thermal stability (T80 lifetime = 120 h) at 85 °C compared with the CFIB-free device, because of its morphological robustness by evolving no residual CFIB in the film. The CFIB features a combination of advantages of solvent (easy application) and solid (high volatility) additives, demonstrating its great potential use in the commercial mass production of OSCs.

Fostering a Holistic Understanding of the Full Volatility Spectrum of Organic Compounds from Benzene Series Precursors through Mechanistic Modeling.

A comprehensive understanding of the full volatility spectrum of organic oxidation products from the benzene series precursors is important to quantify the air quality and climate effects of secondary organic aerosol (SOA) and new particle formation (NPF). However, current models fail to capture the full volatility spectrum due to the absence of important reaction pathways. Here, we develop a novel unified model framework, the integrated two-dimensional volatility basis set (I2D-VBS), to simulate the full volatility spectrum of products from benzene series precursors by simultaneously representing first-generational oxidation, multigenerational aging, autoxidation, dimerization, nitrate formation, etc. The model successfully reproduces the volatility and O/C distributions of oxygenated organic molecules (OOMs) as well as the concentrations and the O/C of SOA over wide-ranging experimental conditions. In typical urban environments, autoxidation and multigenerational oxidation are the two main pathways for the formation of OOMs and SOA with similar contributions, but autoxidation contributes more to low-volatility products. NOx can reduce about two-thirds of OOMs and SOA, and most of the extremely low-volatility products compared to clean conditions, by suppressing dimerization and autoxidation. The I2D-VBS facilitates a holistic understanding of full volatility product formation, which helps fill the large gap in the predictions of organic NPF, particle growth, and SOA formation.

Obscured Contribution of Oxygenated Intermediate-Volatility Organic Compounds to Secondary Organic Aerosol Formation from Gasoline Vehicle Emissions.

Secondary organic aerosol (SOA) formation from gasoline vehicles spanning a wide range of emission types was investigated using an oxidation flow reactor (OFR) by conducting chassis dynamometer tests. Aided by advanced mass spectrometric techniques, SOA precursors, including volatile organic compounds (VOCs) and intermediate/semivolatile organic compounds (I/SVOCs), were comprehensively characterized. The reconstructed SOA produced from the speciated VOCs and I/SVOCs can explain 69% of the SOA measured downstream of an OFR upon 0.5-3 days' OH exposure. While VOCs can only explain 10% of total SOA production, the contribution from I/SVOCs is 59%, with oxygenated I/SVOCs (O-I/SVOCs) taking up 20% of that contribution. O-I/SVOCs (e.g., benzylic or aliphatic aldehydes and ketones), as an obscured source, account for 16% of total nonmethane organic gas (NMOG) emission. More importantly, with the improvement in emission standards, the NMOG is effectively mitigated by 35% from China 4 to China 6, which is predominantly attributed to the decrease of VOCs. Real-time measurements of different NMOG components as well as SOA production further reveal that the current emission control measures, such as advances in engine and three-way catalytic converter (TWC) techniques, are effective in reducing the "light" SOA precursors (i.e., single-ring aromatics) but not for the I/SVOC emissions. Our results also highlight greater effects of O-I/SVOCs to SOA formation than previously observed and the urgent need for further investigation into their origins, i.e., incomplete combustion, lubricating oil, etc., which requires improvements in real-time molecular-level characterization of I/SVOC molecules and in turn will benefit the future design of control measures.

Secondary Organic Aerosol from OH-Initiated Oxidation of Mixtures of d-Limonene and ß-Myrcene.

The chemical composition and physical properties of secondary organic aerosol (SOA) generated through OH-initiated oxidation of mixtures containing ß-myrcene, an acyclic monoterpene, and d-limonene, a cyclic monoterpene, were investigated to assess the extent of the chemical interactions between their oxidation products. The SOA samples were prepared in an environmental smog chamber, and their composition was analyzed offline using ultraperformance liquid chromatography coupled with electrospray ionization high-resolution mass spectrometry (UPLC-ESI-HRMS). Our results suggested that SOA containing ß-myrcene showed a higher proportion of oligomeric compounds with low volatility compared to that of SOA from d-limonene. The formula distribution and signal intensities of the mixed SOA could be accurately predicted by a linear combination of the mass spectra of the SOA from individual precursors. Effects of cross-reactions were observed in the distribution of isomeric oxidation products within the mixed SOA, as made evident by chromatographic analysis. On the whole, ß-myrcene and d-limonene appear to undergo oxidation by OH largely independently from each other, with only subtle effects from cross-reactions influencing the yields of specific oxidation products.

Molecular-Level Insights into the Relationship between Volatility of Organic Aerosol Constituents and PM2.5 Air Pollution Levels: A Study with Ultrahigh-Resolution Mass Spectrometry.

Volatility of organic aerosols (OAs) significantly influences new particle formation and the occurrence of particulate air pollution. However, the relationship between the volatility of OA and the level of particulate air pollution (i.e., particulate matter concentration) is not well understood. In this study, we compared the chemical composition (identified by an ultrahigh-resolution Orbitrap mass spectrometer) and volatility (estimated based on a predeveloped parametrization method) of OAs in urban PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 µm) samples from seven German and Chinese cities, where the PM2.5 concentration ranged from a light (14 µg m-3) to heavy (319 µg m-3) pollution level. A large fraction (71-98%) of compounds in PM2.5 samples were attributable to intermediate-volatility organic compounds (IVOCs) and semivolatile organic compounds (SVOCs). The fraction of low-volatility organic compounds (LVOCs) and extremely low-volatility organic compounds (ELVOCs) decreased from clean (28%) to heavily polluted urban regions (2%), while that of IVOCs increased from 34 to 62%. We found that the average peak area-weighted volatility of organic compounds in different cities showed a logarithmic correlation with the average PM2.5 concentration, indicating that the volatility of urban OAs increases with the increase of air pollution level. Our results provide new insights into the relationship between OA volatility and PM pollution levels and deepen the understanding of urban air pollutant evolution.

Molecular Insights into Gas-Particle Partitioning and Viscosity of Atmospheric Brown Carbon.

Biomass burning organic aerosol (BBOA), containing brown carbon chromophores, plays a critical role in atmospheric chemistry and climate forcing. However, the effects of evaporation on BBOA volatility and viscosity under different environmental conditions remain poorly understood. This study focuses on the molecular characterization of laboratory-generated BBOA proxies from wood pyrolysis emissions. The initial mixture, "pyrolysis oil (PO1)", was progressively evaporated to produce more concentrated mixtures (PO1.33, PO2, and PO3) with volume reduction factors of 1.33, 2, and 3, respectively. Chemical speciation and volatility were investigated using temperature-programmed desorption combined with direct analysis in real-time ionization and high-resolution mass spectrometry (TPD-DART-HRMS). This novel approach quantified saturation vapor pressures and enthalpies of individual species, enabling the construction of volatility basis set distributions and the quantification of gas-particle partitioning. Viscosity estimates, validated by poke-flow experiments, showed a significant increase with evaporation, slowing particle-phase diffusion and extending equilibration times. These findings suggest that highly viscous tar ball particles in aged biomass burning emissions form as semivolatile components evaporate. The study highlights the importance of evaporation processes in shaping BBOA properties, underscoring the need to incorporate these factors into atmospheric models for better predictions of BBOA aging and its environmental impact.

New Insights on the Formation of Nucleation Mode Particles in a Coastal City Based on a Machine Learning Approach.

Atmospheric particles have profound implications for the global climate and human health. Among them, ultrafine particles dominate in terms of the number concentration and exhibit enhanced toxic effects as a result of their large total surface area. Therefore, understanding the driving factors behind ultrafine particle behavior is crucial. Machine learning (ML) provides a promising approach for handling complex relationships. In this study, three ML models were constructed on the basis of field observations to simulate the particle number concentration of nucleation mode (PNCN). All three models exhibited robust PNCN reproduction (R2 > 0.80), with the random forest (RF) model excelling on the test data (R2 = 0.89). Multiple methods of feature importance analysis revealed that ultraviolet (UV), H2SO4, low-volatility oxygenated organic molecules (LOOMs), temperature, and O3 were the primary factors influencing PNCN. Bivariate partial dependency plots (PDPs) indicated that during nighttime and overcast conditions, the presence of H2SO4 and LOOMs may play a crucial role in influencing PNCN. Additionally, integrating additional detailed information related to emissions or meteorology would further enhance the model performance. This pilot study shows that ML can be a novel approach for simulating atmospheric pollutants and contributes to a better understanding of the formation and growth mechanisms of nucleation mode particles.

Enhanced Light Absorption and Elevated Viscosity of Atmospheric Brown Carbon through Evaporation of Volatile Components.

Samples of brown carbon (BrC) material were collected from smoke emissions originating from wood pyrolysis experiments, serving as a proxy for BrC representative of biomass burning emissions. The acquired samples, referred to as "pyrolysis oil (PO1)," underwent subsequent processing by thermal evaporation of their volatile compounds, resulting in a set of three additional samples with volume reduction factors of 1.33, 2, and 3, denoted as PO1.33, PO2, and PO3. The chemical compositions of these POx samples and their BrC chromophore features were analyzed using a high-performance liquid chromatography instrument coupled with a photodiode array detector and a high-resolution mass spectrometer. The investigation revealed a noteworthy twofold enhancement of BrC light absorption observed for the progression of PO1 to PO3 samples, assessed across the spectral range of 300-500 nm. Concurrently, a decrease in the absorption Ångstrom exponent (AAE) from 11 to 7 was observed, indicating a weaker spectral dependence. The relative enhancement of BrC absorption at longer wavelengths was more significant, as exemplified by the increased mass absorption coefficient (MAC) measured at 405 nm from 0.1 to 0.5 m2/g. Molecular characterization further supports this darkening trend, manifesting as a depletion of small oxygenated, less absorbing monoaromatic compounds and the retention of relatively large, less polar, more absorbing constituents. Noteworthy alterations of the PO1 to PO3 mixtures included a reduction in the saturation vapor pressure of their components and an increase in viscosity. These changes were quantified by the mean values shifting from approximately 1.8 × 103 µg/m3 to 2.3 µg/m3 and from ∼103 Pa·s to ∼106 Pa·s, respectively. These results provide quantitative insights into the extent of BrC aerosol darkening during atmospheric aging through nonreactive evaporation. This new understanding will inform the refinement of atmospheric and chemical transport models.

VUCA in the present-day health workplace and the mental health and wellbeing of health care workers: a systematic scoping review.

BACKGROUND: The health workplace is fraught with fluctuations and uncertainties, creating a volatile, uncertain, complex, and ambiguous (VUCA) environment, particularly impacting frontline healthcare workers (HCWs) and leading to an epidemic of stress, burnout and health issues, exacerbated by the COVID-19 pandemic. OBJECTIVES: This paper aims to explore the multifaceted aspects of HCWs wellbeing, address challenges arising due to COVID-19 and VUCA and highlight innovative approaches within health systems to enhance the quality of life HCWs. METHODS: A systematic review was conducted using PubMed and Scopus with search terms including 'VUCA,' 'health personnel,' 'frontline healthcare workers,' and 'psychological wellbeing.' Grey literature focusing on Australia and Nigeria was also included. Search was limited to titles on "COVID-19", articles published in English, and articles published from inception to 11th March 2024. FINDINGS: Initial search terms generated hundreds of thousands of literatures but after limitations to titles on COVID-19, 32 articles were screened and 22 selected for critical review. Seven other grey articles were included with focus on Australia and Nigeria. The summary findings indicate the disruptiveness of VUCA, and associated need to improve healthcare workers' resilience and this calls for further research. CONCLUSION: This report highlights the further need to explore the volatile, uncertain, complex and/or ambiguous health workplace with a view to improve healthcare workers wellbeing. Intentional organizational support strategies along with personal coping strategies should be further explored towards improving HCWs resilience and wellbeing.

On explosive boiling of a multicomponent Leidenfrost drop.

The gasification of multicomponent fuel drops is relevant in various energy-related technologies. An interesting phenomenon associated with this process is the self-induced explosion of the drop, producing a multitude of smaller secondary droplets, which promotes overall fuel atomization and, consequently, improves the combustion efficiency and reduces emissions of liquid-fueled engines. Here, we study a unique explosive gasification process of a tricomponent droplet consisting of water, ethanol, and oil ("ouzo"), by high-speed monitoring of the entire gasification event taking place in the well-controlled, levitated Leidenfrost state over a superheated plate. It is observed that the preferential evaporation of the most volatile component, ethanol, triggers nucleation of the oil microdroplets/nanodroplets in the remaining drop, which, consequently, becomes an opaque oil-in-water microemulsion. The tiny oil droplets subsequently coalesce into a large one, which, in turn, wraps around the remnant water. Because of the encapsulating oil layer, the droplet can no longer produce enough vapor for its levitation, and, thus, falls and contacts the superheated surface. The direct thermal contact leads to vapor bubble formation inside the drop and consequently drop explosion in the final stage.

Fruit bats adjust their decision-making process according to environmental dynamics.

One of the main functions of behavioral plasticity lies in the ability to contend with dynamic environments. Indeed, while numerous studies have shown that animals adapt their behavior to the environment, how they adapt their latent learning and decision strategies to changes in the environment is less understood. Here, we used a controlled experiment to examine the bats' ability to adjust their decision strategy according to the environmental dynamics. Twenty-five Egyptian fruit bats were placed individually in either a stable or a volatile environment for four consecutive nights. In the stable environment, two feeders offered food, each with a different reward probability (0.2 vs. 0.8) that remained fixed over two nights and were then switched, while in the volatile environment, the positions of the more and the less rewarding feeders were changed every hour. We then fit two alternative commonly used models namely, reinforcement learning and win-stay-lose-shift strategies to the bats' behavior. We found that while the bats adapted their decision-making strategy to the environmental dynamics, they seemed to be limited in their responses based on natural priors. Namely, when the environment had changed slowly, at a rate that is natural for these bats, they seemed to rely on reinforcement learning and their performance was nearly optimal, but when the experimental environment changed much faster than in the natural environment, the bats stopped learning and switched to a random decision-making strategy. Together, these findings exemplify both the bats' decision-making plasticity as well as its natural limitations.

Why Some Nonelderly Adult Medicaid Enrollees Appear Ineligible Based on Their Annual Income.

CONTEXT: Recent studies have highlighted Medicaid enrollment among middle- and higher-income populations and questioned whether the program is reaching those for whom it is intended. METHODS: The authors use administrative tax data to measure Medicaid enrollment and income in 2017, they use survey data to measure monthly income, and they use administrative data to identify Medicaid enrollment pathways. FINDINGS: Among 38.8 million nonelderly adults in Medicaid at any point in 2017, 24.4 million had annual income below their state's typical eligibility threshold, and 14.4 million (37%) had income above the threshold. Among those above the threshold, 3.5 million enrolled through a pathway allowing higher income (pregnant women, the "medically needy," and others). The authors also estimate that more than 12 million had at least one month with income below the threshold, and roughly 4 million had at least five months with income below the eligibility threshold. CONCLUSIONS: Pathways allowing higher income account for one quarter of enrollees with annual incomes above typical thresholds. Among low-income adults, month-to-month variation in income is common and can account for most or all of the remaining enrollees with annual incomes above typical thresholds. A complete accounting of eligibility status would require merged data on income, Medicaid enrollment, and family structure.

Removal kinetics of vinyl acetate under aerobic and anoxic conditions in a batch bioreactor.

Vinyl acetate is a volatile organic compound widely used in the chemical industry, and there is a need for effective and economic removal of this volatile organic compound from wastewater and waste gases in chemical industries. This study aims to determine the biological treatability of vinyl acetate both under aerobic and anoxic conditions using mixed cultures obtained from a wastewater treatment plant. Considering the previous studies in the literature, testing the biodegradability of vinyl acetate under both aerobic and anoxic conditions, together with evaluating the effect of other mechanisms, such as adsorption and volatilization, on the removal of vinyl acetate, can be regarded as the prominent part of this study. Wastewater containing artificially prepared vinyl acetate was treated in a batch bioreactor, and performance and kinetic constants were investigated. Aerobic treatment under batch conditions conformed to the Haldane biokinetic equation, and the biokinetic constants of µmax, Ks, and Ki were calculated as 0.66 h-1, 19.67 mg L-1 and 50.56 mg L-1, respectively. Anoxic treatment under batch conditions conformed to the Monod biokinetic equation, and the biokinetic constants of µmax and Ks were calculated as 0.31 h-1 and 33.88 mg L-1, respectively. Experiments revealed that vinyl acetate was not volatile, and its adsorption and biological treatment performances were 28% and 72%, respectively. The mixed culture had a very high performance for removing vinyl acetate under batch operating conditions. The primary mechanism of vinyl acetate removal was found to be biological treatment.

Environmentally clean and dirty energy equities during extraordinary global conditions.

I examine which extraordinary international events coincide with pronounced changes in the equity markets for some of the world's largest publicly traded suppliers on opposite sides of the global energy mix - oil and environmentally clean energy companies. First, I adapt an intuitively appealing non-parametric filter to empirically timestamp unexpected and prominent increases and decreases in a wide range of global indicators relevant to the international energy market. Then, I use such extraordinary conditions to characterise the performance of oil and environmentally clean energy equities, and their relationships. My findings suggest that jumps in the global stock market, international crude oil market shocks, and the US dollar real effective exchange rate, are the indicators that define the financial landscape during which considerable gains, losses, and instability across both types of energy markets materialise. In contrast, major elevated uncertainties related to geo-political risk and climate policy reflect relative stability in the equities of both oil and environmentally clean energy companies. Although these results imply that both energy assets are potentially lucrative hedging strategies for investors to exploit during heightened geo-political and climate policy uncertainties, clean energy equities offer market participants the option to combine profit maximising and sustainability objectives while minimising global energy security risks.

Climate change effects and their implications for the financial markets: Evidence from the United Kingdom.

This study aims to examine how the climate affects the behaviour of the stock market. To achieve this, we have drawn on daily data from Jan 2005 to Jan 31, 2023 and several environmental factors (e.g., temperature, humidity, cloud cover and visibility) to account for extreme weather conditions using the 21-day moving average and its standard deviation. The empirical analysis has revealed three key findings regarding the impact of weather on the stock market's behaviour. First, various forms of extreme weather conditions consistently lead to influence stock behaviour. Second, results provide valuable insights into market behaviour and help investors to make more informed investment decisions. Third, the weather conditions have new information about the climate risk and investors should react to it swiftly in light of our findings. The saliency theory can help reconcile the theoretical conflicts between the real options and risk-shifting theories when it comes to investing in uncertain and extreme climate conditions.

Atomic Layer Deposition of CeO2 Film with a Novel Heteroleptic Ce(III) Complex.

In this paper, four heteroleptic Ce(III) complexes, including Ce(thd)3-phen (thd = 2,2,6,6-tetramethyl-3,5-heptanedione, phen = 1, 10-phenanthroline (1), Ce(thd)3-MEDA (MEDA = N-Methylethylenediamine (2), Ce(thd)3-MOMA (MOMA = N-(2-Methoxyethyl)methylamine (3), and Ce(thd)3-DMDE (DMDE = N,N″-dimethyl ethanol amine (4), were synthesized and characterized with 1H-NMR, elemental analysis, and X-ray single-crystal diffraction. The thermogravimetric analysis and vapor pressure results indicated that the complexing ability of a nitrogen-containing bidentate ligand with a cerium ion was stronger than that of a mixed oxygen-nitrogen-containing bidentate ligand. Complex 2 was selected as an ALD precursor to deposit a CeO2 film on a SiO2/Si (100) wafer. The self-limited deposition results demonstrated that complex 2 was a potential ALD precursor.

Prediction of volatility and seasonality vegetation by using the GARCH and Holt-Winters models.

Seasonality and volatility of vegetation in the ecosystem are associated with climatic sensitivity, which can have severe consequences for the environment as well as on the social and economic well-being of the nation. Monitoring and forecasting vegetation growth patterns in ecosystems significantly rely on remotely sensed vegetation indices, such as Normalized Difference Vegetation Index (NDVI). A novel integration of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and the Holt-Winters (H-W) models was used to simulate the seasonality and volatility of the three different agro-climatic zones in Jharkhand, India: the central north-eastern, eastern, and south-eastern agro-climatic zones. MODIS Terra Vegetation Indices NDVI data MOD13Q1, from 2001 to 2021, was used to create NDVI time series volatility and seasonality modeled by the GARCH and the H-W models, respectively. GARCH-based Exponential GARCH (EGARCH) [1,1] and Standard GARCH (SGARCH) [1,1] models were used to check the volatility of vegetation growth in three different agro-climatic zones of Jharkhand. The SGARCH [1,1] and EGARCH [1,1] models for the western agro-climatic zone experienced the best indicator as it has maximum likelihood and minimal Schwarz-Bayesian criterion and Akaike information criterion. The seasonality results showed that the additive H-W model showed better results in the eastern agro-climatic zone with the optimized values of MAE (16.49), MAPE (0.49), NSE (0.86), RMSE (0.49), and R2 (0.82) followed by the south-eastern and central north-eastern agro-climatic zones. By utilizing the H-W and GARCH models, the finding demonstrates that vegetation orientation and monitoring seasonality can be predicted using NDVI.

Stochastic Volatility Models with Skewness Selection.

This paper expands traditional stochastic volatility models by allowing for time-varying skewness without imposing it. While dynamic asymmetry may capture the likely direction of future asset returns, it comes at the risk of leading to overparameterization. Our proposed approach mitigates this concern by leveraging sparsity-inducing priors to automatically select the skewness parameter as dynamic, static or zero in a data-driven framework. We consider two empirical applications. First, in a bond yield application, dynamic skewness captures interest rate cycles of monetary easing and tightening and is partially explained by central banks' mandates. In a currency modeling framework, our model indicates no skewness in the carry factor after accounting for stochastic volatility. This supports the idea of carry crashes resulting from volatility surges instead of dynamic skewness.