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The Hallucinatory Persisting Perception Disorder (HPPD) is a distressing condition associated with the consumption of hallucinatory drugs, especially psychedelics. Its symptoms include abnormal visual perceptions, hallucinations, and depersonalization/derealisation. They can appear after repetitive use of a psychoactive substance as well as after the first experience, and can last for a short period or for the entire life. It is currently not well understood how common HPPD is, or what are the mechanisms behind it. However, much research has been conducted on the processes behind plasticity in the primary visual cortex, both during development and in adulthood, and recent studies have provided some insights into how psychedelics work on the brain. It is then possible now to try and link the different pieces of information concerning plasticity in the visual cortex, psychedelics, and HPPD. With the recent increase in interest and attention surrounding psychedelics, the number of people experimenting with these substances has increased. As a result, it is important for research on HPPD and its potential relationship with psychedelics to continue, as well as for accurate information about the potential risks and effects of using psychedelics to be widely disseminated. The first part of this article describes the mechanisms behind the critical period for vision, and the plasticity of the visual system during development and adulthood. This part, which includes the first three paragraphs, has been adapted from my master’s thesis (Scarlatti, 2021). In the second part, I show how this could be relevant for HPPD.

We propose a methodology to derive the aerosol optical depth (AOD) and Angstrom exponent (AE) from calibrated images of an all-sky camera. It is based on a machine learning (ML) approach that establishes a relationship between AERONET measurements of AOD and AE and different signals derived from the principal plane radiance measured by an all-sky camera at three RGB channels. Gaussian process regression (GPR) has been chosen as machine learning method and applied to four models that differ in the input choice: RGB individual signals to predict spectral AOD; red signal only to predict spectral AOD and AE; blue-to-red ratio (BRR) signals to predict spectral AOD and AE; red signals to predict spectral AOD and AE at once. The novelty of our approach mostly relies on obtaining a cloud-screened and smoothed signal that enhances the aerosol features contained in the principal plane radiance and can be applied in partially cloudy conditions. In addition, a quality assurance criterion for the prediction has been also suggested, which significantly improves our results. When applied, our results are very satisfactory for all the models and almost all predictions are close to real values within ±0.02 for AOD and ±0.2 for AE, whereas the MAE is less than 0.005. They show an excellent agreement with AERONET measurements, with correlation coefficients over 0.92. Moreover, more than 87% of our predictions lie within the AERONET uncertainties (±0.01 for AOD, ±0.1 for AE) for all the output parameters of the best model. All the models offer a high degree of numerical stability with negligible sensitivities to the training data, atmospheric conditions and instrumental issues. All this supports the strength and efficiency of our models and the potential of our predictions. The optimum performance shown by our proposed methodology indicates that a well-calibrated all-sky camera can be routinely used to accurately derive aerosol properties. Together, all this makes the all-sky cameras ideal for aerosol research and this work may represent a significant contribution to the aerosol monitoring.

All sky-cameras are devices with a very high potential in order to study atmospheric phenomena and were originally designed to obtain the cloud cover. However, methods based in different approaches produce significant differences in the results. State-of-art methods usually offer better performance, thanks to computer vision and machine learning (ML) techniques, than traditional algorithms based on channel ratios using both fixed and adaptive thresholds to classify the pixels of one image as cloud or cloud free. We have developed a cloud cover adaptive threshold algorithm base on Probability Density Function (PDF) of the Blue to Red Ratio (BRR), standing out in: simplicity; ease of implementation; compatibility with any sky-camera in terms of technical requirement and type of image acquisition. The goal of this study is to compare our algorithm with a most fashionable method based on Machine learning, discovering the pros and cons of each one and weather ultimately less can be more. The comparison has been done using a set of 1-year HDR imagery database, representing a wider range of atmospheric scenarios such as clear sky, cloudy, partly cloudy and different types of aerosol conditions and clouds such as cirrus, cumulus, stratum and nimbus. To stablish a quantitative comparison of both methods, a limited set of images has been chosen. The PDF method show better agreement than our ML implementation, with a better performance for all weather conditions, in comparison against our cloud cover database.

A commercial all-sky-camera is employed to derive a whole-sky product of Cloud Optical Depth. The methodology consists in a radiative closure combining measurements of the blue and red channels with libRadtran 1D monochromatic radiance simulations. Besides, a matrix of data quality Flags is obtained for every COD image. The data quality Flags indicate the reliability of the retrieval at each pixel, and gives information about the method to solve the radiance monochromatic ambivalence. In addition, the Flags product also indicates the presence of out-of-range radiances with respect to the RT simulations. Such out-of-range radiances are related to the neglection of horizontal radiation transport in the 1D plane-parallel approach. A set of around 2000 images during 2020 have been analyzed and COD has been obtained for each pixel. The COD shows values ranging from 0 to 130, with around 83% of the cases between 5 and 30. Our COD results have been validated using the zenith COD retrieval from AERONET. Our results present a very good agreement with AERONET cloud mode retrieval and show a correlation factor of 0.94 and a slope of 0.99 respectively.

The radiative closure methodologies to obtain Cloud Optical Depth (COD) from Remote Sensing techniques have traditionally relied on one-dimensional (1D) assumptions. These assumptions might be far away from the radiation transport over a realistic three-dimensional (3D) atmosphere, especially in cloudy conditions, as the natural inhomogeneities of clouds are not conveniently represented and treated in 1D models. The differences between the 1D and 3D approaches manifests in the 3D effects: a) the plane-parallel albedo bias and, b) the horizontal transport effect. The plane-parallel albedo bias is usually addressed by means of the Independent Pixel approximation (IPA), that considers each pixel radiatively independent from the others. Nevertheless, the IPA neglects the horizontal transport, entailing bias in the retrievals. In this work, we use the advantages of 3D radiative transfer (RT) to analyze COD and parameterize the 3D biases in terms of the plane-parallel approach. Detailed 3D RT simulations using MYSTIC are performed over two Highly Resolved Large Eddy Simulations cloud fields of known optical thickness. The output radiance is analyzed by a 1D IPA inversion retrieval based on a radiative closure to obtain the COD. The comparison between the retrieved COD fields for diverse illumination conditions and the real COD allow us to study the 3D effects separately and evaluate the retrieval. Our results show radiation enhancement in cloud edges depending on solar, viewing and cloud geometries, that induces a COD underestimation. The 1D approach works well for overcast conditions and underestimates the COD in broken clouds scenarios.

The availability of long-term records of the total ozone content (TOC) represents a valuable source of information for studies on the assessment of short- and long-term atmospheric changes and their impact on the terrestrial ecosystem. In particular, ground-based observations represent a valuable tool for validating satellite-derived products. To our knowledge, details about software packages for processing Brewer spectrophotometer measurements and for retrieving the TOC are seldom specified in studies using such datasets. The sources of the differences among retrieved TOCs from the Brewer instruments located at the Italian stations of Rome and Aosta, using three freely available codes (Brewer Processing Software, BPS; O3Brewer software; and European Brewer Network (EUBREWNET) level 1.5 products) are investigated here. Ground-based TOCs are also compared with Ozone Monitoring Instrument (OMI) TOC retrievals used as an independent dataset since no other instruments near the Brewer sites are available.The overall agreement of the BPS and O3Brewer TOC data with EUBREWNET data is within the estimated total uncertainty in the retrieval of total ozone from a Brewer spectrophotometer (1 %). However, differences can be found depending on the software in use. Such differences become larger when the instrumental sensitivity exhibits a fast and dramatic drift which can affect the ozone retrievals significantly. Moreover, if daily mean values are directly generated by the software, differences can be observed due to the configuration set by the users to process single ozone measurement and the rejection rules applied to data to calculate the daily value.This work aims to provide useful information both for scientists engaged in ozone measurements with Brewer spectrophotometers and for stakeholders of the Brewer data products available on Web-based platforms.

The availability of long-term records of the total ozone content (TOC) represents a valuable source of information in studies on the assessment of short and long-term changes and their impact on the terrestrial ecosystem. In addition, ground-based observations represent a valuable tool to validate satellite-derived products. To our knowledge, details about processing software packages to retrieve the TOC from Brewer spectrophotometer measurements are seldom specified in studies concerning such datasets, although some discrepancies can arise from the use of different algorithms and implementations. The deviations among retrieved TOCs from the Brewer instruments located at Rome and Aosta (Italy), using different processing software (Brewer Processing Software, O3Brewer software and EUBREWNET products (Level 1.5) are investigated. Ground-based TOCs are also compared with the Ozone Monitoring Instrument (OMI) TOC retrievals used as an independent dataset since no other instruments near the Brewer sites, are available. Although the overall agreement of the BPS and O3Brewer TOC data with EUBREWNET data is clearly very good (as expected) and in most cases within the Brewer declared uncertainty less than 2 %, it is worth noticing that slight differences have been seen depending on the software in use. Such differences become larger when the instrumental sensitivity exhibits a long-term drift and even in short-term episodes due to the different algorithm for the standard lamp correction. This work aims to provide useful information both for scientists engaged in ozone measurements with Brewer spectrophotometry and for stakeholders of the Brewer data products available at web-based platforms.

The list of Italian violinist-composers who settled in the British Isles during the eighteenth century includes Pietro Castrucci, Francesco Geminiani, Felice Giardini, Francesco Barsanti and a member of one of the most renowned musical families of Italy, Francesco Scarlatti. The younger brother of the illustrious oratorio and opera composer Alessandro Scarlatti, and therefore the uncle of the great harpsichord composer Domenico Scarlatti, Francesco Scarlatti left few traces of his life and career. The majority of his previously known compositions consisted of vocal works and only one instrumental piece, a sinfonia in C major. The existence of the concerti grossi of this edition was unknown until 2000, when they were discovered in one of the two workbooks of Charles Avison that reappeared after being hidden for more than two hundred years. The concertos were probably written in London at some point after Francesco Scarlatti moved to England in 1719 and represent a significant addition to his total output. They also enhance our appreciation and understanding of the musical activities of all the members of the Scarlatti family, and provide further insights into the reception of Italian musicians and the Italian concerto grosso in England during the eighteenth century.