Habituated

Senior Project submitted to The Division of Arts of Bard College

Neural codes for one’s own position and direction in a real-world “vista” environment

Humans, like animals, rely on an accurate knowledge of one’s spatial position and facing direction to keep orientated in the surrounding space. Although previous neuroimaging studies demonstrated that scene-selective regions (the parahippocampal place area or PPA, the occipital place area or OPA and the retrosplenial complex or RSC), and the hippocampus (HC) are implicated in coding position and facing direction within small-(room-sized) and large-scale navigational environments, little is known about how these regions represent these spatial quantities in a large open-field environment. Here, we used functional magnetic resonance imaging (fMRI) in humans to explore the neural codes of these navigationally-relevant information while participants viewed images which varied for position and facing direction within a familiar, real-world circular square. We observed neural adaptation for repeated directions in the HC, even if no navigational task was required. Further, we found that the amount of knowledge of the environment interacts with the PPA selectivity in encoding positions: individuals who needed more time to memorize positions in the square during a preliminary training task showed less neural attenuation in this scene-selective region. We also observed adaptation effects, which reflect the real distances between consecutive positions, in scene-selective regions but not in the HC. When examining the multi-voxel patterns of activity we observed that scene-responsive regions and the HC encoded both spatial information and that the RSC classification accuracy for positions was higher in individuals scoring higher to a self-reported questionnaire of spatial abilities. Our findings provide new insight into how the human brain represents a real, large-scale “vista” space, demonstrating the presence of neural codes for position and direction in both scene-selective and hippocampal regions, and revealing the existence, in the former regions, of a map-like spatial representation reflecting real-world distance between consecutive positions

Presenting a Semi-Automatic, Statistically-Based Approach to Assess the Sharpness Level of Optical Images from Natural Targets via the Edge Method. Case Study: The Landsat 8 OLI–L1T Data

Developing reliable methodologies of data quality assessment is of paramount importance for maximizing the exploitation of Earth observation (EO) products. Among the different factors influencing EO optical image quality, sharpness has a relevant role. When implementing on-orbit approaches of sharpness assessment, such as the edge method, a crucial step that strongly affects the final results is the selection of suitable edges to use for the analysis. Within this context, this paper aims at proposing a semi-automatic, statistically-based edge method (SaSbEM) that exploits edges extracted from natural targets easily and largely available on Earth: agricultural fields. For each image that is analyzed, SaSbEM detects numerous suitable edges (e.g., dozens-hundreds) characterized by specific geometrical and statistical criteria. This guarantees the repeatability and reliability of the analysis. Then, it implements a standard edge method to assess the sharpness level of each edge. Finally, it performs a statistical analysis of the results to have a robust characterization of the image sharpness level and its uncertainty. The method was validated by using Landsat 8 L1T products. Results proved that: SaSbEM is capable of performing a reliable and repeatable sharpness assessment; Landsat 8 L1T data are characterized by very good sharpness performance

The Impact of School and After-School Friendship Networks on Adolescent Vaccination Behavior

Psychological and social characteristics of individuals are important determinants of their health choices and behaviors. Social networks represent "pipes" through which information and opinions circulate and spread out in the social circle surrounding individuals, influencing their propensity toward important health care interventions. This paper aims to explore the relationship between students' vaccination health choices and their social networks. We administered a questionnaire to students to collect data on individual students' demographics, knowledge, and attitudes about vaccinations, as well as their social networks. Forty-nine pupils belonging to 4 classrooms in an Italian secondary school were enrolled in the study. We applied a logistic regression quadratic assignment procedure (LR-QAP) by regressing students' positive responsive behavior similarity as a dependent variable. LRQAP findings indicate that students' vaccination behavior similarity is significantly associated with after-school social ties and related social mechanisms, suggesting that pupils are more likely to share information and knowledge about health behaviors through social relationships maintained after school hours rather than through those established during the school day. Moreover, we found that vaccination behaviors are more similar for those students having the same ethnicity as well as for those belonging to the same class. Our findings may help policymakers in implementing effective vaccination strategies

Influence of γ-glutamyltransferase and alkaline phosphatase activity on in vitro fertilisation of bovine frozen/thawed semen

AbstractThe aim of this work was to evaluate whether the residual amount of γ-glutamyl-transferase (GGT) and alkaline phosphatase (ALP) in bovine sperm after freezing/thawing is correlated with fertility parameters, including blastocyst rates after in vitro fertilisation (IVF). The enzyme activities were determined in both spermatozoa and supernatant after centrifugation. While ALP was only correlated with sperm viability, GGT activity was correlated with sperm motility (rs = .4; p < .05) both in sperm and supernatant. Interestingly, GGT activity was also correlated with cleavage (rs = .5; p < .05 and .8; p < .01, for sperm and supernatant respectively) and blastocyst (rs = .6 and .9, for sperm and supernatant respectively; p < .01) rates obtained after IVF. These results suggest that GGT could play an important role in the protection of sperm against oxidative stress and could be considered a reliable marker to assess frozen/thawed sperm quality in bovine

CHM/REP1 Transcript Expression and Loss of Visual Function in Patients Affected by Choroideremia

PURPOSE. To evaluate the disease progression in patients with clinical and genetic diagnoses of choroideremia during a long-term follow-up and to investigate the relationship between pathogenic variants in the CHM/REP1 gene and disease phenotypes. METHODS. We performed a retrospective longitudinal study on 51 affected men by reviewing medical charts at baseline and follow-up visits to extract the following ocular findings: best-corrected visual acuity, Goldmann visual field, optical coherence tomography, microperimetry. Data obtained from the analysis of DNA and mRNA were reevaluated for genetic classification of patients. RESULTS. The longitudinal analysis showed a significant (P < 0.001) worsening of best-corrected visual acuity with a mean rate of 0.011 logMar per year before 50 years and 0.025 logMar per year after 50 years. Similarly, V4e Goldmann visual field area significantly (P ≤ 0.01) decreased at a mean rate of 2.7% per year before 40 years and 5.7% after 40 years. Moreover, we observed a significant (P < 0.05) decrease of macular sensitivity with a mean rate of 5.0% per year and a decrease of mean macular thickness with a mean rate of 0.8% per year. We classified our patients into two groups according to the expression of the CHM/ REP1 gene transcript and observed that mutations leading to mRNA absence are associated with an earlier best-corrected visual acuity and Goldmann visual field loss. CONCLUSIONS. Our analysis of morphological and functional parameters in choroideremia patients showed a slow disease progression, particularly in the first decades of life. Overall, reevaluation of clinical and molecular data suggests exploring the genotype–phenotype relationship based on CHM/REP1 transcript expression. PURPOSE. To evaluate the disease progression in patients with clinical and genetic diagnoses of choroideremia during a long-term follow-up and to investigate the relationship between pathogenic variants in the CHM/REP1 gene and disease phenotypes. METHODS. We performed a retrospective longitudinal study on 51 affected men by reviewing medical charts at baseline and follow-up visits to extract the following ocular findings: best-corrected visual acuity, Goldmann visual field, optical coherence tomography, microperimetry. Data obtained from the analysis of DNA and mRNA were reevaluated for genetic classification of patients. RESULTS. The longitudinal analysis showed a significant (P < 0.001) worsening of best-corrected visual acuity with a mean rate of 0.011 logMar per year before 50 years and 0.025 logMar per year after 50 years. Similarly, V4e Goldmann visual field area significantly (P ≤ 0.01) decreased at a mean rate of 2.7% per year before 40 years and 5.7% after 40 years. Moreover, we observed a significant (P < 0.05) decrease of macular sensitivity with a mean rate of 5.0% per year and a decrease of mean macular thickness with a mean rate of 0.8% per year. We classified our patients into two groups according to the expression of the CHM/ REP1 gene transcript and observed that mutations leading to mRNA absence are associated with an earlier best-corrected visual acuity and Goldmann visual field loss. CONCLUSIONS. Our analysis of morphological and functional parameters in choroideremia patients showed a slow disease progression, particularly in the first decades of life. Overall, reevaluation of clinical and molecular data suggests exploring the genotype–phenotype relationship based on CHM/REP1 transcript expression

Sentinel-2 Level-2 processing Sen2Cor status and outlook of 2022

The Sentinel-2 mission is fully operating since June 2017 with a constellation of two polar orbiting satellite units. Both Sentinel-2A and Sentinel-2B are equipped with an optical imaging sensor MSI (MultiSpectral Instrument) which acquires high spatial resolution optical data products. The Sentinel-2 mission is dedicated to land monitoring, emergency management and security. It serves for monitoring of land-cover change and biophysical variables related to agriculture and forestry, monitors coastal and inland waters and is useful for risk and disaster mapping. Accurate atmospheric correction of satellite observations is a precondition for the development and delivery of high quality applications. Therefore the atmospheric correction processor Sen2Cor was developed with the objective of delivering land surface reflectance products. Sen2Cor is designed to process single tile Level-1C products, providing Level-2A surface (Bottom-of-Atmosphere) reflectance product together with Aerosol Optical Thickness (AOT), Water Vapour (WV) estimation maps and a Scene Classification (SCL) map including cloud / cloud shadow classes for further processing. Sen2Cor processor can be downloaded from ESA website as a standalone tool for individual Level-2A processing by the users. It can be run either from command line or as a plugin of the Sentinel-2 Toolbox (SNAPS2TBX). In parallel, ESA started in June 2017 to use Sen2Cor for systematic Level-2A processing of Sentinel-2 acquisitions over Europe. Since March 2018, Level-2A products are generated by the official Sentinel-2 ground segment (PDGS) and are available on the Copernicus Open Access Hub. The objective of this presentation is to provide users with an overview of the Level-2A product contents and up-to-date information about the data quality of the Level-2A products (processing baseline PB.04.00 onwards) generated by Sentinel-2 PDGS since end of January 2022, in terms of Cloud Screening and Atmospheric Correction. In addition, the presentation will give an outlook on the recent updates of Sen2Cor, which improve L2A Data Quality: updated L2A metadata, updated scene classification, updated fall-back method using meteorological information from the Copernicus Atmosphere Monitoring Service, updated Copernicus DEM

Topography processing in Sen2Cor - Impact of horizontal resolution of Digital Surface Model

The Copernicus Sentinel-2 mission is fully operating since June 2017 with a constellation of two polar orbiting satellite units. Both Sentinel-2A and Sentinel-2B are equipped with an optical imaging sensor MSI (Multi-Spectral Instrument) which acquires high spatial resolution optical data products. Accurate atmospheric correction of satellite observations is a precondition for the development and delivery of high-quality applications. Therefore the atmospheric correction processor Sen2Cor was developed with the objective of delivering land surface reflectance products. Sen2Cor is designed to process single tile Level-1C products, providing Level-2A surface (Bottom-of-Atmosphere) reflectance product together with Aerosol Optical Thickness (AOT), Water Vapour (WV) estimation maps and a Scene Classification (SCL) map including cloud / cloud shadow classes for further processing. Sen2Cor processor can be downloaded from ESA website as a standalone tool for individual Level-2A processing by the users. It can be run either from command line or as a plugin of the Sentinel-2 Toolbox (SNAP-S2TBX). In parallel, ESA started in June 2017 to use Sen2Cor for systematic Level-2A processing of Sentinel-2 acquisitions over Europe. Since March 2018, Level-2A products are generated by the official Sentinel-2 ground segment (PDGS) and are available on the Copernicus Open Access Hub. Since the beginning of the Sentinel-2 mission, the digital surface model “PlanetDEM 90” from Planet Observer is used as source of Earth topography information, within the Sentinel-2 PDGS. It is at 90- meter resolution, based on SRTM data filled and corrected for 40% of Earth surface. However, until now most users had only access to original SRTM data to run with Sen2Cor or had to provide their own DEM following SRTM or DTED formats. The objective of this presentation is to provide users with an overview of how Sen2Cor makes use of the topography information to improve the quality of the cloud screening and scene classification as well as in the atmospheric correction and terrain correction. In addition, the presentation gives an outlook on Sen2Cor working with the upcoming Copernicus DEM, a new Digital Surface Model (DSM), which represents the surface of the Earth, including buildings, infrastructure and vegetation. This DEM is derived from an edited DSM named WorldDEM. The presentation shows the different L2A surface reflectance obtained with PlanetDEM 90, global Copernicus DEM at 30 m and at 90 m horizontal resolution, and some of these differences are discussed

Sentinel-2 Level-2 processing: Sen2Cor status and outlook for 2021

The Copernicus Sentinel-2 mission is fully operating since June 2017 with a constellation of two polar orbiting satellite units. Both Sentinel-2A and Sentinel-2B are equipped with an optical imaging sensor MSI (Multi-Spectral Instrument) which acquires high spatial resolution optical data products. The Sentinel-2 mission is dedicated to land monitoring, emergency management and security. It serves for monitoring of land-cover change and biophysical variables related to agriculture and forestry, monitors coastal and inland waters and is useful for risk and disaster mapping. Accurate atmospheric correction of satellite observations is a precondition for the development and delivery of high-quality applications. Therefore, the atmospheric correction processor Sen2Cor was developed with the objective of delivering land surface reflectance products. Sen2Cor is designed to process single tile Level-1C products, providing Level-2A surface (Bottom-of-Atmosphere) reflectance product together with Aerosol Optical Thickness (AOT), Water Vapour (WV) estimation maps and a Scene Classification (SCL) map including cloud / cloud shadow classes for further processing. Sen2Cor processor can be downloaded from ESA website as a standalone tool for individual Level-2A processing by the users. It can be run either from command line or as a plugin of the Sentinel-2 Toolbox (SNAP-S2TBX). In parallel, ESA started in June 2017 to use Sen2Cor for systematic Level-2A processing of Sentinel-2 acquisitions over Europe. Since March 2018, Level-2A products are generated by the official Sentinel-2 ground segment (PDGS) and are available on the Copernicus Open Access Hub. The objective of this presentation is to provide users with an overview of the Level-2A product contents and up-to-date information about the data quality of the Level-2A products (processing baseline >= PB.02.12) generated by Sentinel-2 PDGS since May 2019, in terms of Cloud Screening and Atmospheric Correction. In addition, the presentation will give an outlook on the upcoming updates of Sen2Cor which will improve L2A Data Quality: updated L2A metadata, updated scene classification, updated fall-back method using meteorological information from the Copernicus Atmosphere Monitoring Service, updated Copernicus DEM

Comparison of the Copernicus Sentinel-2 L2A Core Product distributed by ESA and the Sen2Cor Toolbox ‘user-generated’ product

Sen2Cor is a Level-2A (L2A) processor whose main purpose is to correct mono-temporal Copernicus Sentinel-2 (S2) mission Level-1C (L1C) products from the effects of the atmosphere in order to deliver radiometrically corrected Bottom-of-Atmosphere (BOA) data. Byproducts are Aerosol Optical Thickness (AOT), Water Vapour (WV) and Scene Classification (SCL) maps. The Sen2Cor Toolbox can be downloaded from the ESA website for autonomous processing of S2 L1C data by the users, thus generating BOA products here referred as ‘user’ products. In parallel, Sen2Cor is used for systematic processing of Sentinel-2 L1C data thus generating the S2 L2A products systematically distributed to users. Operational global L2A processing with the integration of Sen2Cor in the Sentinel-2 Ground Segment started in December 2018. These S2 L2A core products can be downloaded from the Copernicus SciHub. The S2 L2A core products agree with ‘user’ products as long as both are generated with the same Sen2Cor version and configuration. However, sometimes new versions of Sen2Cor Toolbox are released to public at a different time than their implementation in the S2 Ground Segment processing chain. This may result in a disagreement between ‘user’ and core products for some time. Additionally, differences between the outcome of the two processing lines can arise due to the DEM used and different minor patches included. Patches are usually included faster in the ESA-L2A core products. Whereas ESA-L2A core products have been using Planet-DEM for a while, the current DEM available to the users is SRTM-DEM. However, the new Copernicus DEM is now available also to users and is going to be used also to generate the S2 L2A core products, this limiting those differences. Both production lines are compared for what concerns AOT and WV maps, and BOA outputs per band. Both AOT and WV retrievals are validated by comparison with reference data provided by AERONET sunphotometers. Comparison of BOA outputs is performed by statistical metrics for pixelby-pixel differences between both processing lines over 9 km x 9 km areas. The influence of the different DEMs on resulting BOA will be discussed. ESA-L2A core products are generated with default configuration values, which are suitable for most situations of this operational mission. ‘User’ production gives the opportunity to apply individual configuration settings, which may prove favourable in some cases. This will be demonstrated on a few examples