Porównanie i ocena europejskich map pokrycia terenu o średniej i wysokiej rozdzielczości przestrzennej

W pracy przedstawiono podejście, które pozwala na ocenę dokładności europejskich map pokrycia terenu na poziomie regionów biogeograficznych oraz analizę wpływu struktury przestrzennej, kompozycji i konfiguracji pokrycia terenu, na dokładność przestrzenną i tematyczną. Dokładność tematyczną oszacowano dla danych Corine Land Cover 2006 (CLC2006) i Corine Land Cover 2012 (CLC2012), a także dla warstw High Resolution Layers (HRLs): Imerviousness 2006, 2012 i 2015, Forests 2012 i 2015, Grasslands 2015, oraz Water and Wetness 2015 na dwóch poziomach: europejskim i regionów biogeograficznych. Uzyskano wyższe ogólne dokładności dla danych HRL, jednak dokładności użytkownika i producenta znacznie się różnią pomiędzy zarówno produktami oraz regionami. Najniższe dokładności uzyskano dla regionu Alpejskiego, Borealnego oraz Śródziemnomorskiego. Obliczone dokładności były analizowane wraz z kompozycją klasy pokrycia terenu oraz konfiguracją reprezentowaną przez wskaźniki krajobrazowe: całkowita krawędź, całkowity obszar wnętrza oraz stosunek krawędzi do wnętrza. Otrzymane wysokie wartości wskaźnika stosunku krawędzi do wnętrza są związane z niską dokładnością producenta danej klasy, podczas gdy klasy obejmujące większe obszary (większy udział w kompozycji danego typu pokrycia terenu) są często kartowane z większą dokładnością (zarówno użytkownika, jak i producenta). Moje badania wykazały, że analiza struktury przestrzennej typów pokrycia terenu może pomóc w lepszym zrozumieniu wyników oceny dokładności map pokrycia terenu.This study presents an approach to assessing accuracy of European land cover maps consistently across the different biogeographical regions, and for exploring the effect of land cover spatial structure, composition and configuration, on spatial and thematic accuracy. The thematic accuracy is calculated for the Corine Land Cover 2006 (CLC2006) and Corine Land Cover 2012 (CLC2012), as well as for the High Resolution Layers (HRLs): Imperviousness 2006, 2012 and 2015, Forests 2012 and 2015, Grasslands 2015, and Water and Wetness 2015 on two levels, pan-European and bioregional. Overall accuracies are higher for the HRLs, however, the user’s and producer’s accuracies vary considerably. The lowest accuracies are achieved for the Alpine, Boreal, and Mediterranean biogeographical regions. Calculated accuracies are assessed together with the land cover class composition, and configuration represented by the total edge and total core areas, and edge to core ratio. High edge to core ratios are linked to a low producer’s accuracy of the class, whereas classes covering larger areas (higher share in the given class composition) are often mapped with better accuracy (both user’s and producer’s). My study showed that analysis of a spatial structure of land cover types can help in better understanding of the accuracy assessment results

The role of the central bank on the example of the Federal Reserve System

W pracy opisany został System Rezerwy Federalnej. Scharakteryzowano również poprzednie banki centralne Stanów Zjednoczonych, ich struktury i funkcje jakie spełniały. Przedstawiono narzędzia, przy pomocy których bank centralny USA oddziałuje na gospodarkę państwa i na gospodarki innych państw świata. W ostatnim rozdziale zaprezentowano postawę Fedu w odpowiedzi na Wielki Kryzys lat 30., szoki naftowe, kryzys z roku 2000 i lat 2007-2010. Praca opisuje rolę jaką odgrywa instytucja banku centralnego w państwie.In this work has been described the Federal Reserve System. The previous central banks of the United States, their structures and functions that were fulfilled were also characterized. It is presented tools, which are used by central bank in order to influence the country's economy and the economies of other countries. The last chapter presents the Fed's attitude in response to the Great Depression of the 1930s, oil shocks, the crisis of 2000 and the years 2007-2010. The work describes the role played by the institution of the central bank in the state

Spermatozoon-ultrastructure and processes leading to its maturation and functionality.

Plemniki to unikatowe komórki posiadające ojcowską informację genetyczną, biorące udział w rozmnażaniu płciowym. Dzięki poznaniu ultrastruktury plemników możliwe stało się zrozumienie procesu ich powstawania - spermatogenezy. Spermatogeneza jest złożonym procesem, zachodzącym w okresie dojrzewania płciowego w kanalikach plemnikotwórczych jądra, polegającym na wytworzeniu haploidalnych spermatyd z diploidalnych spermatogoniów. W procesie tym najważniejszą rolę pełnią komórki Sertoliego. Spermatogeneza regulowana jest przez wzajemne współdziałanie gonadotropin i hormonów steroidowych oraz lokalnie produkowanych czynników wzrostu i różnicowania komórek szlaku spermatogenezy. W czasie tego procesu istotne są także oddziaływania pomiędzy komórkami gonady, odbywające się przy pomocy specyficznych połączeń międzykomórkowych. Plemniki powstałe w procesie spermatogenezy nie wykazują zdolności do ruchu postępowego, dlatego są transportowane do najądrza, w którym znajduje się odpowiednie środowisko do ich dojrzewania. W najądrzu dochodzi do szeregu przemian biochemicznych, morfologicznych i funkcjonalnych, które regulowane są m. in. przez androgeny, w efekcie czego plemnik nabywa zdolności do zapłodnienia komórki jajowej. W związku ze wzrostem niepłodności wśród mężczyzn, w niniejszej pracy przedstawiono również główne mechanizmy związane z zaburzeniami funkcji spermatogenicznej w gonadzie męskiej.Spermatozoa are unique cells, having a paternal genetic information used for sexual reproduction. By understanding the ultrastructure of sperm it is possible to understand the process of their formation, called spermatogenesis. Spermatogenesis is a complex process that occurs during puberty in the seminiferous tubules, which involves the formation of haploid spermatids from diploid spermatogonia. In this process, the most important is the role of the Sertoli cells. Spermatogenesis is regulated by the mutual interaction of gonadotropins, steroid hormones and locally produced growth factors. Also important cell-cell interactions via specialized cell junction. Spermatozoa do not reveal the ability for progressive motility, therefore they are transported to the epididymis, where there is the appropriate microenvironment for their maturation. Here spermatozoa undergo numerous biochemical, morphological and functional changes during passage through the epidydimis and these change are regulated by androgens. This study presents the main mechanisms associated with dysfunctions of spermatogenesis, which are one of the reasons of the increasing infertility among men

Steroidogenesis in male mouse gonads with the lack of phosphodiesterase

Celem niniejszej pracy było wykazanie roli fosfodiesterazy 8B na ekspresję białek zaangażowanych w steroidogenezę w komórkach Leydiga in vivo. Badania prowadzono u myszy z nokautem PDE8B (KO) oraz myszy kontrolnych (K). Od myszy tych pobierano gonady, w obrębie których badano lokalizację i ekspresję białek zaangażowanych w proces steroidogenezy: białko natychmiastowo regulujące steroidogenezę (StAR), dehydrogenaza 3β-hydroksysteroidowa (3β-HSD), aromataza, insulinopodobny peptyd 3 (Insl3), receptor estrogenowy alfa (ERα). Lokalizację powyższych białek uwidoczniono przy pomocy barwienia immunohistochemicznego, natomiast do oceny ekspresji tych białek wykorzystano analizę Western Blot. Wszystkie wymienione powyżej białka zlokalizowano w komórkach Leydiga, zarówno u myszy KO, jak i u myszy K. Różnice dotyczyły intensywności reakcji immunohistochemicznej. U myszy KO zaobserwowano zmniejszoną ekspresję białek StAR, aromatazy i ERα, oraz zwiększoną ekspresję białka 3β-HSD. Ekspresja białka Insl3 pozostawała na takim samym poziomie w obu grupach eksperymentalnych. Na podstawie przeprowadzonych badań stwierdzono że, zwiększona ekspresja białka 3β-HSD u myszy KO może powodować zaburzenia ilościowe w produkcji hormonów steroidowych. Natomiast obniżona ekspresja białek StAR, aromatazy i ERα świadczy o pośredniej roli PDE8B lub/i zaangażowaniu innych PDEs w regulację funkcji jądra, głównie w transport cholesterolu i konwersję go do estrogenów. Dodatkowo niezmienna ekspresja białka Insl3, będącego funkcjonalnym markerem komórek Leydiga, wskazuje na prawidłowe ich różnicowanie, bez jakichkolwiek zaburzeń.Podsumowując, w gonadzie myszy funkcja StAR, 3β-HSD, aromatazy i ERα jest regulowana przez PDEs, podczas gdy regulacja funkcji Insl3 przebiega w inny sposób.The aim of the present study was to show the role of phosphodiesterase 8B in the regulation of steroidogenic proteins in Leydig cells in vivo. For the study PDE8B KO (KO) mice and wild type (K) mice have been used. Gonads from KO and K mice was isolated and used for evaluation of expression of proteins involved in the steroidogenesis: steroidogenic acute regulatory protein (StAR), 3β-hydroxysteroid dehydrogenase (3β-HSD), aromatase, insulin-like peptide 3 (Insl3), estrogen receptor alpha (ERα). Localization of these proteins was visualized by the use immunohistochemical staining, while for the evaluation of the expression of the proteins Western blot analysis was used.In the testes of KO and K mice the presence of all studied proteins has been confirmed. Differences were related to the intensity of immunohistochemical reaction. In KO mice decreased expression of StAR, aromatase and ERα, and increased expression of 3β-HSD was observed. Insl3 expression remained at the same level in both experimental groups.Based on the results of the present study, the increased expression of 3β-HSD in KO mice can be related to disturbances of sex hormone biosynthesis. In contrast, reduced expression of StAR, aromatase and ERα indicates on the indirect of effect of PDE8B or/and involvement other PDEs mechanism of testis regulation, mainly in the cholesterol transport and conversion to estrogens. In addition unchanged expression of Insl3, which is the functional Leydig cell marker indicates on the proper differentiation process of these cells.In conclusion, in the mouse gonad, the function of StAR, 3β-HSD, aromatase, ERα seems to be regulated by PDEs, while function of Insl3 is regulated in different manner

Exploring the relationship between temporal fluctuations in satellite nightlight imagery and human mobility across Africa

Mobile phone data have been increasingly used over the past decade or more as a pretty reliable indicator of human mobility to measure population movements and the associated changes in terms of population presence and density at multiple spatial and temporal scales. However, given the fact mobile phone data are not available everywhere and are generally difficult to access and share, mostly because of commercial restrictions and privacy concerns, more readily available data with global coverage, such as night-time light (NTL) imagery, have been alternatively used as a proxy for population density changes due to population movements. This study further explores the potential to use NTL brightness as a short-term mobility metric by analysing the relationship between NTL and smartphone-based Google Aggregated Mobility Research Dataset (GAMRD) data across twelve African countries over two periods: 2018–2019 and 2020. The data were stratified by a measure of the degree of urbanisation, whereby the administrative units of each country were assigned to one of eight classes ranging from low-density rural to high-density urban. Results from the correlation analysis, between the NTL Sum of Lights (SoL) radiance values and three different GAMRD-based flow metrics calculated at the administrative unit level, showed significant differences in NTL-GAMRD correlation values across the eight rural/urban classes. The highest correlations were typically found in predominantly rural areas, suggesting that the use of NTL data as a mobility metric may be less reliable in predominantly urban settings. This is likely due to the brightness saturation and higher brightness stability within the latter, showing less of an effect than in rural or peri-urban areas of changes in brightness due to people leaving or arriving. Human mobility in 2020 (during COVID-19-related restrictions) was observed to be significantly different than in 2018–2019, resulting in a reduced NTL-GAMRD correlation strength, especially in urban settings, most probably because of the monthly NTL SoL radiance values remaining relatively similar in 2018–2019 and 2020 and the human mobility, especially in urban settings, significantly decreasing in 2020 with respect to the previous considered period. The use of NTL data on its own to assess monthly mobility and the associated fluctuations in population density was therefore shown to be promising in rural and peri-urban areas but problematic in urban settings.</p

Global holiday datasets for understanding seasonal human mobility and population dynamics

Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010–2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements

Practical geospatial and sociodemographic predictors of human mobility

Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.</p