Programmed cell death in protozoa

Tijekom zadnjih 10-15 godina, znatan pomak je napravljen u razumijevanju kako pojedine programirane stanične smrti u parazitskim praživotinjama mogu utjecati na njihovu biologiju i njihove interakcije s domaćinima i vektorima. Pojava markera koji pokazuju apoptozu praživotinja ili autofagiju tijekom pojedinih procesa kao i funkcionalni dokazi su poduprli mišljenje da ove programirane stanične smrti igraju bitnu ulogu u načinu života parazitskih praživotinja. U suprotnosti, još nam je uvijek nepoznat učinak parazitske nekroze za parazitdomaćin interakcije i da li se on može pojaviti u reguliranom načinu kako je opisano kod višestaničnih organizama. Postoje dokazi da gustoća pojedinih parazita korelira s pojavom markera programirane stanične smrti. Mora se naglasiti, međutim, da definitivan dokaz koncepta regulacije gustoće parazita još uvijek nedostaje, posebno u uvjetima in vivo. Druga česta tema koja se pojavljuje iz navedenih primjera je da je autofagija uključena u diferencijaciju parazitskih praživotinja. Dodatno, autofagija i autofagijska stanična smrt su uobičajeni odgovori kada su paraziti izloženi okolišnom stresu. Autofagija u parazitskim praživotinjama, kao i u životinjama, primarno promiče opstanak, ali se može pretvoriti u programirani oblik smrti ako uvjeti okoliša premaše kapacitet stanice. Iako smo daleko od razumijevanja ovih procesa u potpunosti, apoptoza i autofagija su bitne kod parazitskih praživotinja i interakcija s njihovim domaćinima i vektorima. Detaljno znanje ovih mehanizama bi omogućilo efikasnu borbu protiv parazitskih praživotinja upotrebljavanjem njihovih vlastitih puteva smrti stanice.Over the past 10-15 years, considerable progress has been made in understanding how some of programmed cell death in parasitic protozoa may influence their biology and their interactions with hosts and vectors. The appereance of markers that show apoptosis or autophagy during certain processes and functional evidence support the view that this programmed cell death plays an important role in the lifestyle of parasitic protozoans. In contrast, the effect of parasitic necrosis of the parasite-host interactions and whether it can occur in a regulated manner as described in metazoa is still unknown. Now there is good evidence that the density of individual trypanosomatids and apicomplexan parasites correlates with the appearance of markers of programmed cell death. It should be noted, however, that definitive proof of the concept of regulation of parasite density by parasite apoptosis is still lacking, especially in vivo. Another common theme that appears from these examples is that autophagy is involved in differentiation of parasitic protozoans. In addition, autophagy and autophagic cell death are common responses when the parasites encounter environmental stress. It has become clear that autophagy in parasitic protozoa, as in metazoan, primarily promotes survival, but can be converted to a form of programmed cell death if environmental conditions exceed the capacity of cells. Although we are far from understanding these processes fully, apoptosis and autophagy are essential for the parasitic protozoa and interactions with their hosts and vectors. A detailed knowledge of the underlying molecular mechanism might open the possibility to combat protozoan parasites efficiently by promoting their own death pathways

Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale

Current and upcoming Sun-Induced chlorophyll Fluorescence (SIF) satellite products (e.g., GOME, TROPOMI, OCO, FLEX) have medium-to-coarse spatial resolutions (i.e., 0.3–80 km) and integrate radiances from different sources into a single ground surface unit (i.e., pixel). However, intrapixel heterogeneity, i.e., different soil and vegetation fractional cover and/or different chlorophyll content or vegetation structure in a fluorescence pixel, increases the challenge in retrieving and quantifying SIF. High spatial resolution Sentinel-2 (S2) data (20 m) can be used to better characterize the intrapixel heterogeneity of SIF and potentially extend the application of satellite-derived SIF to heterogeneous areas. In the context of the COST Action Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), in which this study was conducted, we proposed direct (i.e., spatial heterogeneity coefficient, standard deviation, normalized entropy, ensemble decision trees) and patch mosaic (i.e., local Moran’s I) approaches to characterize the spatial heterogeneity of SIF collected at 760 and 687 nm (SIF760 and SIF687, respectively) and to correlate it with the spatial heterogeneity of selected S2 derivatives. We used HyPlant airborne imagery acquired over an agricultural area in Braccagni (Italy) to emulate S2-like top-of-the-canopy reflectance and SIF imagery at different spatial resolutions (i.e., 300, 20, and 5 m). The ensemble decision trees method characterized FLEX intrapixel heterogeneity best (R2 > 0.9 for all predictors with respect to SIF760 and SIF687). Nevertheless, the standard deviation and spatial heterogeneity coefficient using k-means clustering scene classification also provided acceptable results. In particular, the near-infrared reflectance of terrestrial vegetation (NIRv) index accounted for most of the spatial heterogeneity of SIF760 in all applied methods (R2 = 0.76 with the standard deviation method; R2 = 0.63 with the spatial heterogeneity coefficient method using a scene classification map with 15 classes). The models developed for SIF687 did not perform as well as those for SIF760, possibly due to the uncertainties in fluorescence retrieval at 687 nm and the low signal-to-noise ratio in the red spectral region. Our study shows the potential of the proposed methods to be implemented as part of the FLEX ground segment processing chain to quantify the intrapixel heterogeneity of a FLEX pixel and/or as a quality flag to determine the reliability of the retrieved fluorescence

Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale

Current and upcoming Sun-Induced chlorophyll Fluorescence (SIF) satellite products (e.g., GOME, TROPOMI, OCO, FLEX) have medium-to-coarse spatial resolutions (i.e., 0.3–80 km) and integrate radiances from different sources into a single ground surface unit (i.e., pixel). However, intrapixel heterogeneity, i.e., different soil and vegetation fractional cover and/or different chlorophyll content or vegetation structure in a fluorescence pixel, increases the challenge in retrieving and quantifying SIF. High spatial resolution Sentinel-2 (S2) data (20 m) can be used to better characterize the intrapixel heterogeneity of SIF and potentially extend the application of satellite-derived SIF to heterogeneous areas. In the context of the COST Action Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), in which this study was conducted, we proposed direct (i.e., spatial heterogeneity coefficient, standard deviation, normalized entropy, ensemble decision trees) and patch mosaic (i.e., local Moran’s I) approaches to characterize the spatial heterogeneity of SIF collected at 760 and 687 nm (SIF760 and SIF687, respectively) and to correlate it with the spatial heterogeneity of selected S2 derivatives. We used HyPlant airborne imagery acquired over an agricultural area in Braccagni (Italy) to emulate S2-like top-of-the-canopy reflectance and SIF imagery at different spatial resolutions (i.e., 300, 20, and 5 m). The ensemble decision trees method characterized FLEX intrapixel heterogeneity best (R2 &gt; 0.9 for all predictors with respect to SIF760 and SIF687). Nevertheless, the standard deviation and spatial heterogeneity coefficient using k-means clustering scene classification also provided acceptable results. In particular, the near-infrared reflectance of terrestrial vegetation (NIRv) index accounted for most of the spatial heterogeneity of SIF760 in all applied methods (R2 = 0.76 with the standard deviation method; R2 = 0.63 with the spatial heterogeneity coefficient method using a scene classification map with 15 classes). The models developed for SIF687 did not perform as well as those for SIF760, possibly due to the uncertainties in fluorescence retrieval at 687 nm and the low signal-to-noise ratio in the red spectral region. Our study shows the potential of the proposed methods to be implemented as part of the FLEX ground segment processing chain to quantify the intrapixel heterogeneity of a FLEX pixel and/or as a quality flag to determine the reliability of the retrieved fluorescence.</p

Macrophyte vegetation of the middle course of Mreznica river

Makrofiti su dobri indikatori stanja rijeke jer brzo odgovaraju na promjene u fizikalno-kemijskim svojstvima, brzini ili razini vode. Cilj ovog rada bio je istražiti sastav i strukturu makrofitske vegetacije srednjeg toka rijeke Mrežnice i ocijeniti njeno biološko stanje. Istraživano je ukupno 14 lokaliteta i zabilježeno ukupno 48 makrofitskih vrsta. Najviše je bilo zakorijenjenih vaskularnih biljaka koje rastu u sporijim i dubljim dijelovima rijeke i pojavljuju se u više morfoloških oblika, a svrstavamo ih u Sparganium emersum zajednicu. Mahovine su s najvećom pokrovnošću rasle na sedrenim barijerama, a pripadale su Berula-Agrostis zajednici koja je značajna za sedrene slapove. Na ovom tipu staništa bila je i najveća bioraznolikost i homogenost makrofitske vegetacije. Ukupno je bilo devet vrsta zaštićenih na nacionalnoj razini, jedna je vrsta bila strogo zaštićena sa statusom ugrožene svojte (EN), a jedna vrsta imala je status nedovoljno poznate (DD) prema kriterijima Međunarodne unije za očuvanje prirode (IUCN). Na temelju analize makrofita, biološko stanje srednjeg toka Mrežnice svrstava se u vrlo dobro stanje (klasa 1). Ovaj rad pomnije istražuje makrofitsku vegetaciju rijeke Mrežnice i može poslužiti za daljnji monitoring koji je važan za izradu plana upravljanja i zaštite.Macrophytes are good indicators of the river condition because they respond quickly to the changes in physico-chemical parameters, velocity or the level of water. The aim of this study was to investigate the composition and structure of macrophyte vegetation of the middle course of the Mrežnica River and evaluate its biological condition. A total of 14 sites were investigated resulting in 48 recorded macrophyte species. The majority of species were rooted vascular plants that grow in slower and deeper parts of the river and appear in several morphological forms that are classified in Sparganium emersum community. The highest coverage of mosses was found on travertine barriers. They belonged to the Berula-Agrostis community that is significant for travertine waterfalls. This type of habitat had the highest biodiversity and homogeneity of macrophyte vegetation. Nine species were protected at the national level, while one was strictly protected with the status of endangered species (EN), and one was classified in the data-deficient category (DD) according to the criteria of the International Union for Conservation of Nature (IUCN). Based on the analysis of macrophytes, the biological condition of the middle course of the Mrežnica River is classified as “in very good condition” (class 1). This work closely examined the macrophyte vegetation of Mrežnica and can be used for future monitoring, which is important for the development of the management and protection plans

Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale

Current and upcoming Sun-Induced chlorophyll Fluorescence (SIF) satellite products (e.g., GOME, TROPOMI, OCO, FLEX) have medium-to-coarse spatial resolutions (i.e., 0.3–80 km) and integrate radiances from different sources into a single ground surface unit (i.e., pixel). However, intrapixel heterogeneity, i.e., different soil and vegetation fractional cover and/or different chlorophyll content or vegetation structure in a fluorescence pixel, increases the challenge in retrieving and quantifying SIF. High spatial resolution Sentinel-2 (S2) data (20 m) can be used to better characterize the intrapixel heterogeneity of SIF and potentially extend the application of satellite-derived SIF to heterogeneous areas. In the context of the COST Action Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), in which this study was conducted, we proposed direct (i.e., spatial heterogeneity coefficient, standard deviation, normalized entropy, ensemble decision trees) and patch mosaic (i.e., local Moran’s I) approaches to characterize the spatial heterogeneity of SIF collected at 760 and 687 nm (SIF760 and SIF687, respectively) and to correlate it with the spatial heterogeneity of selected S2 derivatives. We used HyPlant airborne imagery acquired over an agricultural area in Braccagni (Italy) to emulate S2-like top-of-the-canopy reflectance and SIF imagery at different spatial resolutions (i.e., 300, 20, and 5 m). The ensemble decision trees method characterized FLEX intrapixel heterogeneity best (R2 > 0.9 for all predictors with respect to SIF760 and SIF687). Nevertheless, the standard deviation and spatial heterogeneity coefficient using k-means clustering scene classification also provided acceptable results. In particular, the near-infrared reflectance of terrestrial vegetation (NIRv) index accounted for most of the spatial heterogeneity of SIF760 in all applied methods (R2 = 0.76 with the standard deviation method; R2 = 0.63 with the spatial heterogeneity coefficient method using a scene classification map with 15 classes). The models developed for SIF687 did not perform as well as those for SIF760, possibly due to the uncertainties in fluorescence retrieval at 687 nm and the low signal-to-noise ratio in the red spectral region. Our study shows the potential of the proposed methods to be implemented as part of the FLEX ground segment processing chain to quantify the intrapixel heterogeneity of a FLEX pixel and/or as a quality flag to determine the reliability of the retrieved fluorescence