Numerička analiza cirkulacije u sjevernom Jadranu za vrijeme dvije uzastopne epizode bure

The north Adriatic circulation during two successive bora episodes was studied using single point current measurements and results of the Princeton Ocean Model (POM). Surface currents recorded at the gas field Ivana positioned in the central part of the northern Adriatic were directed downwind during the bora episode of 8-11 February 1984, whereas during the next bora period of 12-16 February 1984 they were directed upwind. The observed current reversal was reproduced in the numerical experiment controlled by air-sea fluxes and river inflows. Atmospheric forcing for the POM model was estimated from high resolution Mesoscale Model 5 (MM5) surface fields, while river discharges were introduced in the simulations as source terms of zero salinity in the continuity equation. The baseline experiment with realistic forcings, atmospheric and riverine, related the current reversal to displacements of the bora induced gyres. Sensitivity studies revealed a dominant role of the wind stress curl for the vorticity in the current field and showed the relevance of the changing wind divergence for the cross-shore circulation variability and accompanied current reversal. Additional numerical experiments emphasized even more the role of the spatial wind variability for the recorded flow and also stressed the importance of the model domain size for the numerical results.Površinsko strujanje u sjevernom Jadranu za vrijeme trajanja dvije uzastopne epizode bure analizirano je na temelju strujomjernih podatka i rezultata numeričkog modela Princeton Ocean Model (POM). Površinske struje zabilježene na plinskom polju Ivana smještenom u centralnom dijelu sjevernog Jadrana bile su u smjeru vjetra za vrijeme prve epizode bure od 8. do 11. veljače 1984., dok su za vrijeme slijedeće epizode bure od 12. do 16. veljače bile suprotnog smjera od vjetra. Zabilježeni obrat struje reproduciran je u numeričkom eksperimentu u kojem je oceanografski model kontroliran površinskom napetošću vjetra, protocima topline i riječnim dotocima. Atmosfersko prisilno djelovanje za POM model izračunato je na temelju prizemnih polja iz meteorološkog mod- ela Mesoscale Model 5 (MM5), dok je djelovanje sjevernojadranskih rijeka uvedeno u numeričke eksperimente kao izvor slatke vode u jednadžbi kontinuiteta. Rezultati osnovnog eksperimenta s realističnim prisilnim djelovanjima, atmosferskim i riječnim, povezali su obrat površinske struje naplinskom polju Ivana s pomacima burom induciranih vrtloga. Analize osjetljivosti numeričkih rezul -tata ukazale su na dominantnu ulogu rotora vjetra za vrtložnost u strujnom polju, dok je promjenjiva divergencija u polju vjetra utjecala na varijabilnost struja u smjeru okomitom na obalu te tako dovela do obrata površinske cirkulacije na poziciji plinskog polja Ivana. Dodatni numerički eksperimenti još su više naglasili važnost prostorne varijabilnosti vjetra za zabilježeni obrat struje, a također su pokazali i važnost veličine modelske domene za dobivene rezultate

Imaging Features of Triple N Negative Breast Cancers – Mammography, Ultrasound and Magnetic Resonance Imaging

Breast cancer (BC) is a highly heterogeneous disease. Aim was to evaluate imaging features of triple negative breast cancers (TNBC) in comparison to non-TNBC. We reviewed data of 30 patients who had been diagnosed as having TNBC and 37 patients with non-TNBC (control group) using criteria described for mammography (MMG), ultrasound (US) and magnetic resonance imaging (MRI) in Breast Imaging-Reporting and Data System (BI-RADS) lexicon for image interpretation. Age of patients, size of tumor, multifocality, histological type, tumor grade and status of lymph nodes were reviewed. TNBC were more often histological grade 3 and had significantly more positive lymph nodes at the time of diagnosis on pathology reports. On MMG, US and MRI TNBC mostly appeared as regularly shaped masses. On US as hypoechogenic masses with no posterior acoustic features and on MRI as masses with rim type of enhancement, fast wash-in and plateau type of curves. Most frequent category reported after MMG and US was BI RADS 4, and after MRI BI RADS 5. In conclusion, our study confirmed higher histological grade of TNBC, as well as more frequent lymph node involvement in comparation to the non-TNBC. TNBC showed tendency to affect younger women and to be larger than non-TNBC. Although, they most often presented as a mass on mammography and sonography, in a significant number of cases they remained miscategorized, due to the benign imaging features. All cases are recognized on MRI where they appear as rim enhancing masses

Application of Lagrangian methods in numerical modelling of dispersion in the Adriatic sea

Jedan od nedovoljno istraženih problema u morskoj ekologiji je mehanizam transporta čestica prirodnog odnosno antropogenog podrijetla. U rješavanju problema rasprostiranja tvari u moru danas se sve više koriste lagrangeovske metode. Lagrangeovski stohastički disperzijski modeli koriste veliki broj čestica kako bi simulirali transport i disperziju tvari iz izvora pod utjecajem trodimenzionalnih polja izračunatih oceanografskim modelima. Langrangeovske koherentne strukture (eng. Lagrangian coherent structures; LCS) su analitički alat koji otkriva, ne uvijek očite, mehanizme transporta u fluidima. Upotrebom numeričkog oceanografskog modela ROMS, lagrangeovskog individualno temeljenog modela (ITM) Ichthyop i proračuna LCS-ova metodom Ljapunovljevog eksponenta konačnog vremena (eng. finite-time Lyapunov exponent; FTLE) pokazala se povezanost jedinki riba ulovljenih u srednjem i južnom Jadranu s pretpostavljenim lokacijama mrijesta vezanim uz uzgajališta. Navedena metodologija primijenjena je na dva slučaja u kojima je prethodno utvrđena genetska povezanost. Prvi slučaj je potraga za mogućom lokacijom mrijesta jedinki tune koje su uhvaćene južno od otoka Mljeta 1. rujna 2011. godine. Ljeto 2011. je bilo karakterizirano obratom istočnojadranske struje u površinskom sloju i opaženim spontanim mrijestom odraslih tuna u uzgajalištima. Drugi slučaj je povezivanje lokacije mrijesta s lokacijama rastilišta ranih stadija komarče tijekom prvih pet mjeseci 2016. godine. Lokacije mrijesta su u blizini uzgajališta uz obale Brača i Ugljana, dok su rastilišta u boćatim područjima Neretve, Pantana i Raše. Prostorna raspodjela čestica u obje simulacije je pratila dinamiku strujanja u Jadranskom moru koje je prevladavalo u analiziranom razdoblju. Privlačni i odbojni LCS-ovi su se podudarali s područjima najvećih koncentracija čestica, odnosno omeđivali su ih od područja do kojih čestice nisu mogle biti transportirane. Korištenje ITM-ova i LCS-ova omogućuje prepoznavanje područja u kojem se odvija najveći dio transporta ihtioplanktona.Extended abstract in thesi

Table_2_Cataloguing environmental influences on the spatiotemporal variability of Adriatic anchovy early life stages in the eastern Adriatic Sea using an artificial neural network.docx

The anchovy (Engraulis encrasicolus, Linnaeus, 1758), one of the most important small pelagic fish species in the Adriatic, is currently described as a species that can be considered overfished. From 2013 to 2020, samples of anchovy eggs and larvae were collected through scientific surveys during the summer months. The collected ichthyoplankton data were combined with environmental data (measured satellite sea surface temperature and chlorophyll data, numerically simulated salinity, maps of primary production) to identify anchovy spawning habitats and environmental conditions affecting the anchovy early life stages. For this large dataset, a nonlinear method called Growing Neural Gas Network analysis was used to explain the multiple dependencies between anchovy and the explanatory environmental variables and represent them in 9 patterns called Best Matching Unit (BMU). Obtained values of anchovy early life stages abundances (eggs/m2; larvae/m2) showed a clear negative trend, which was easily observed both in the time series and in the annual spatial distributions. Among all measured environmental parameters that were previously mentioned, salinity showed a significant increase, which can be attributed to the cyclonic phase of the bimodal oscillatory system of the Adriatic and Ionian Seas. The calculated BMUs showed several interesting results that shed new light on previous findings: (a) there is a split between the richer northern and poorer southern parts of the Adriatic in terms of anchovy eggs and larvae abundances, (b) the Kvarner Bay, the west coast of Istria and the area around Dugi otok are consistently rich spawning grounds, (c) decreased abundance in the southern areas is a result of the influence of salinity, (d) an increase in chlorophyll can lead to an increase in egg count, (e) the positive effects of upwelling can be negated by an increase in salinity, (f) increased primary production is followed by increased egg count. Upwelling, as one of the factors that can influence larval and egg abundance by bringing nutrients up from the seafloor, showed increased spatial and temporal variability during the investigated period, which depended on the wind regime. Our analysis showed that neural network analysis can successfully describe the effects and interplay of environmental factors on the abundance of anchovy early life stages.</p

Image_4_Cataloguing environmental influences on the spatiotemporal variability of Adriatic anchovy early life stages in the eastern Adriatic Sea using an artificial neural network.tif

The anchovy (Engraulis encrasicolus, Linnaeus, 1758), one of the most important small pelagic fish species in the Adriatic, is currently described as a species that can be considered overfished. From 2013 to 2020, samples of anchovy eggs and larvae were collected through scientific surveys during the summer months. The collected ichthyoplankton data were combined with environmental data (measured satellite sea surface temperature and chlorophyll data, numerically simulated salinity, maps of primary production) to identify anchovy spawning habitats and environmental conditions affecting the anchovy early life stages. For this large dataset, a nonlinear method called Growing Neural Gas Network analysis was used to explain the multiple dependencies between anchovy and the explanatory environmental variables and represent them in 9 patterns called Best Matching Unit (BMU). Obtained values of anchovy early life stages abundances (eggs/m2; larvae/m2) showed a clear negative trend, which was easily observed both in the time series and in the annual spatial distributions. Among all measured environmental parameters that were previously mentioned, salinity showed a significant increase, which can be attributed to the cyclonic phase of the bimodal oscillatory system of the Adriatic and Ionian Seas. The calculated BMUs showed several interesting results that shed new light on previous findings: (a) there is a split between the richer northern and poorer southern parts of the Adriatic in terms of anchovy eggs and larvae abundances, (b) the Kvarner Bay, the west coast of Istria and the area around Dugi otok are consistently rich spawning grounds, (c) decreased abundance in the southern areas is a result of the influence of salinity, (d) an increase in chlorophyll can lead to an increase in egg count, (e) the positive effects of upwelling can be negated by an increase in salinity, (f) increased primary production is followed by increased egg count. Upwelling, as one of the factors that can influence larval and egg abundance by bringing nutrients up from the seafloor, showed increased spatial and temporal variability during the investigated period, which depended on the wind regime. Our analysis showed that neural network analysis can successfully describe the effects and interplay of environmental factors on the abundance of anchovy early life stages.</p

Image_5_Cataloguing environmental influences on the spatiotemporal variability of Adriatic anchovy early life stages in the eastern Adriatic Sea using an artificial neural network.tif

The anchovy (Engraulis encrasicolus, Linnaeus, 1758), one of the most important small pelagic fish species in the Adriatic, is currently described as a species that can be considered overfished. From 2013 to 2020, samples of anchovy eggs and larvae were collected through scientific surveys during the summer months. The collected ichthyoplankton data were combined with environmental data (measured satellite sea surface temperature and chlorophyll data, numerically simulated salinity, maps of primary production) to identify anchovy spawning habitats and environmental conditions affecting the anchovy early life stages. For this large dataset, a nonlinear method called Growing Neural Gas Network analysis was used to explain the multiple dependencies between anchovy and the explanatory environmental variables and represent them in 9 patterns called Best Matching Unit (BMU). Obtained values of anchovy early life stages abundances (eggs/m2; larvae/m2) showed a clear negative trend, which was easily observed both in the time series and in the annual spatial distributions. Among all measured environmental parameters that were previously mentioned, salinity showed a significant increase, which can be attributed to the cyclonic phase of the bimodal oscillatory system of the Adriatic and Ionian Seas. The calculated BMUs showed several interesting results that shed new light on previous findings: (a) there is a split between the richer northern and poorer southern parts of the Adriatic in terms of anchovy eggs and larvae abundances, (b) the Kvarner Bay, the west coast of Istria and the area around Dugi otok are consistently rich spawning grounds, (c) decreased abundance in the southern areas is a result of the influence of salinity, (d) an increase in chlorophyll can lead to an increase in egg count, (e) the positive effects of upwelling can be negated by an increase in salinity, (f) increased primary production is followed by increased egg count. Upwelling, as one of the factors that can influence larval and egg abundance by bringing nutrients up from the seafloor, showed increased spatial and temporal variability during the investigated period, which depended on the wind regime. Our analysis showed that neural network analysis can successfully describe the effects and interplay of environmental factors on the abundance of anchovy early life stages.</p

Image_3_Cataloguing environmental influences on the spatiotemporal variability of Adriatic anchovy early life stages in the eastern Adriatic Sea using an artificial neural network.tif

The anchovy (Engraulis encrasicolus, Linnaeus, 1758), one of the most important small pelagic fish species in the Adriatic, is currently described as a species that can be considered overfished. From 2013 to 2020, samples of anchovy eggs and larvae were collected through scientific surveys during the summer months. The collected ichthyoplankton data were combined with environmental data (measured satellite sea surface temperature and chlorophyll data, numerically simulated salinity, maps of primary production) to identify anchovy spawning habitats and environmental conditions affecting the anchovy early life stages. For this large dataset, a nonlinear method called Growing Neural Gas Network analysis was used to explain the multiple dependencies between anchovy and the explanatory environmental variables and represent them in 9 patterns called Best Matching Unit (BMU). Obtained values of anchovy early life stages abundances (eggs/m2; larvae/m2) showed a clear negative trend, which was easily observed both in the time series and in the annual spatial distributions. Among all measured environmental parameters that were previously mentioned, salinity showed a significant increase, which can be attributed to the cyclonic phase of the bimodal oscillatory system of the Adriatic and Ionian Seas. The calculated BMUs showed several interesting results that shed new light on previous findings: (a) there is a split between the richer northern and poorer southern parts of the Adriatic in terms of anchovy eggs and larvae abundances, (b) the Kvarner Bay, the west coast of Istria and the area around Dugi otok are consistently rich spawning grounds, (c) decreased abundance in the southern areas is a result of the influence of salinity, (d) an increase in chlorophyll can lead to an increase in egg count, (e) the positive effects of upwelling can be negated by an increase in salinity, (f) increased primary production is followed by increased egg count. Upwelling, as one of the factors that can influence larval and egg abundance by bringing nutrients up from the seafloor, showed increased spatial and temporal variability during the investigated period, which depended on the wind regime. Our analysis showed that neural network analysis can successfully describe the effects and interplay of environmental factors on the abundance of anchovy early life stages.</p

Image_1_Cataloguing environmental influences on the spatiotemporal variability of Adriatic anchovy early life stages in the eastern Adriatic Sea using an artificial neural network.tif

The anchovy (Engraulis encrasicolus, Linnaeus, 1758), one of the most important small pelagic fish species in the Adriatic, is currently described as a species that can be considered overfished. From 2013 to 2020, samples of anchovy eggs and larvae were collected through scientific surveys during the summer months. The collected ichthyoplankton data were combined with environmental data (measured satellite sea surface temperature and chlorophyll data, numerically simulated salinity, maps of primary production) to identify anchovy spawning habitats and environmental conditions affecting the anchovy early life stages. For this large dataset, a nonlinear method called Growing Neural Gas Network analysis was used to explain the multiple dependencies between anchovy and the explanatory environmental variables and represent them in 9 patterns called Best Matching Unit (BMU). Obtained values of anchovy early life stages abundances (eggs/m2; larvae/m2) showed a clear negative trend, which was easily observed both in the time series and in the annual spatial distributions. Among all measured environmental parameters that were previously mentioned, salinity showed a significant increase, which can be attributed to the cyclonic phase of the bimodal oscillatory system of the Adriatic and Ionian Seas. The calculated BMUs showed several interesting results that shed new light on previous findings: (a) there is a split between the richer northern and poorer southern parts of the Adriatic in terms of anchovy eggs and larvae abundances, (b) the Kvarner Bay, the west coast of Istria and the area around Dugi otok are consistently rich spawning grounds, (c) decreased abundance in the southern areas is a result of the influence of salinity, (d) an increase in chlorophyll can lead to an increase in egg count, (e) the positive effects of upwelling can be negated by an increase in salinity, (f) increased primary production is followed by increased egg count. Upwelling, as one of the factors that can influence larval and egg abundance by bringing nutrients up from the seafloor, showed increased spatial and temporal variability during the investigated period, which depended on the wind regime. Our analysis showed that neural network analysis can successfully describe the effects and interplay of environmental factors on the abundance of anchovy early life stages.</p

Image_2_Cataloguing environmental influences on the spatiotemporal variability of Adriatic anchovy early life stages in the eastern Adriatic Sea using an artificial neural network.tif

The anchovy (Engraulis encrasicolus, Linnaeus, 1758), one of the most important small pelagic fish species in the Adriatic, is currently described as a species that can be considered overfished. From 2013 to 2020, samples of anchovy eggs and larvae were collected through scientific surveys during the summer months. The collected ichthyoplankton data were combined with environmental data (measured satellite sea surface temperature and chlorophyll data, numerically simulated salinity, maps of primary production) to identify anchovy spawning habitats and environmental conditions affecting the anchovy early life stages. For this large dataset, a nonlinear method called Growing Neural Gas Network analysis was used to explain the multiple dependencies between anchovy and the explanatory environmental variables and represent them in 9 patterns called Best Matching Unit (BMU). Obtained values of anchovy early life stages abundances (eggs/m2; larvae/m2) showed a clear negative trend, which was easily observed both in the time series and in the annual spatial distributions. Among all measured environmental parameters that were previously mentioned, salinity showed a significant increase, which can be attributed to the cyclonic phase of the bimodal oscillatory system of the Adriatic and Ionian Seas. The calculated BMUs showed several interesting results that shed new light on previous findings: (a) there is a split between the richer northern and poorer southern parts of the Adriatic in terms of anchovy eggs and larvae abundances, (b) the Kvarner Bay, the west coast of Istria and the area around Dugi otok are consistently rich spawning grounds, (c) decreased abundance in the southern areas is a result of the influence of salinity, (d) an increase in chlorophyll can lead to an increase in egg count, (e) the positive effects of upwelling can be negated by an increase in salinity, (f) increased primary production is followed by increased egg count. Upwelling, as one of the factors that can influence larval and egg abundance by bringing nutrients up from the seafloor, showed increased spatial and temporal variability during the investigated period, which depended on the wind regime. Our analysis showed that neural network analysis can successfully describe the effects and interplay of environmental factors on the abundance of anchovy early life stages.</p