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274 results for “baltic sea”
Fig. 1 in Heartworm and seal louse: Trends in prevalence, characterisation of impact and transmission pathways in a unique parasite assembly on seals in the North and Baltic Sea
Fig. 1. Levels of infection with E. horridus in P. vitulina. A: Mild E. horridus infection of a harbour seal yearling, asterisk pointing at E. horridus B: Close up of E. horridus in the head area of a harbour seal C: Severe E. horridus infection of a harbour seal D: Close up of severe E. horridus infection. Scale bars: A-D 1 cm.
Fig. 5 in Prevalence and molecular characterisation of Acanthocephala in pinnipedia of the North and Baltic Seas
Fig. 5. Maximum likelihood tree based on COI sequences using the Jones-TaylorThornton (JTT) model. The log likelihood is −2479.70. The percentage of trees based on 1000 bootstrap replicates in which the associated taxa clustered together is shown next to the branches. GenBank accession numbers of analysed amino acid sequences are listed in Table 2.
Fig. 1 in Prevalence and molecular characterisation of Acanthocephala in pinnipedia of the North and Baltic Seas
Fig. 1. Prevalence of acanthocephalan infections in harbour and grey seals from the German North and Baltic Seas between 1996 and 2012. Notations indicate the Acanthocephala positive and total number of examined Phoca vitulina (Pv) and Halichoerus grypus (Hg). Connecting lines indicate statistically significant differences between annual prevalences after Holm–Bonferroni correction (P ≤ 0.001).
Fig. 3 in Prevalence and molecular characterisation of Acanthocephala in pinnipedia of the North and Baltic Seas
Fig. 3. Phylogenetic analysis of the ribosomal ITS1-5.8S-ITS2-complex using the Maximum Likelihood method based on the Kimura 2-parameter model. The log likelihood is −16,028.00. The percentage of trees based on 1000 bootstrap replicates in which the associated taxa clustered together is shown next to the branches. GenBank accession numbers of analysed nucleotide sequences are listed in Table 1.
Fig. 2 in Planktonic Ciliates of the Neva Estuary (Baltic Sea): Community Structure and Spatial Distribution
Fig. 2. Two groups of samples, distinguished by ordination (MDS) on the basis of similarity of the ciliate community structure (p <0.05). Upper and lower parts of the inner Neva Estuary (white and grey symbols) slightly differed by community structure (Global R = 0.163).
Fig. 1 in Planktonic Ciliates of the Neva Estuary (Baltic Sea): Community Structure and Spatial Distribution
Fig. 1. Scheme of the inner Neva Estuary and location of sampling stations; modified from Telesh et al. (2008). Broken line indicates the storm-surge barrier.
Fig. 1 in Beetles (Coleoptera) from seaside beach and dunes in the regions of Świnoujście, Międzyzdroje and Wisełka (Poland) located along the southern coast of the Baltic Sea
Fig. 1. The examined seaside beach and dunes in the regions of Świnoujście, Międzyzdroje and Wisełka.
Figure 9. Dipole eddy evolution from August 7 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 9. Dipole eddy evolution from August 7 to August 9, 2018 in the suspended matter field from OLCI Sentinel-3A data for August 7 (a) and August 8, 2018 (b) and MSI Sentinel-2B data for August 9, 2008 (c) according Krayushkin et al. (2018).
Figure 5 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 5. Spatial variations of optical, physical, chemical, and biological parameters along the coast of the Sambia Peninsula and the Curonian Spit. The yellow background corresponds to warm waters in eddies, the blue background corresponds to cold waters, and the grey background corresponds to waters outside eddies.
Figure 4 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 4. Photosynthetic active radiation attenuation coefficient (Kd) and inverse Secchi depth (D, circles) vs. turbidity and inverse Secchi depth (squares) at stations with cyanobacteria blooms (green symbols), under the influenced of cold water stations (blue symbols) and under the influenced of warm water stations (black symbols), respectively. Turbidity is given in units according to the turbidity standard for Formazine (Formazin Turbidity Unit, ftu).
Figure 3 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 3. Sea surface temperature (a) and chlorophyll a concentration (b) on August 22 (11:30 UTC), and sea surface temperature (c) on August 23, 2018 (12:10 UTC), all from MODIS-Aqua satellite data; (d) fragment of optical satellite image (red, green, blue composite) derived from the Ocean and Land Color Instrument (OLCI) on Sentinel-3A satellite from August 23, 2018 (9:25 UTС); (e) temperature (˚C) and salinity (f) transects along the northern coast of the Sambia Peninsula and Curonian Spit on August 23, 2018.
Figure 7 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 7. Vertical distribution of chlorophyll a concentration along the coastal area of the Sambia Peninsula on August 22, 2018.
Figure 8 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 8. Fragments of optical satellite images derived from OLCI Sentinel-2 on May 3, 2019 (a) and August 28, 2022 (b).
Figure 2 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 2. Study area and locations of station in the southeastern Baltic Sea (а) Conditional symbols: Yellow circles correspond to the stations conducted on August 22, 2018. Red circles correspond to the stations conducted on August 23, 2018. White circle is Wastewater Treatment Plant (WTP) on the northern coast of Sambia Peninsula. Amber Mining Plant (AC) is indicated as an asterisk on the western coast.
Figure 1 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 1. Phytoplankton patches on the surface of the southern Baltic Sea. Fragment of a color-synthesized image of the Baltic Sea surface in the visible range from OLCI-Sentinel-3 satellite scanner data of June 27, 2018.
Figure 6 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 6. Phytoplankton biomass contribution in the upper 1 m layer (station 16 and 24 – integrated samples over euphotic depth).
Fig. 1 in Anisakid nematode species identification in harbour porpoises (Phocoena phocoena) from the North Sea, Baltic Sea and North Atlantic using RFLP analysis
Fig. 1. RFLP profiles obtained by digestion of ITS1-5.8S-ITS2 region with the restriction enzymes HinfI, RsaI and HaeIII. a)-i) lane 1–5: Anisakid nematodes from harbour porpoises. j)-l) lane 1–3: A. simplex s. s. from North Sea, Baltic and Norwegian harbour porpoises; lane 4–6: P. decipiens s. s. from North Sea and Baltic harbour and grey seals; lane 7–9: C. osculatum s. s. from North Sea and Baltic harbour and grey seals. L: 100-bp ladder.
Fig. 9 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland
Fig. 9. Size differences between infected and non-infected cormorants. The black line represents the median length (a and b) and median weight (c). The grey box represents the middle 50% of the data (n = 65).
Fig. 7 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland
Fig. 7. Size differences of cormorants between Kustavi and Airisto in terms of body length and body weight (n = 65). The black line represents the median length and weight. The grey box represents the middle 50% of the data (n = 65).
Fig. 6 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland
Fig. 6. Left: Body length of herring in the Archipelago Sea (n = 1167) and the Bothnian Sea (n = 1528) in 2018 and in the infected and non-infected herring (n = 7002). The black line represents the median length, and the grey box is the middle 50% of the data.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.