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Fig. 9. A – O.A in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Fig. 9. A – O.A. Scarlato – Director of the Zoological Institute. 1975. Archive of S.O. Scarlato; B – O.A. Scarlato at work in the mollusk collections of the Laboratory of Marine Research of the Zoological Institute. Photo By V.N. Tanasijtshuk. Archive of V.N. Tanasijtshuk.
Рис. 11. A – О.А. Скарлато выступает на 7-м малакологическом совеЩании. Слева направо: И.М. Лихарев, О.А. Скарлато, А.Н. Голиков, …. Апрель 1983 г. Архив А.В. Смирнова; B – Встреча на квартире О.А. Скарлато после 7-го малакологического совеЩаниЯ. Апрель 1983 г. Слева направо: …, ИльЯ Михайлович Лихарев, Орест Александрович Скарлато, Анита Алексеевна Нейман, …, …. Архив И.М. Лихарева. in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Рис. 11. A – О.А. Скарлато выступает на 7-м малакологическом совеЩании. Слева направо: И.М. Лихарев, О.А. Скарлато, А.Н. Голиков, …. Апрель 1983 г. Архив А.В. Смирнова; B – Встреча на квартире О.А. Скарлато после 7-го малакологического совеЩаниЯ. Апрель 1983 г. Слева направо: …, ИльЯ Михайлович Лихарев, Орест Александрович Скарлато, Анита Алексеевна Нейман, …, …. Архив И.М. Лихарева.
Рис. 7. A – Е.Н. ГруЗов, О.А. Скарлато и А.Н. Голиков на водолаЗных работах. Дальний Восток. 1962 или 1963 г. Архив А.А. Голикова; B – О.А. Скарлато готовитсЯ к погруЖению. ЮЖный Сахалин. 1963 г. Фото З.В. Кунцевич. in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Рис. 7. A – Е.Н. ГруЗов, О.А. Скарлато и А.Н. Голиков на водолаЗных работах. Дальний Восток. 1962 или 1963 г. Архив А.А. Голикова; B – О.А. Скарлато готовитсЯ к погруЖению. ЮЖный Сахалин. 1963 г. Фото З.В. Кунцевич.
Рис. 5. A – О.А. Скарлато и Зав. Кафедрой Зоологии беспоЗвоночных Ленинградского государственного университета, член-корр. АН СССР профессор В.А. Догель. Май 1955 г. Архив С.О. Скарлато; B – В.В. Хлебович, П.В. Ушаков, О.А. Скарлато и китайский Зоолог У Бао Лин. Конец 1950-х гг. Архив В.В. Хлебовича. in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Рис. 5. A – О.А. Скарлато и Зав. Кафедрой Зоологии беспоЗвоночных Ленинградского государственного университета, член-корр. АН СССР профессор В.А. Догель. Май 1955 г. Архив С.О. Скарлато; B – В.В. Хлебович, П.В. Ушаков, О.А. Скарлато и китайский Зоолог У Бао Лин. Конец 1950-х гг. Архив В.В. Хлебовича.
Fig. 4. A – O.A in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Fig. 4. A – O.A. Scarlato, student of the 2nd year. October 14, 1947. Archive of the S.O. Skarlato; B – Post-graduate student O.A. Scarlato in office. 1950. Archive of I.M. Likharev.
Fig. 3. A in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Fig. 3. A – Lieutenant (junior grade) O.A. Scarlato. 1943. Archive of S.O. Scarlato; B – Sergeant O.A. Scarlato. April 27, 1940. Archive of S.O. Scarlato.
Fig. 2. A – O.A in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Fig. 2. A – O.A. Scarlato with students of the same year of the Biological Faculty, Leningrad University drafted into the Red Army in 1939. From left to right: E.V. Zhukov, V.Yegorov, V.V. Pinevich,A.V. Zhirmunsky, O.A. Scarlato. Zaporozhye. 1940. Archive of S.O. Scarlato; B – Company commander Lieutenant O.A. Scarlato (far left) with his comrades. Odessa. 1945. Archive of S. O. Scarlato.
Data and code for "Salomon et al. 2024: Effects of dissolved organic matter on the toxicity of micro- and nanoplastic particles to Daphnia - a meta-analysis."
<div> <p>All data and R code for</p> <p><strong>Salomon S, Grubmüller E, Kropf P, Nickl E, Rühl A, Weigel S, Becker F, Antonio Vital AL, Laforsch C, Schott M, Mair MM. (2024). Effects of dissolved organic matter on the toxicity of micro- and nanoplastic particles to <em>Daphnia</em> - a meta-analysis. <em>Microplastics and Nanoplastics</em>. (<a href="https://doi.org/10.1186/s43591-024-00088-4" target="_blank" rel="noopener">https://doi.org/10.1186/s43591-024-00088-4</a>)</strong></p> <p><em>Abstract</em></p> <p>Effects of micro- and nanoplastic particles (MNP) on organisms have been increasingly reported in recent years, with a large number of studies conducted on water fleas of the genus <em>Daphnia</em>. Most of the available studies used pristine particles that have not been exposed to the environment or to organic substances. In natural environments, however, organic substances like dissolved organic matter (DOM) attach to the MNP, forming an ecocorona on the particles’ surface. How the formation of an ecocorona influences MNP toxicity is still uncertain. While some studies suggest that DOM can mitigate the negative effects of MNP on organisms, other studies did not find such associations. In addition, it is unclear whether the DOM attached to the particles’ surface is attenuating the effects of MNP directly or whether co-exposure with DOM solved in the medium attenuates MNP toxicity indirectly, for instance by increasing Daphnia‘s resilience to stressors in general. To draw more solid conclusions about the direction and size of the mediating effect of DOM on MNP-associated immobilization in <em>Daphnia</em> spp., we synthesized evidence from the published literature and compiled 305 data points from 13 independent studies. The results of our meta-analysis show that the toxic effects of MNP are likely reduced in the presence of certain types of DOM in the exposure media. We found similar mediating effects when MNP were incubated in media containing DOM before the exposure experiments, although to a lesser extent. Future studies designed to disentangle the effects of DOM attached to the MNP from the general effects of DOM in the exposure medium will contribute to a deeper mechanistic understanding of MNP toxicity in nature and enhance the reliability of MNP risk assessment.</p> </div>
Predicting particle quality attributes of organic crystalline materials using Particle Informatics
<p>Dataset related to the publication: "Predicting particle quality attributes of organic crystalline materials using Particle Informatics" published in Powder Technology (<a title="Go to table of contents for this volume/issue" href="https://www.sciencedirect.com/journal/powder-technology/vol/443/suppl/C"><span>Volume 443</span></a>, 1 July 2024, 119927Volume 443, 1 July 2024, 119927). In this work, a novel quercetin solvate of dimethylformamide (QDMF) was studied. The crystal structure was solved using single crystal X-ray diffraction and analysed using synthon analysis and other particle informatics tools (<em>e.g.</em>, solvate analyser). The thermal behaviour and thermodynamic stability of QDMF were studied experimentally using Raman spectroscopy, ATR-FTIR spectroscopy, differential scanning calorimetry, and thermogravimetric analysis. A clear relationship between the two-step desolvation behaviour of QDMF and the type, strength, and directionality of the main bulk synthons characterizing the QDMF structure was observed. Additionally, the attachment energy model was used to predict the QDMF morphology, together with facet-specific topology and chemical nature of each of the dominant {001}, {110}, and {200} facets. The {200} facet was found to be significantly rougher than the other two; whereas, the {110} was characterized by a higher percentage of exposed DMF molecules compared to the other two facets. Specific scanning electron microscopy and contact angle measurements were used to experimentally detect differences among the three facets and validate the modelling results.</p>
Figure 3 in Anatomical characteristics and resprouting capacity of the underground organs of Bohemian knotweed (Polygonum ×bohemicum)
Figure 3. Morphology of rhizomes and roots of Polygonum ×bohemicum. (A and B) Young rhizome; (C and D) mature rhizome; (E and F) young root; (G and H) mature root. bu, rhizome bud; co, cortex; ep, epidermis; ha, rhizome hair; in, internodal interval; pi, pith; rh, root hair; vb, vascular bundles.
Figure 7 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 7. Annual, perennial, dicot, and monocot weed biomass in each weed management treatment pooled across fields. Similar letters above bars indicate no significant difference using separate Fisher's LSD tests (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.
Figure 6 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 6. Diversity indices of weed communities for all treatments. Weed by species biomass was pooled across fields.Similar letters above bars indicate no significant difference using separate Fisher's LSD tests (P> 0.05).Error bars are standard errors and treatments are abbreviated:NC,nontreated control; SR,seeding rate; IM, interrow mower;WZ,Weed Zapper™.
Figure 5 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 5. Weed biomass in each weed management treatment pooled across all site-years.Biomass was sampled in mid-August after all management tactics had been applied. Similar letters above bars indicate no significant difference using Fisher's LSD test (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.
Figure 4 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 4. Soybean density in August after all weed management treatments were applied. Data were pooled across all site-years. Similar letters above bars indicate no significant difference using Fisher's LSD test (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.
Figure 8 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 8. Soybean yield from each weed management treatment pooled across fields. Yield is dry weight corrected to 13% moisture. Similar letters above bars indicate no significant difference using Fisher's LSD test (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.
Figure 1 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 1. The interrow mower used in this experiment, attached to a John DeereṜ 5100R tractor with a three-point hitch. The mower is powered with a hydraulic system and was custom made by IRM X4, R-Tech Industries (Homewood, MB, Canada).
Figure 1 in Relative uptake of organic and inorganic nitrogen by common weed species
Figure 1. Estimated intact amino acid uptake (root) and translocation (shoot) plotted against the excess 15N (atom percent excess 15N [15N APE]) found in each respective tissue type (amino acid 15N treatment only) for each weed species. Whole amino acid uptake and translocation were calculated by dividing the observed 15N:13C in plant tissue with the measured 15N:13C in the dual-labeled fertilizer. Data are means ± SE (n = 5). See Table 1 for list of full species names.
Figure 2 in Relative uptake of organic and inorganic nitrogen by common weed species
Figure 2. Canonical plot displaying the results of a linear discriminant analysis evaluating patterns in 15N uptake by species (Wilks's lambda = 0.0157, F(42, 83) = 2.8227, P <0.0001). The first two canonical axes were the most explanatory, accounting for 66.9% and 19.7% of the model variation. Species multivariate means (þ symbols) are surrounded by 95% confidence ellipses. Weighted biplots of N form by tissue atom percent excess 15N (15N APE) values are emanating from the grand mean of the data set, and their length and direction indicate the relative strength of their correlation with the first two axes. See Table 1 for list of full species names.
Figure 2. The model 6R30 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 2. The model 6R30 Weed Zapper™ used in this experiment. The generator is attached to the back of a John DeereṜ 5100R tractor with a three-point hitch. The 4.6-m electric copper boom is attached to the front of the tractor with a three-point hitch. The Weed Zapper™ was purchased from Old School Manufacturing (Sedalia, MO, USA).
Figure 5 in Anatomical characteristics and resprouting capacity of the underground organs of Bohemian knotweed (Polygonum ×bohemicum)
Figure 5. Resprouting rate for each pith color class (time days = 70; ntot = 201) in Polygonum ×bohemicum. (A) using the six pith brightness classes according to grayscale [0–1] intervals and (B) using the six color classes for which the reference RGB color is indicated in Hex Code. Differences in resprouting capacities (resprouting rates) were significant among pith brightness classes (Χ2 = 29.4, df = 5, P-value <0.01), as well as among pith color classes (Χ2 = 31.1, df = 5, P-value <0.01).
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.