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Figure 2 from: Nong Y, Lai K-D, Qin Y-R, Wei G-Y, Yan K-J, Xu C-G, Zhao Z-Y, Hu R-C, Huang Y-F (2024) Aletris guangxiensis (Nartheciaceae), a new species from Guangxi, China. PhytoKeys 237: 79-89. https://doi.org/10.3897/phytokeys.237.115037
Figure 2 Line drawing of Aletris guangxiensisA flowering branch B flowers C Ovary and stigma D Filaments of stamens and perianth [Drawn by Xin–cheng Qu from Y Nong NY2020042301 (GXMI)].
Figure 1 from: Nong Y, Lai K-D, Qin Y-R, Wei G-Y, Yan K-J, Xu C-G, Zhao Z-Y, Hu R-C, Huang Y-F (2024) Aletris guangxiensis (Nartheciaceae), a new species from Guangxi, China. PhytoKeys 237: 79-89. https://doi.org/10.3897/phytokeys.237.115037
Figure 1 Habitat of Aletris guangxiensis on the moist cliffs next to streams. [Photographed by You Nong and Ke–Jian Yan].
Figure 4 from: Nugroho A, Tamtomo DG, Indarto D, Cilmiaty R, Soetrisno (2024) The effect of L-Arginine from Giant Snake Head fish (Channa micropeltes) on neuroinflammation and neuron damage in traumatic brain injury in rats. Pharmacia 71: 1-9. https://doi.org/10.3897/pharmacia.71.e111239
Figure 4 Immunohistochemical staining of Caspase-3 400× microscope magnification. Picture of caspase 3 expression in the cerebral cortex area shows a picture of caspase 3 expression in the cytoplasm of neuron cells (yellow arrow). A. The normal control group showed a score of 0; B. The negative control group shows a score of 4; C. Group A shows a score of 3; D. Group B shows a score of 2; E. Group C shows a score of 1.
Figure 5 from: Nugroho A, Tamtomo DG, Indarto D, Cilmiaty R, Soetrisno (2024) The effect of L-Arginine from Giant Snake Head fish (Channa micropeltes) on neuroinflammation and neuron damage in traumatic brain injury in rats. Pharmacia 71: 1-9. https://doi.org/10.3897/pharmacia.71.e111239
Figure 5 Histopathological picture of brain tissue damage with HE staining, 400× microscope magnification. Description: Histopathological picture of the cerebral cortex area shows degeneration of neuron cells (yellow arrows). A. The normal control group shows a score of 0; B. The negative control group shows a score of 2; C. Group A shows a score of 2; D. Group B shows a score of 1; E. Group C shows a score of 1.
Figure 2 from: Nugroho A, Tamtomo DG, Indarto D, Cilmiaty R, Soetrisno (2024) The effect of L-Arginine from Giant Snake Head fish (Channa micropeltes) on neuroinflammation and neuron damage in traumatic brain injury in rats. Pharmacia 71: 1-9. https://doi.org/10.3897/pharmacia.71.e111239
Figure 2 TLR4 immunohistochemical staining with a microscope magnification of 400×. Description: TLR4 expression in the cerebral cortex area shows TLR4 expression in astrocytes (yellow arrows). A. Control group normally expressed 5%; B. Negative control group expressed 20%; C. Group A expressed 15%; D. Group B expressed 15%; E. Group C expressed 10%.
Figure 3 from: Nugroho A, Tamtomo DG, Indarto D, Cilmiaty R, Soetrisno (2024) The effect of L-Arginine from Giant Snake Head fish (Channa micropeltes) on neuroinflammation and neuron damage in traumatic brain injury in rats. Pharmacia 71: 1-9. https://doi.org/10.3897/pharmacia.71.e111239
Figure 3 TNF-α immunohistochemical staining with a microscope magnification of 400×. TNF-α expression in the cerebral cortex area shows TNF-α expression in astrocytes (yellow arrows). A. Control group normally expressed 5%; B. Negative control group expressed 15%; C. Group A was depressed 10%; D. Group B expressed 10%; E. Group C expressed 5%.
Figure 1 from: Tobing TCL, Wahrianto, Saputri E, Wafa NI, Zulfianti PD, Sihaloho LI, Husna AR, Salsabila D, Hotasi Silalahi FH, Sitohang AI, Sabrina A, Hasyati Harianja AD, Barus SA, Sabina S, Rahma AA, Velaro AJ, Khairunnnisa K, Salim E, Nurkolis F, Abdi Syahputra R (2024) Malaria in Indonesia: current treatment approaches, future strategies, and potential herbal interventions. Pharmacia 71: 1-14. https://doi.org/10.3897/pharmacia.71.e116095
Figure 1 High risk region and populations of malaria in Indonesia. (The data from the Ministry of Health, Republic of Indonesia, 2019).
Figure 3 from: Xu R-J, Li J-F, Zhou D-Q, Boonmee S, Zhao Q, Chen Y-Y (2024) Three novel species of Aquapteridospora (Distoseptisporales, Aquapteridosporaceae) from freshwater habitats in Tibetan Plateau, China. MycoKeys 102: 183-200. https://doi.org/10.3897/mycokeys.102.112905
Figure 3 Aquapteridospora yadongensis (HKAS 128992, holotype) a colonies on the substratum b, c conidiophore and conidiogenous cell d-g conidiogenous cells with developmental conidia h–k conidia l germinating conidium m culture on PDA. Scale bars: 100 μm (b, c); 20 μm (d, g); 10 μm (h–l).
Figure 1 from: Xu R-J, Li J-F, Zhou D-Q, Boonmee S, Zhao Q, Chen Y-Y (2024) Three novel species of Aquapteridospora (Distoseptisporales, Aquapteridosporaceae) from freshwater habitats in Tibetan Plateau, China. MycoKeys 102: 183-200. https://doi.org/10.3897/mycokeys.102.112905
Figure 1 Maximum likelihood (ML) tree is based on combined LSU, TEF1-α and ITS sequence data. ML bootstrap support values equal to or greater than 70% and Bayesian posterior probabilities (PP) equal to or greater than 0.95 given above the nodes, shown as "ML/PP". The tree is rooted with Pseudostanjehughesia aquitropica (MFLUCC 16-0569) and P. lignicola (MFLUCC 15-0352). New species are indicated in red and type strains are in bold.
Figure 2 from: Xu R-J, Li J-F, Zhou D-Q, Boonmee S, Zhao Q, Chen Y-Y (2024) Three novel species of Aquapteridospora (Distoseptisporales, Aquapteridosporaceae) from freshwater habitats in Tibetan Plateau, China. MycoKeys 102: 183-200. https://doi.org/10.3897/mycokeys.102.112905
Figure 2 Aquapteridospora linzhiensis (HKAS 128991, holotype) a colonies on the substratum b–e conidiophores, conidiogenous cells with conidia f, g conidiogenous cells with developmental conidia h–k conidia l, m culture on PDA. Scale bars: 50 μm (b–e); 20 μm (f, g); 10 μm (h–k).
Figure 4 from: Xu R-J, Li J-F, Zhou D-Q, Boonmee S, Zhao Q, Chen Y-Y (2024) Three novel species of Aquapteridospora (Distoseptisporales, Aquapteridosporaceae) from freshwater habitats in Tibetan Plateau, China. MycoKeys 102: 183-200. https://doi.org/10.3897/mycokeys.102.112905
Figure 4 Aquapteridospora submersa (HKAS 128980, holotype) a colonies on the substratum b–d conidiophores, conidiogenous cells with conidia e–g conidiogenous cells with developmental conidia h–k conidia l germinating conidium m, n culture on PDA. Scale bars: 50 μm (b–d); 20 μm (e–g); 10 μm (h–l).
Supplementary material 3 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Plant pathogenic bacteria assessment for Zambia
Supplementary material 4 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Plant pathogenic protist assessment for Zambia
Supplementary material 2 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Guidelines for scoring species
Supplementary material 5 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Assessment for vector species
Supplementary material 1 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
All data from horizon scanning for Zambia
Supplementary material 1 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782
Discussion question response data
Figure 2 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782
Figure 2 Polling results of discussion session 1 "Extent of imaging", question 1: "What are the barriers to increasing the extent of imaging the oceans?".
Figure 9 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782
Figure 9 Polling results of discussion session 3 "Automation", question 3: "What do you think is the biggest barrier to making AI systems applicable across datasets?".
Figure 7 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782
Figure 7 Polling results of discussion session 3 "Automation", question 1: "Which AI Frameworks are your lab using/planning to use?".
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.