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Fig. 12. a in Spirostomum teres: A Long Term Study of an Anoxic-Hypolimnion Population Feeding upon Photosynthesizing Microorganisms

Fig. 12. a) Spirostomum teres numbers plot [cells mL–1] against dissolved oxygen, DO [mg L–1] vs. nitrate nitrogen NO – [µmolL–1], and 3 all analysed DO/nitrate data, b) Species occurrence in habitats, in which S. teres was found (in the logarithmic scale of nitrate nitrogen concentration).

Fig. 8 in Spirostomum teres: A Long Term Study of an Anoxic-Hypolimnion Population Feeding upon Photosynthesizing Microorganisms

Fig. 8. Spirostomum teres in Alchichica on July 2013 (epifluorescence microscope, Leica DMLB). a) DAPI filter set (A); b) Phycobilin filter set (Y3); c) Chlorophyll a filter set (I3). 1 ≡ 10 µm.

Fig. 3. a in Spirostomum teres: A Long Term Study of an Anoxic-Hypolimnion Population Feeding upon Photosynthesizing Microorganisms

Fig. 3. a) Dissolved Oxygen isopleths, sampled data/depths (Analysed), and of Spirostomum teres numbers in Lake Alchichica; b) average S. teres numbers throughout an hypoxic/anoxic layer (based on Peštová et al. 2008, Bautista-Reyes and Macek 2012, Sánchez-Medina et al. 2016, and this study).

Fig. 7 in Spirostomum teres: A Long Term Study of an Anoxic-Hypolimnion Population Feeding upon Photosynthesizing Microorganisms

Fig. 7. Spirostomum teres in Alchichica on July 2016 (epifluorescence microscope, Leica DMLB). a) DAPI staining; b) Phycobilin filter set (Y3); c) Chlorophyll a filter set (I3). 1 ≡ 10 µm.

Fig. 16. a in Spirostomum teres: A Long Term Study of an Anoxic-Hypolimnion Population Feeding upon Photosynthesizing Microorganisms

Fig. 16. a) Plot of distance-based redundancy analysis (dbRDA) of Spirostomum teres (S. teres), autotrophic picoplankton (APP) and heterotrophic picoplankton (HPP) abundance using environmental data as predictor variables, for Lake Alchichica. Environmental variables: Concentrations of dissolved oxygen (DO), ammonium (NH 3), nitrite (NO 2), nitrate (NO 3), dissolved reactive phosphorus (DRP) and silicon (SiO).

Table 1 in Latitudinal Diversity Gradients in Free-living Microorganisms - Hoogenraadia a Key Genus in Testate Amoebae Biogeography

Table 1. Characters and distribution of six species of the genus Hoogenraadia (L – length, W – width). Many of the earlier papers do not report a sample size for number of tests measured – so it is possible that some of these data may be based on a very low sample size.<table><tbody><tr><th>Species</th><th>Size (µm)</th><th>Distribution regions and publication</th><th>Habitats</th></tr></tbody><tbody><tr><th>H. africana</th><td>L = 95–115, W = 47–60</td><td>Moyen-Congo (Gauthier-Lièvre and Thomas 1958), Guinea and Equatorial Guinea (Golemansky 1962), Brasil (Leiptniz et al. 2003), China (Qin et al. 2011)</td><td>Sphagnum, water, river, forest marsh</td></tr><tr><th>H. asiatica</th><td>L = 95, W = 70</td><td>China (Wang and Min 1987)</td><td>Quaternary deposit</td></tr><tr><th>H. cryptostoma</th><td>L = 130–140, W = 105–110</td><td>Moyen-Congo (Gauthier-Lièvre and Thomas 1958), States of Parana, Mato Grosso du Sul, Brasil (Velho et al. 1996, 2000)</td><td>Swamp quite shady in the bed of a stream</td></tr><tr><th>H. humicola</th><td>L = 143–146, W = 96–100</td><td>Nepal, Himalayas (Bonnet 1977, 1978), Philippines (Bonnet 1980), Cote d’Ivoire, Africa (Bonnet 1976, 1978), Tonga and Western Samoa Islands (Korganova 1994), China (this paper)</td><td>Soils rich in organic debris in forest-gallery backwaters. The ground litter and sublitter horizons of white subtropical soils</td></tr><tr><th>H. ovata</th><td>L = 60–67, W = 36–39</td><td>Cote d’Ivoire, Africa (Bonnet 1976)</td><td>Soils rich in organic debris in forest-gallery backwaters</td></tr><tr><th>H. sylvatica</th><td>L = 82–93, W = 60–70</td><td>Punta Lara Province of Buenos Aires, Argentina (Vucetich 1974)</td><td>Moss in marginal forest</td></tr></tbody></table>

opencc-by-4.0Dec 2021View details →

Crosswalks IUCLID 6.6 EU PPP Microorganisms - active substance application (product) to KMA&KMP

This Excel file provides detailed crosswalks from the <a href="https://esubmission.ecpa.eu/toc/EU">EU Table of Contents (SANCO/10181/2013)</a> for microbial plant protection product (PPP) dossiers to <a href="https://iuclid6.echa.europa.eu/">IUCLID 6.6</a>. It includes two spreadsheets, containing the mappings for active substance (as laid out in Commission Regulation (EU) No 283/2013) and representative product (Commission Regulation (EU) No 284/2013): <ul> <li>EU_PPP_Micro_ActiveSubstance: mapping between the "KMA" ToC and IUCLID 6.5's working context "EU PPP Microorganisms - active substance information"</li> <li>EU_PPP_Micro_Product: mapping between the "KMP" ToC and IUCLID 6.5's working context "EU PPP Microorganisms - active substance application (product)"</li> </ul> The spreadsheets map each section of the original EU ToC to: <ul> <li>IUCLID section: name of the section in IUCLID where to input the corresponding data/information</li> <li>Endpoint study record: name of the document template used to report individual studies of the section (if exists). These usually correspond to OECD Harmonised Templates (OHT).</li> <li>Endpoint summary: name of the document template used to report the summary information of the studies presented in the section (if exists)</li> <li>Other IUCLID document: name of any other document template in IUCLID used to report information of the section (if exists)</li> <li>OHT: name/id of the OECD Harmonised Template used for the endpoint study record document (if exists)</li> <li>Additional context: fulI IUCLID paths indicating the section of the respective document where information needs to be provided and/or specific values to be indicated. If more than one path is provided, these are separated by ";". Operators "=" and "!=" are used to indicate values to be used and to be avoided, respectively. When more than one value is possible, "IN" and "NOT IN" operators are used, followed by the exhaustive list of possible values. This nomenclature was defined with the aim of making contents machine-readable.</li> </ul> Version 3 includes changes in the table of contents of IUCLID6.6.

opencc-by-4.0Aug 2022View details →

Fig. 8 Incertae sedis microorganisms and agglutinated worm tubes. a-i in Upper Jurassic To Lowermost Cretaceous Microfossils From The Hăghimaş Mountains (Eastern Carpathians, Romania)

Fig. 8 Incertae sedis microorganisms and agglutinated worm tubes. a-i Crescentiella morronensis (Crescenti) in longitudinal-oblique (a, c, i), oblique (b, d, e, f), and transverse (e, h) sections; a, c – thin section FO1-B (c = closeup view of the middle part in a); b – thin section FO2-B8; d – thin section FO2-A1(3); e, f – thin section FO1-E (f = close-up view of the middle part in e). g – thin section FO1-A1; h – thin section FO2-A1; i – thin section FO1- F2(2). j-n Terebella lapilloides (Münster). Longitudinal (l), longitudinal-oblique (k, n), and transverse (j, m) sections; j – thin section FO2-B10; k – thin section FO1-B; l, m – thin sections FO1-A0; n – thin section FO2-A1.

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Data from: Gut-resident microorganisms and their genes are associated with cognition and neuroanatomy in children

The gastrointestinal tract, its resident microorganisms, and the central nervous system are connected by biochemical signaling, also known as the "microbiome-gut-brain-axis." Both the human brain and the gut microbiome have critical developmental windows in the first years of life, raising the possibility that their development is co-occurring and likely co-dependent. Emerging evidence implicates gut microorganisms and microbiota composition in cognitive outcomes and neurodevelopmental disorders (e.g., autism and anxiety), but the influence of gut microbial metabolism on typical neurodevelopment has not been explored in detail. We investigated the relationship of the microbiome with the neuroanatomy and cognitive function of 381 healthy children, demonstrating that differences in gut microbial taxa and gene functions are associated with overall cognitive function and with differences in the size of multiple brain regions. Using a combination of multivariate linear and machine learning (ML) models, we showed that many species, including Alistipes obesi and Blautia wexlerae, were associated with higher cognitive function, while some species such as Ruminococcus gnavus were more commonly found in children with low cognitive scores after controlling for sociodemographic factors. Microbial genes for enzymes involved in the metabolism of neuroactive compounds, particularly short-chain fatty acids such as acetate and propionate, were also associated with cognitive function. In addition, ML models were able to use microbial taxa to predict the volume of brain regions, and many taxa that were identified as important in predicting cognitive function also dominated the feature importance metric for individual brain regions, and for specific subscales of cognitive function. For example, B. wexlerae was the most important species in models predicting the size of the parahippocampal region in both the left and right hemispheres and was among the top predictors of gross motor and expressive language performance. Several species from the phylum Bacteroidetes, including GABA-producing Bacteroides ovatus, were important for predicting the size of the left accumbens area, but not the right. These findings provide potential biomarkers of neurocognition and brain development and may lead to the future development of targets for early detection and early intervention.

opencc-zeroJul 2024View details →

Figure 3 in Host conservation through their parasites: molecular surveillance of vector-borne microorganisms in bats using ectoparasitic bat flies

Figure 3. Comparison of detected microorganism prevalence (prevalence of infection) between bats and bat flies. Different bars represent hosts (black), all bat flies (dark grey), and consensus fly results, meaning that at least one infected fly individual was present on the host (light grey).

Figure 2 in Host conservation through their parasites: molecular surveillance of vector-borne microorganisms in bats using ectoparasitic bat flies

Figure 2. Prevalence of Bartonella spp., Polychromophilus spp., and Trypanosoma spp. infection in nycteribiid flies collected from 28 bats, which carried between 2 and 7 flies. Black: all flies are infected, dark grey: all flies are non-infected, light grey: both infected and non-infected flies occurred on the same host.

Figure 1 in Host conservation through their parasites: molecular surveillance of vector-borne microorganisms in bats using ectoparasitic bat flies

Figure 1. Number of detected vector-borne microorganisms in bats (A) and bat flies (B). Black colour corresponds to Miniopterus natalensis (A), and Nycteribia schmidlii scotti (B), whereas grey shows Miniopterus schreibersii (A) and Nycteribia schmidlii (B).

opencc-by-4.0Dec 2022View details →

Figure 1 in Microorganisms from corn stigma with biocontrol potential of Fusarium verticillioides

Figure 1. Percentage of mycelial growth inhibition of Fusarium verticillioides by endophytic and epiphytic microorganisms from maize silks collected in different Brazilian regions.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: International Collection of Microorganisms from Plants (ICMP).

Natural history specimen data linked to collectors and determiners held within, "International Collection of Microorganisms from Plants (ICMP)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3c6e7390-3b56-11dc-8c19-b8a03c50a862">https://bionomia.net/dataset/3c6e7390-3b56-11dc-8c19-b8a03c50a862</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3c6e7390-3b56-11dc-8c19-b8a03c50a862">https://gbif.org/dataset/3c6e7390-3b56-11dc-8c19-b8a03c50a862</a>. Formatted as a Frictionless Data package.

opencc-zeroAug 2024View details →

Linked collectors and determiners for: CNR-ISA Agri-food Microorganism Dataset (ISA01-CC).

Natural history specimen data linked to collectors and determiners held within, "CNR-ISA Agri-food Microorganism Dataset (ISA01-CC)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/aebc4372-55b7-4ddf-8bd1-5693eb67e8b3">https://bionomia.net/dataset/aebc4372-55b7-4ddf-8bd1-5693eb67e8b3</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/aebc4372-55b7-4ddf-8bd1-5693eb67e8b3">https://gbif.org/dataset/aebc4372-55b7-4ddf-8bd1-5693eb67e8b3</a>. Formatted as a Frictionless Data package.

opencc-by-4.0Nov 2018View details →

Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI)

(Version 20181130) Edit #1 (Mar 06, 2023): New database version (v.4.2 - 20230306) - available: <a href="https://zenodo.org/records/14562231">https://zenodo.org/records/14562231</a> Version 3 (20181130) of the RKI’s MALDI-TOF mass spectral database represents the second update of the original database (version 20161027, https://doi.org/10.5281/zenodo.163517). The RKI database v.3 contains altogether 6264 mass spectra from highly pathogenic (i.e. biosafety level 3, BSL-3) bacteria such as Bacillus anthracis, Yersinia pestis, Burkholderia mallei, Burkholderia pseudomallei and Francisella tularensis as well as a selection of spectra from their close and more distant relatives. The database can be used as a reference for the diagnostics of BSL-3 bacteria using proprietary and free software packages for MALDI-TOF MS-based microbial identification. Spectral data are distributed as a 7-zip archive that contains the original mass spectra in its native data format (Bruker Daltonics). Please refer to the pdf file (181130-ZENODO-Metadata.pdf) to obtain information on cultivation condition, sample preparation and details of spectra acquisition. Do not try to print this document (~1000 pages!) The pkf-file (181130_ZENODO_Peaklist_30Peaks_1.6.pkf) contains the MS peak list data in a Matlab compatible format. The latter data file can be imported into MicrobeMS, a Matlab-based free-of-charge software solution developed at RKI. MicrobeMS is available from <a href="https://wiki-ms.microbe-ms.com">https://wiki-ms.microbe-ms.com</a>. The RKI mass spectral database will be updated on a regular basis. The author's grateful thanks are given to the following persons for providing microbial strains and species, or mass spectra. Without their help this work would not be possible. <ul> <li>Wolfgang Beyer - University of Hohenheim, Faculty of Agricultural Sciences, Stuttgart, Germany</li> <li>Guido Werner - Robert Koch-Institute, Nosocomial Pathogens and Antibiotic Resistances (FG13), Wernigerode, Germany</li> <li>Alejandra Bosch - CINDEFI, CONICET-CCT La Plata, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina</li> <li>Michal Drevinek - National Institute for Nuclear, Biological and Chemical Protection, Milin, Czech Republic</li> <li>Roland Grunow - Robert Koch-Institute, Highly Pathogenic Microorganisms (ZBS2), Berlin, Germany</li> <li>Daniela Jacob - Robert Koch-Institute, Highly Pathogenic Microorganisms (ZBS2), Berlin, Germany</li> <li>Silke Klee - Robert Koch-Institute, Highly Pathogenic Microorganisms (ZBS2), Berlin, Germany</li> <li>Jörg Rau - Chemisches und Veterinäruntersuchungsamt Stuttgart, Fellbach, Germany</li> <li>Jens Jacob - Robert Koch-Institute, Hospital Hygiene, Infection Prevention and Control (FG14), Berlin, Germany</li> <li>Martin Mielke - Robert Koch-Institute, Department 1 - Infectious Diseases, Berlin, Germany</li> <li>Monika Ehling-Schulz - Functional Microbiology, Institute of Microbiology, University of Veterinary Medicine, Vienna, Austria</li> <li>Armand Paauw - Department of Medical Microbiology, CBRN protection, Universitair Medisch Centrum Utrecht, TNO, Rijswijk, The Netherlands</li> </ul>

opencc-by-4.0Apr 2023View details →

Dataset for: Intracellular carbon storage by microorganisms is an overlooked pathway of biomass growth

Dataset and code for the publication: Intracellular carbon storage by microorganisms is an overlooked pathway of biomass growth Mason-Jones, K., Breidenbach, A., Dyckmans, J., Banfield, C.C.,  Dippold, M.A. Nature Communications 2023   Article DOI: 10.1038/s41467-023-37713-4

Fig. 1. SEM photos showing c.f in Latitudinal Diversity Gradients in Free-living Microorganisms - Hoogenraadia a Key Genus in Testate Amoebae Biogeography

Fig. 1. SEM photos showing c.f. Hoogenraadia humicola found from soils in Shennongjia Mountains of central China (the left picture is from Qin et al. 2011). This was previously identified as Planhoogenraadia africana by Qin et al. (2011). Scale bars: 50 µm (a) and 20 µm (b).

Fig. 3 in Latitudinal Diversity Gradients in Free-living Microorganisms - Hoogenraadia a Key Genus in Testate Amoebae Biogeography

Fig. 3. Relationship between shell width and shell length of each species of the genus Hoogenraadia. The sample sizes on which these measurements are based is unclear as the older literature often doesn't specify the number of tests measured (see Table 1).

Fig. 2 in Latitudinal Diversity Gradients in Free-living Microorganisms - Hoogenraadia a Key Genus in Testate Amoebae Biogeography

Fig. 2. Shell outline and SEM photos of six species of genus Hoogenraadia Gauthier-Lièvre and Thomas 1958, with scale bar 50 μm. a – H. cryptostoma Gauthier-Lièvre and Thomas 1958; b – H. sylvatica Vucetich 1974; c – H. africana Gauthier-Lièvre and Thomas 1958; d – H. humicola Bonnet 1976; e – H. ovata Bonnet 1976; f – H. asiatica Wang and Min 1987.