dataesr/scientific-paragraphs-categorization huggingface dataset:
Diversity in mixoplankton species and MFTs The MDB comprises a total of 435 species. Of these, 150 species had been previously identified as mixoplankton in the datasets of  Leles et al. (2017 Leles et al. ( , 2019) )  and  Faure et al. (2019). Over 50% of the species listed within the MDB belong to the eSNCM functional group (Figure  2 ). The CM functional group, which includes species commonly identified as "phytoplankton," is the second most abundant with 36% of the species in the database belonging to this group. The GNCM and pSNCM functional groups each contain 30 species within the database (Figure  2A ). Of the total mixoplankton, ~9% of the CM and ~3% of the pSNCM species have been recorded as HAB species within the IOC-UNESCO database (Figure  2A ). Analysis of the size class distribution (Figure  2B ) within each MFT (Figure  1 ) showed the greatest range for eSNCM, which span across the nano to macro sizes, though the majority belong to the micro size class.
, 1988). Therefore, these smaller sizes must be present, albeit F I G U R E 3 Taxonomic diversity within each MFT. See Figure  1  for MFT definitions. Table  1  documents the most frequently recorded species in each MFT, while Table 2 further characterizes these MFTs within size categories. For species with extensive ontogenetic development (e. g. many Foraminifera), only the maximum sizes are provided in Table  1. Such species commence as much smaller forms (often as nanoplankton) and progress through to meroplankton; this information is present in the database. Most other protists do not change as much in size during reproduction because binary fission is the dominant form of reproduction. Species of the genus Tripos (previously included in Ceratium) are the most important frequently recorded mixoplankton (Table  1 ).
Chlorophyta Data for the 18 S rRNA gene marker were available in the PR 2 database for 229 mixoplankton species, comprising 105 CM, 89 eSNCM, 13 GNCM, and 22 pSNCM species (Figure  S3 ). Of these, 32 of the 105 CM species also had available records of plastidic 16 S rRNA genes. The species with the most gene records was the eSNCM Pulleniatina obliquiloculata (with 274 sequences registered in PR 2 ), followed by the eSNCM species Globigerinoides ruber and G. elongatus (248 and 183 entries, respectively). In summary, records of 50% and 41% of the eSNCM species in the MDB were located within both the OBIS and metaPR 2 databases, respectively. The OBIS database recorded the presence of 55% of the CM species, 57% of the GNCM species, and 67% of pSNCM species; the metaPR 2 holds records for 67% of the CM species, 43% of the GNCM species, and 73% of the pSNCM species.
Mixoplankton occurrence according to OBIS versus metaPR 2 The global distribution of mixoplankton species by Longhurst province differs considerably between OBIS and metaPR 2 (Figure  8 ); NMDS shows a clear separation between samples derived from OBIS and metaPR 2. A closer analysis suggests that this separation occurs independently of size class or mixoplankton type (Figure  S4 ). The sampling effort can partly explain these differences between databases because metaPR 2 contains a majority of open ocean samples, while OBIS includes also coastal regions. Global maps comparing the OBIS and metaPR 2 datasets provide further insight into why the distribution patterns differ; this is shown for selected species in Figures 9 and 10. Among CM species (Figure  9 ), the cosmopolitan coccolithophorid Emiliania huxleyi is found in OBIS but absent from metaPR 2 , while members of the HAB (IOC-UNESCO) database such as Phaeocystis globosa and Chrysochromulina leadbeateri are better covered by metaPR 2.
The large CM dinoflagellate Tripos furca has a similar distribution between databases, probably due to its easier morphological identification and higher probability of being sampled intact due to its larger robust cells (see also Tables  1  and 2 ). Fewer data are available for GNCM and pSNCM species compared to CM species but we were able to compare a few key representatives, such as Mesodinium rubrum, Dinophysis acuminata, D. acuta, and Laboea strobila (Figure  10A ). The clear distinction based on the oceanic regions in which the species were observed (mainly in open seas by metaPR2 and in coastal regions by OBIS) suggests that these two databases can be complementary in the investigation of mixoplankton distribution. Similarly, these databases can provide further information on the biogeography of eSNCM species (Figure  10B ) when used sideby-side since OBIS is superior in providing distribution records for Foraminifera (e.
Updating plankton life-form databases The MDB provides the first comprehensive, speciesspecific, collection of data for marine photo-phagotrophic protist plankton. Analysis of this database provides an evaluation of what we know, and equally important what we do not know, about a group of organisms that have hitherto been largely ignored by mainstream marine science (from laboratory, field, and modeling work, through to management). Only the HAB sector has previously recognized the physiological diversity of mixoplankton  (Burkholder et al. , 2008; Flynn et al. , 2018) , though even there the vast bulk of the research effort and allied ecosystem management strategies have treated these organisms as phytoplankton, emphasizing the roles of light and inorganic nutrients in their ecology. Identifying those "phytoplankton" and "zooplankton" that are actually "mixoplankton" is only part of the task going forward.
Additionally, it is important that we revise databases of "phytoplankton" and "zooplankton", to either delete those species entries that are actually "mixoplankton", or at the very least to explicitly flag them as "mixoplankton". The former is clearly the more robust route; an individual species, with very few exceptions (most notably "red" vs. "green" Noctiluca scintillans;  Gomes et al. , 2018) , cannot properly be a member of two high-level trophiclinked functional categories. Such a development needs then to also be mirrored by updates to catalogs of living forms. Thus, while the diatom database of Leblanc et al. (  2012 ) is unaffected by the mixoplankton paradigm (no known diatoms being phagotrophic), there   2020 ) that are actually mixoplankton, and which at the least would benefit from being tagged as being mixoplanktonic.
To not recognize mixoplanktonic species as mixoplankton, to continue to label these organisms as something that they are not, has implications not only for science but also for stakeholders such as policy makers and ecosystem managers. For example, neither the OBIS nor metaPR 2 datasets discriminate between the zooplanktonic red Noctiluca scintillans and the ecosystem disruptive bloom-forming eSNCM green N. scintillans (Figure  11 ). Green N. scintillans is spreading in the Indian Ocean and adjoining provinces with deleterious impact on ecosystem services  (Goes & Gomes, 2016). There is, therefore, a clear need to routinely and explicitly differentiate between the red (zooplankton) and green (mixoplankton) forms of this species.
Identification to the genus level is often more feasible, but we know that many protist genera contain both mixoplankton and strict heterotrophs (Cf. Schneider et al. , 2020 vs. the MDB). For example, the oligotrich ciliate genus Strombidium includes several species that have been brought into culture for laboratory studies, of which some are GNCM based on their retention of chloroplasts from ingested prey, some are known to be purely heterotrophic zooplankton, while many remain cryptic (e. g. McManus et al. , 2010). Thus, identification to genus would not be enough to validate the presence of a mixoplankton from such a group in a meta-barcode dataset with genus level resolution. In cases where all known members of a genus are mixoplanktonic (e. g. the oligotrich genera Laboea and Tontonia), this difficulty is   e Synonym, Globigerinella aequilateralis.
The plasticity of photo-phago-mixotrophy as a trophic mode, the diversity of mixoplankton types based on fundamentally different metabolisms (e. g. constitutive vs. non-constitutive or even kleptoplastidic vs. symbiotic), and unresolved evolutionary histories, illustrated by taxa exhibiting multiple types of mixotrophy (photo-osmo vs. photo-osmo-phago) even at the genus level, are all challenging hurdles to overcome for defining effective genomic and transcriptomic markers of diverse mixotrophic activity (e. g. between photoosmo-mixotrophic phytoplankton vs. photo-osmophago-mixotrophic mixoplankton). The presence of prey genetic material within individual mixoplankton provides additional challenges. Lambert et al. (2022)  highlighted the potential of large-scale transcriptome comparisons to identify such markers, using a public database with only limited information on organisms' trophic modes and lacking measures of mixotrophic activities in phytoplankton and/or mixoplankton.
Mismatch in most frequently observed mixoplankton species versus exemplar laboratory species The most well documented mixoplankton species across the global oceans are, inevitably, the more robust and larger forms that are captured and identified in routine surveys, such as the Continuous Plankton Recorder  (Leles et al. , 2017 (Leles et al. , , 2019)). Notably, these include the dinoflagellate Tripos sp. (formerly Ceratium sp. ) and various Foraminifera species (Table  1 ). In contrast, mixoplankton that are often exploited in laboratory studies are ranked relatively low within the OBIS database according to the frequency of observations. These include the HAB species Alexandrium catenella (ranked #43 in the MDB and observed in eight Longhurst Provinces), Alexandrium minutum (ranked #50, observed in eight Longhurst Provinces), Karlodinium veneficum (ranked #60, observed in seven Longhurst Provinces), and Prymnesium parvum (ranked #111, observed in two Longhurst Provinces).
The ciliate Laboea strobila is the most commonly and widespread recorded GNCM (#41 in the MDB, observed in 11 Longhurst Provinces; Table  1 ). The non-HAB CM genus, Ochromonas, which has been the subject of various experimental studies (e. g. Lie et al. , 2018; Sanders & Porter, 1988) , appears to be of minor importance when one considers the OBIS and F I G U R E 8 Comparison of species distributions between the OBIS and the metaPR 2 databases. NMDS ordination of species was based on the number of occurrences across the Longhurst biogeographic provinces. Symbol color indicates species data derived from metaPR 2 (purple) and OBIS (green) databases. Differences between OBIS and metaPR 2 are observed across size classes and MFT (see also Figure  S4 ). metaPR 2 databases, However, such an absence could be attributed to a range of different reasons, including difficulties in identifying these small organisms to species level.
(Dis)continuums in mixoplanktonic activity Mixoplankton comprise a highly diverse group of organisms with respect to their taxonomy (Figure  4 ), their broad physiological functionalities linked to the sources of their phototrophic potential (Figure  1 ) and F I G U R E 1 0 Geographic records of selected NCM species from the metaPR 2 and OBIS databases. Panel (A) shows data for selected GNCM (Laboea strobila) and pSNCM (Mesodinium rubrum, Dinophysis acuminata, D. acuta) species. Panel (B) shows data for selected eSNCM species. F I G U R E 1 1 Geographic records of the occurrence of Noctiluca scintillans from the metaPR 2 and OBIS databases. These databases do not discriminate between the zooplanktonic red N. scintillans versus the mixoplanktonic green N. scintillans. use of nitrate, feeding mechanisms (Figures S1 and S2 that impact the allometric relationships with their prey (Figures  5  and 6 ), and also the balance of their expressed levels of phototrophy versus phagotrophy  (Adolf et al.
, 2006; Anschütz & Flynn, 2020; Jones, 1997; Millette et al. , 2017; Mitra & Flynn, 2010; Stoecker, 1998). Taking just the aspect of feeding, across the database we see a wide range of mechanisms enabling prey acquisition by immobilization, killing, lysing or otherwise capturing prey using trichocysts  (Li et al. , 1999) , haptonema  (Kawachi et al. , 1991) , mucocysts or other "sticky" surfaces  (Gowing, 1989; Jeong et al. , 2010; Sugiyama et al. , 2008) , mucus traps  (Blossom et al. , 2017; Larsson et al. , 2022) , toxins  (Granéli et al. , 2012; Tillmann, 2003) , and raptorial capture  (Riisgård & Larsen, 2009). Mechanisms for ingestion include engulfment  (Jeong et al. , 2005; Tillmann, 1998) , semi-extracellular phagocytosis  (Kamennaya et al. , 2018) , use of a peduncle (akin to a feeding straw inserted into the prey to suck out material  ; Larsen, 1988; Nagai et al.
The creation of the MDB is a pivotal part of this journey, mirrored as it will need to be by the consequential re-appraisal of the content of phytoplankton and zooplankton databases and life-form registers. Future developments will see a gradual expansion of the MDB through the addition of other species, and critically also the procurement of data to fill the very many gaps present in the current iteration of the database. It is important that the database is expanded, and the authors welcome additional information in this regard. Some of the most important aspects include data for potential growth rate and temperature optima. Information on photosynthetic and feeding parameters is also needed to contextualize the role of mixoplankton in biogeochemistry and ecology. Much of the focus of future work will find its way into models, and eventually into digital twins of planktonic ecosystems  (Flynn et al.
AU T HOR CON T R I BU T ION S The original concept of this paper arose from discussions between AM, DKS, FN, and GBM following a special session on mixoplankton at the International Society of Protistology (ISOP) conference in Rome (2019). For construction of the MDB, taxonomic groups were reviewed as follows-Cercozoa, Chlorophyta, Cryptophyceae, Dinoflagellata, Haptophyta, Ochrophyta: UT, PJH, and KJF; Ciliophora: DKS, GBM, and LS; Foraminifera: DAC and AM; Radiolaria: AM, FN, and KJF; HdRG provided data for green Noctiluca scintillans. Literature reviews in German were undertaken by UT and in French by FN. IOC-UNESCO HABs list interrogated by AM. WoRMS database interrogation for AphiaID and crossreferencing to AlgaeBase by AM and SGL. Radiolaria database interrogation by AM and KJF; Galerie de l'Observatoire Océanologique de Villefranche-sur-Mer was interrogated by FN.
R-scripts were developed by SGL. Data from OBIS were extracted and compiled by SGL. Genetic data were extracted and compiled by EF and LS. Data analysis was conducted by AM, SGL, EF, and LS. Visualization by AM, SGL, and LS. Writing (original draft, reviewing, and editing) of the text was undertaken by AM, KJF, GBM, EF, SGL, and LS; additional review and editing of the text were undertaken by FN, DKS, DAC, PJH, and UT.
%LP = CountIF(LP records ≥ 10) total #LP • 100 F I G U R E 2 Diversity of mixoplankton species. Panel (A): species categorized according to MFT, also showing species recorded in the IOC-UNESCO database as Harmful Algal Bloom species (HAB, https://marin espec ies. org/hab/). Panel (B): size class distribution of species within each MFT. NR, size not recorded. See Figure 1 for MFT definitions.
Sources of acquired phototrophy for plastidic-and endosymbiotic-specialist non-constitutive mixoplankton (pSNCM and eSNCM, respectively). Panels (A) and (B) show the allometric relationships between the mixoplankton and the source organisms for the acquired phototrophy. Panels (C) and (D) portray the taxonomic groups contributing photosynthetic material to pSNCM and endosymbionts to eSNCM, respectively. NR, not recorded. "Bacteria," purple sulfur bacteria. Size classes: pico, 0. 2-2 μm; nano, 2-20 μm; micro, 20-200 μm; meso, 200 μm-20 mm; macro, 20 mm-2 cm. Red dot indicates where phototrophy is acquired from prey within the same size range as the mixoplankton species. | 13 of 25 THE MIXOPLANKTON DATABASE are many species within the phytoplankton database of Righetti et al. (.
Global distribution of mixoplankton across Longhurst's biogeographical provinces. Distribution maps are shown for different MFT across different size classes (Y-axes in μm). The color-casts indicate the number of records (as log (n + 1)) for each combination of MFT and size class; white provinces indicate no data. The absence of maps indicates that there are no known members of MFT of that size class. See Figure 1 for MFT definitions. T A B L E 1 Ten most frequently recorded species for each MFT in OBIS database; species in bold are recorded as HAB forming mixoplankton in the database.
15507408, 2023, 4, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/jeu. 12972 by IFREMER Centre Bretagne BLP, Wiley Online Library on [10/07/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License T A B L E 2 Fifteen most frequently observed species in each MFT within each size range in OBIS database; species in bold are recorded as HAB forming mixoplankton in the database.
In some instances the number of species <15. "OBIS records" indicate the total number of observations per species in the OBIS database. Size class: femto, <0. 2 μm; pico, 0. 2-2 μm; nano, 2-20 μm; micro, 20-200 μm; meso, 200 μm-20 mm. a Number of Longhurst provinces (LP) where ≥10 observations for the species have been reported in OBIS.
F Sources of acquired phototrophy for plastidic-and endosymbiotic-specialist non-constitutive mixoplankton (pSNCM and eSNCM, respectively). Panels (A) and (B) show the allometric relationships between the mixoplankton and the source organisms for the acquired phototrophy. Panels (C) and (D) portray the taxonomic groups contributing photosynthetic material to pSNCM and endosymbionts to eSNCM, respectively. NR, not recorded. "Bacteria," purple sulfur bacteria. Size classes: pico, 0. 2-2 μm; nano, 2-20 μm; micro, 20-200 μm; meso, 200 μm-20 mm; macro, 20 mm-2 cm. Red dot indicates where phototrophy is acquired from prey within the same size range as the mixoplankton species. | 13 of 25 THE MIXOPLANKTON DATABASE are many species within the phytoplankton database of Righetti et al. (.
F Global distribution of mixoplankton across Longhurst's biogeographical provinces. Distribution maps are shown for different MFT across different size classes (Y-axes in μm). The color-casts indicate the number of records (as log (n + 1)) for each combination of MFT and size class; white provinces indicate no data. The absence of maps indicates that there are no known members of MFT of that size class. See Figure 1 for MFT definitions. T A B L E 1 Ten most frequently recorded species for each MFT in OBIS database; species in bold are recorded as HAB forming mixoplankton in the database.
In some instances the number of species <15. "OBIS records" indicate the total number of observations per species in the OBIS database. Size class: femto, <0. 2 μm; pico, 0. 2-2 μm; nano, 2-20 μm; micro, 20-200 μm; meso, 200 μm-20 mm. a Number of Longhurst provinces (LP) where ≥10 observations for the species have been reported in OBIS. mixoplankton OBIS prey MFT indicative size Species Taxonomic group records # LP a % LP b prey indicative size eSNCM meso Globigerina bulloides Foraminifera 44,885 50 92. 59 indicative size micro-meso CM eSNCM pico meso Micromonas pusilla Globigerinoides ruber Chlorophyta Foraminifera 1995 38,824 3 42 5. 56 77. 78 pico micro-meso CM eSNCM nano meso Heterocapsa rotundata Globigerinita glutinata Dinoflagellata Foraminifera 23,742 37,113 12 45 22. 22 83. 33 nano micro-meso CM eSNCM nano meso Prorocentrum cordatum Orbulina universa Dinoflagellata Foraminifera 22,446 25,162 24 44 44.
Aim: The Diabeloop Generation 1 (DBLG1) system is an interoperable hybrid closed-loop solution that was commercialized in Germany in March 2021. We report the longitudinal glycaemic outcomes among the first 3706 users in a realworld setting. Methods: We performed a retrospective data collection of all consenting adult patients with type 1 diabetes who were equipped in Germany with the DBLG1 system before 30 April 2022, and with a minimum 14 days of closed-loop usage. Results: In total, 3706 users (41% women, age 45. 1 ± 14. 5 years) met the inclusion criteria, reaching a mean follow-up of 131. 0 ± 85. 1 days, an overall 485 600 days of continuous glucose monitoring data, and a median time spent in closed-loop of 95. 0% (IQR 89. 1-97. 4). The median percentage time in range (70-180 mg/dl) was 72.
| INTRODUCTION Automated insulin delivery has been recently introduced as a very promising treatment for patients with type 1 diabetes (T1D). 2] [3] [4] [5]  Real-world data were then reported among large cohorts of patients worldwide, at least for some of these systems. 6, 7 Regarding the Diabeloop DBLG1 ® hybrid closed-loop solution, a randomized controlled trial comparing DBLG1 with sensorassisted pump therapy had led to the approval of its clinical use in Europe (CE-marking) in adult patients with T1D in November 2018. 3  The performance of DBLG1 was then reported under real life conditions after 6 months with 25 French patients. 8 The DBLG1 system was commercially launched in March 2021 in several European countries. As Germany is one of the countries where the system is most widespread and used, this appeared as an opportunity to collect reliable information about the use of the DBLG1 system in real life conditions.
| Insulin delivery Diabeloop Generation 1 system DBLG1 was commercially released in Germany during March 2021 as a hybrid, closed-loop insulin delivery system combining a Dexcom G6 ® CGM device, an insulin pump (Roche Insight ® ) and the DBLG1 software into a dedicated controller handset. Briefly, the DBLG1 system works by using personal medical parameters and adjustable algorithm action parameters to regulate insulin flows continuously every 5 min and keep the patient in the normoglycaemia range (between 70 and 180 mg/dl, configurable values). It combines an algorithm based upon machine-learning within a physiological framework with an expert system and self-learning algorithms. The patient is only requested to enter his or her meals and physical activities, preferably in advance. DBLG1 allows to customize the algorithm, through 10 different settings, to respond to the diversity of existing T1D physiologies (details in Benhamou et al.
| Study population We performed a retrospective data collection of all adult patients with T1D who were equipped in Germany with the DBLG1 system before 30 April 2022 and who gave their consent with data re-use for research purposes at the initiation of the system. Only patients with at least 14 days of closed-loop (days of closed-loop are those with 75% of glucose measurements under closed-loop) were included in the analysis. The full analysis set (FAS) was defined by all the patients who gave their consent and met inclusion criteria. As we did not have access to any clinical report form nor any additional data describing the condition of the patient before closed-loop initiation, we identified a subset of patients called the comparative analysis set (CAS), defined by all patients from the FAS population, in whom a baseline laboratory glycated haemoglobin (HbA1c) was available, ranging between 6% and 9% at the time when they were first equipped with DBLG1.