dataesr/scientific-paragraphs-categorization huggingface dataset:
Each deployment was analysed for 120 min as a standardised sampling unit. We first filtered the mid-water BRUVS dataset for all records of silky sharks. For consistency with the broader silky shark literature, in certain aspects of the analysis, we converted FL into total length (TL) with the equation: TL = -1. 16 + 1. 2 × FL  (Branstetter 1987). We also allocated individuals into three life history classes: young-of-year (YOY), juveniles and adults. These categories were based on reports that silky sharks are born at sizes between 57 and 87 cm TL  (Compagno 1984) , and thus YOY were those individuals < 87 cm or less. To categorise adults, we assigned our sampling locations to each of the central Atlantic, central Indian, eastern Indian, eastern Pacific, western Pacific and southern Pacific Ocean regions, as sexual maturity is reached at different lengths depending on the varying life history characteristics of specific populations by region  (Grant et al.
Silky shark distribution and abundance We evaluated the degree to which our mid-water BRUVS records reflect the predicted distribution of silky sharks from AquaMaps (www. aquam aps. org). AquaMaps is a tool for generating large-scale predicted range maps for marine species based on models which combine occurrence data from online databases with published literature  (Kaschner et al. 2019). We first downloaded the probabilities of silky shark occurrence on a 0. 5° × 0. 5° grid based on depth (m), sea surface temperature (°C), salinity (psu), primary productivity (mgC•m -3 •day -1 ) and distance from land (km) from AquaMaps. We then calculated the centroid of mid-water BRUVS sampling for each location within our dataset and associated mean relative abundance of silky sharks. Mean relative abundance was calculated as the average abundance of silky sharks per longline mid-water BRUVS deployment.
2018; Juhel et al. 2019; Letessier et al. 2019 ). These three metrics also reflect some historical impacts that have occurred before the onset of modern record keeping  (Pauly 1995; Pauly and Zeller 2016). Distance to the nearest human population was calculated using the LandScan 2016 database  (Dobson et al. 2000). Distance to market was defined as the distance to the nearest human density centre using the World Cities spatial layer (ESRI) where density centres are defined as provincial capital cities, major population centres, landmark cities, national capitals and shipping ports. We included both distance metrics as small populations may impact predators locally  (Bellwood et al. 2011 ) but impacts scale rapidly when industrialised markets are present  (D'agata et al. 2014). The distance to the nearest medium or large port was calculated using the World Port Index database (ESRI)  (Andrello et al.
Silky shark distribution The dataset contained 945 silky sharks on 413 deployments from 18 locations (Table  1 ). An examination of AquaMaps with our samples superimposed shows that no silky sharks were observed where they were not expected to be (Fig. 2 ). However, abundance was not correlated with probability of occurrence (Fig. 2 ). High abundances of silky sharks only occurred at three locations (8%) with a high probability of occurrence (probability > 0. 5). Conversely, low or zero abundances were observed at 10 locations (26%) with a low probability of occurrence (probability < 0. 5). Interestingly, 26 locations (66%) were categorised as having a high probability of occurrence yet were characterised by low or zero abundance of silky sharks. Of the 26 high probability-low abundance locations, 17 were within MPAs.
Discussion This rare opportunity to study a cosmopolitan mobile predator on a large spatial scale yields new insights into their distribution, abundance and demographics. BRUVS have facilitated marine management through enabling non-extractive sampling within previously difficult-to-access environments  (Letessier et al. 2014; Santana-Garcon et al. 2014; Letessier et al. 2019). The versatility of BRUVS in their application to both seabed and mid-water environments is demonstrated at the species and ecosystem-level, with insights gained into the impact of anthropogenic pressures on shark abundance  (Goetze et al. 2018) , size  (Letessier et al. 2019 ) and behaviour  (Juhel et al. 2019). Our study represents the first application of mid-water BRUVS to the silky shark across such a broad spatial scale and the creation of a baseline mid-water BRUVS dataset provides a valuable foundation for ongoing monitoring and management.
2019 ) which we did not observe. This suggests that either we did not observe any 'true' nurseries or that this generality does not apply to silky sharks. It is also possible that while our overall database is large, the number of YOY is too few to make inferences about nurseries. Despite the lack of life history segregation, the locations where we observed YOY did share some commonalities that enhance pup survival. Shark nurseries are typically characterised by warm waters, heterogenous bathymetry, high productivity and relatively high abundance of potential prey species  (Heupel et al. 2019)  as is the case at our Malpelo Island, Palau, Wandoo and British Indian Ocean Territory locations. Further research into the fine-scale environmental preferences of silky shark pups would facilitate the development of models specifically identifying potential nursery sites similar to that conducted by  Oh et al.
We thus suggest that overall, the use of MaxN, averaged across the longline deployment, is informative in terms of silky shark distributions. Conversely, we may have underestimated the abundance of silky sharks owing to the aforementioned inherent limitation of deploying midwater BRUVS at 10 m. A potential solution to determine whether observed absences were true absences or simply an inability of mid-water BRUVS to detect silky sharks would be to cross-reference mid-water BRUVS data with fisheries-dependent data to confirm if silky sharks are present beyond the 10 m depth limit. However, employing fisheries-dependent data is challenging due to their poor taxonomic resolution, particularly within the Carcharhinus genus  (Cashion et al. 2019; Henderson 2020). A natural progression of this work is the validation of mid-water BRUVS data with fisheries-dependent data, which is especially pertinent given the expansion of monitoring pelagic wildlife with mid-water BRUVS as part of the UK government's Global Ocean Wildlife Analysis Network.
Acknowledgements We thank the  Ascension Department of Fisheries, Australian Academy of Science, Australian Department of the Environment, Australian Marine Conservation Society , the  Bertarelli Foundation , the  Blue Belt Programme , the  Darwin Foundation ,  Fondation Total ,  Galapagos Science Center , the  Ian Potter Foundation , the  Jock Clough Marine Foundation ,  Margaret Middleton Fund ,  Migramar, National Environment Science Centre Marine Biodiversity Hub, National Geographic's Pristine Seas Program ,  NorthPort Marine ,  Outpost Expedition Pacific ,  Quest Maritime ,  RSPB Wildlife ,  Teach Green ,  UK Department of Environment, Food and Rural Affairs, Universidad San Francisco de Quito, Vermillion Energy  and the  Waitt Institute  for their generous donations which supported data collection.
Methods In this cross-sectional study, secondary weekly epidemiological cholera data covering the 2000-2021 period was extracted from the DRC Ministry of Health surveillance databases. The data series was divided into four periods: pre-MCEP 2003-2007 (pre-MCEP), rst MCEP (MCEP-1), second MCEP (MCEP-2) and third MCEP (MCEP-3). For each period, we assessed the overall cholera pro les and seasonal patterns. We analyzed the spatial dynamics and identi ed cholera risk clusters at the province level. We also assessed the evolution of cholera sanctuary zones identi ed during each period.
Study design and site We conducted a cross-sectional study of cholera outbreaks in the DRC using weekly national surveillance data of suspected cholera cases and deaths from 2000 to 2021. We also analyzed biological data of clinical Vibrio cholerae isolates obtained from the National Institute of Biomedical Research (NIBR) from 2015-2021. We reviewed the three MCEPs developed in the DRC and the evaluation reports of the rst two MCEPs. The DRC is located in the heart of Africa and covers an area of 2,345,000 km 2. In 2020, the country had an estimated population of more than 80 million inhabitants  (17). The Congo River crosses almost the entire country from east to west over 4,700 km. The DRC's lakes are primarily located in the east of the country, including Lake Tanganyika, Lake Kivu, Lake Mweru, Lake Edward and Lake Albert.
Surveillance data sources The weekly epidemiological data from 2000-2021 were obtained from the DRC Ministry of Health. The Integrated Disease Surveillance and Response (IDSR) system has been in place in the DRC since 2000, managed by the Ministry of Health in collaboration with WHO. The IDSR system is a syndromic surveillance system that compiles weekly reports of morbidity and mortality, aggregated at the health zone level. The IDSR system covers thirteen diseases with epidemic potential, including cholera, for passive surveillance outside of epidemic periods. Passive surveillance is coupled with active surveillance during epidemics. The IDSR system uses two de nitions for suspected cholera cases depending on whether a cholera epidemic has been declared by the Ministry of Health (IDSR, 3rd edition): In areas without a declared cholera epidemic: any patient two years of age and older with acute watery diarrhea and severe dehydration or death due to acute watery diarrhea.
Microbiological data Biological con rmation data were obtained from the national cholera reference laboratory at the NIBR. The NIBR performs routine culture con rmation tests for national surveillance and epidemic con rmation (data available from 2015 to 2021). Fecal samples or rectal swabs from suspected cholera cases, which are typically collected at the beginning, middle, and end of a cholera outbreak, are placed in Carry-Blair transport medium and transported to the NIBR for culture con rmation. For each sample tested at the NIBR, the following data were collected: year, number of samples taken and number of positive results.
Data organization and analysis Secondary weekly epidemiological data were extracted from the DRC Ministry of Health surveillance databases from 2000 to 2021. The extracted data were cleaned and analyzed for weekly cases and deaths using Microsoft Excel and R software. To analyze the data throughout the study period, we applied the administrative divisions that were adopted in 2015 (26 provinces, instead of the previous 11 provinces) for the 2000-2014 data  (19). Epidemic curves at the national and provincial levels were produced to assess the temporal evolution of cholera epidemics before and during the implementation of each MCEP. Weekly and annual changes in case fatality rates were calculated with cholera deaths as the numerator and cholera cases as the denominator. To compare the periods before and during the implementation of each MCEP, the data series was divided into four periods: the pre-MCEP period 2003-2007 (pre-MCEP), the rst MCEP period 2008-2012 (MCEP-1), the second MCEP period 2013-2017 (MCEP-2), and the third MCEP period 2018-2021 (MCEP-3).
The averages of these four periods were compared using ANOVA. To perform the ANOVA, we rst tested the data for normal distribution using the Chapiro-Wilk test, which indicated that the data are normally distributed (threshold: p > 0. 05)  (17). We then veri ed variance homogeny using the Bartlett test, indicating that the data are statistically homogeneous (threshold: p > 0. 05)  (21). We used the con dence interval method according to which the means of the two groups are not different when the con dence intervals overlap. We identi ed seasonal patterns by decomposing the weekly time series using R software as described by Cleveland et al. (22). This analysis was performed on three ve-year time series (pre-MCEP, MCEP-1 and MCEP-2) and one four-year time series (MCEP-3).
Cartography To assess the spatial dynamics of cholera cases, we produced maps of average attack rates for the four periods (pre-MCEP, MCEP-1, MCEP-2 and MCEP-3) at the province level. Average attack rates were calculated as follows: average sum of cases for the period*100,000 /average population for the period. The calculated attack rates are expressed as cholera cases per 100,000 inhabitants. Attack rate maps were generated using QGIS 3. 16 Madeira software with shape les obtained from www. DIVA-GIS gis. org/gdata.
Cluster analysis For each time period, we performed a retrospective cluster analysis using a Poisson-based space-time permutation scan statistic according to Kulldorff et al. (23)  with SaTScan software version 9. 6. We identi ed risk clusters at the provincial level with a reactive risk > 1 and a p-value > 0. 05  (24). These risk clusters were then mapped using QGIS 3. 16 software.
Discussion The retrospective analysis of cholera surveillance data from 2000 to 2021 revealed that this disease remains a public health threat in the DRC since 2008 despite the implementation of the MCEP. During the study period, the DRC recorded 520,024 cases and 12,561 deaths (case fatality rate of 2. 4%). Over the 22year period, all provinces and 498 of 518 (96%) health zones reported suspected cholera cases. Although the endemic provinces recorded more than 75% of all cases during each period, the proportion of cases in the endemic provinces decreased over time, while the proportion of cases in the non-endemic provinces increased. The high case fatality rate of 2. 4% (25) may be due to delays in the surveillance system to trigger a rapid response (26) and/or delayed access to healthcare facilities among patients because of insu cient knowledge about cholera or distance from health centers  (27).
1-1. 1 km s -1 ). Integration times on source and other observational parameters are given in Table  1 , while the detected spectral line frequencies of interest to the present study are shown in Table  2. Noisy outlier data points were identified and flagged (removed) through inspection by Joint ALMA Observatory (JAO) staff. The raw data (visibilities) were further flagged and calibrated using the CASA software (version 5. 4; CASA Team et al. 2022), using standard scripts supplied by the JAO. Prior to imaging, the visibilities were continuum-subtracted using a second-order polynomial fit to the line-free channels in each spectral window. The time-resolved interferometric data series was Doppler-corrected to the rest frame of the comet using the CASA cvel task, with cubic spline interpolation between the frequency channels.
Imaging was performed using the CASA tclean (Clark) algorithm with natural weighting. A pixel size of 0 1 was used for the band 6 data and 0 05 for band 7. Deconvolution of the spatial point-spread function (PSF) was performed within an 8″ diameter circular mask centered on the comet and stopping at a flux threshold of twice the rms noise level (σ). The resulting image cubes were corrected for the response of the ALMA primary beam and then transformed from celestial coordinates to sky-projected distances with respect to the center of the comet, which was determined from the peak of the HCN emission (in settings 1 and 2) and the peak CH 3 OH emission (in setting 3). The coma gas and continuum (dust plus nucleus) emission peaks were both found to be consistent (within 0 2) with the JPL Horizons ephemeris position.
The excitation calculation in our modeltakes into account radiative cooling of rotational levels, (de-)excitation by collisions with H 2 O and electrons, and pumping by the solar radiation field as the gas moves outward (see  Cordiner et al. 2022). The impact of coma opacity on the molecular excitation is negligible for the observed species in this comet, so radiation trapping effects are not included. We adopt H 2 O-HCN collision rates from Dubernet & Quintas-Sánchez (2019; also assumed to apply to HNC), while collision rates of H 2 O with CH 3 OH, H 2 CO, CH 3 CN, and CS are assumed to be the same as for H 2 and have been taken from the LAMDA database (van der  Tak et al. 2020 ). An electron density scaling factor of x ne = 0. 2 is used, following the recommendations of  Hartogh et al. (2010)  and  Biver et al.
(2019 Biver et al. ( , 2021)). Pumping rates for HCN, HNC, H 2 CO, and CS were calculated as described by  Cordiner et al. (2019)  and  Roth et al. (2021b)  based on the method of  Crovisier & Encrenaz (1983) , with rovibrational transition data from the latest versions of the HITRAN and GEISA catalogs  (Delahaye et al. 2021; Gordon et al. 2022 ). The CH 3 OH pumping rates were calculated similarly. However, the Einstein A coefficients for CH 3 OH in the HITRAN catalog were found to be incorrect because they were calculated by considering corrupted statistical weights and partition functions. On the other hand, the HITRAN line intensities at 296 K are accurate, so by converting these to A coefficients and employing accurate statistical weights and partition functions  (Villanueva et al. 2012 ), the required spectroscopic information could be recovered.
Acknowledgments This work was supported by the  National Science Foundation  under grant Nos. AST-2009253  (M. A. C. ) and  AST-2009398  (B. P. B. ). The work of M. A. C. , S. N. M. , N. X. R. , and S. B. C. was also supported by  NASA's Planetary Science Division Internal   Scientist Funding Program  through the  Fundamental Laboratory Research  work package (FLaRe). This work makes use of ALMA data set ADS/JAO. ALMA# 2018. 1. 01114. S. Part of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under  NASA  contract  80NM0018D0004. ALMA is a partnership of ESO (representing its member states), the  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada)  and  NSC  and  ASIAA (Taiwan) , in cooperation with the  Republic of Chile. The  Joint ALMA Observatory  is operated by  ESO ,  AUI/NRAO  and  NAOJ.
An increasing woGRO along the width of a specimen (if existing) was oriented in the same direction (to the right in the image). The images were saved in a 24 bit jpg-format and enhanced in ImageJ software  (Abramoff et al. 2004). Growth ring-unrelated information in the images such as patterns from a rough surface related to the sawing were filtered with a Fast Fourier Transform (FFT) programme, allowing the retention of larger scale features such as the growth rings, whilst smoothing the low frequency texture of the wood surface. The contrast of the filtered images was increased by histogram equalisation prior to the application of the structure tensor algorithm. The local GROs did not differ much in the thickness direction of each specimen, and hence the average local GRO of the left, middle and right regions was calculated for further analysis, each region comprised a third of the specimens' width (Figure  3 ).
All the experimental conditions for LC-MS/MSMS acquisitions were as previously described. 17 MS/MS data were processed with an in-house Mascot search server (Matrix Science, Boston, MA; version 2. 4. 1). The mass tolerance was set to 7 ppm for precursor ions and 0. 5 Da for fragments. The maximum number of missed trypsin cleavages was limited to two. The SwissProt database with the Homo sapiens taxonomy was used for the MS/MS identification step.
| Network analysis of Dyrk1A interactome The affinity purification-mass spectrometry experiment produced a list of proteins that are expected to interact with Dyrk1A, either directly or indirectly. Therefore, to restore the connectivity in the Dyrk1A interactome, we submitted the list of candidate partners to the Proteo3Dnet web server. 18 Briefly, the method finds proteinprotein interactions (PPIs) by integrating data from three sources: the Protein Data Bank (PDB),  19  the Eukaryotic Linear Motif (ELM) resource,  20  and the Biological General Repository for Interaction Datasets (BioGRID) database. 21 For a pair of candidate partners, the program searches for experimental structures in the PDB that gather both proteins. For case when no multimeric structure can be found, a distant homology search is performed: if both proteins have homologues located in the same 3D complex, they are then predicted as potential interactants (also called 'interologs'), providing that the interaction has been conserved throughout evolution.
The ELM database is exploited by searching for the presence of a short linear motif (SLiM) in one protein, and for the corresponding Pfam domain in the other. The two candidate partners are thus expected to interact in a transient manner. The detection is conditioned to the location of the SLiM within an intrinsically disordered region, as predicted by the IUPred2A software. 22 Experimental proteomics data from BioGRID are also integrated to add nodes to the network, which may serve as (indirect) connections between the input proteins. This BioGRID analysis only keeps the most robust experimental data, by excluding those associated with the 'Far Western', 'Co-fractionation', 'Co-localization', 'Biochemical Activity' and 'High Throughput' experimental systems. The default parameters have been used for Proteo3Dnet which thus adds 15 nodes from BioGRID.
| Human platelet isolation and protein analysis Human blood was collected on acid citrate dextrose solution and washed platelets were obtained by standard procedures within 2-3 h. 26 All patients were recruited in France, informed about the anonymous use of their data and gave written informed consent in accordance with the Declaration of Helsinki. Platelet lysates were prepared in lysis buffer with a cocktail of proteases and phosphatases inhibitors. Protein concentrations were determined with the Bio-Rad Protein Assay reagent (Bio-Rad). To assess the relative amount of Dyrk1A, 200 mg of proteins were incubated 2 h at 4°C with 2 μg rAb anti-Dyrk1A 7D10 (Abnova corporation, Tebu, France). The immunocomplexes were incubated with protein A-Sepharose protein G-Sepharose overnight at 4°C and the beads were washed.
| Data analysis Statistical analysis was done with the Student's t-test using Statview software (Statview 3, Abacus Corporation). For multiple pairwise comparisons between genotypes and treatments, statistical analysis was done with two-way anova followed by the Bonferroni/Dunnet post hoc test using Statview software. The results are expressed as mean ± SEM (standard error of the mean). Data were considered significant when p < 0. 05.
The list of 20 candidate partners was submitted to the Proteo3Dnet server for a preliminary analysis, which led to adding the undetected DNA damage-binding protein 1 (DDB1) to the input list. A second analysis of the 21 proteins was then performed and a graph representation of the computed protein-protein interaction network was thus generated (Figure  1 ). Among the 21 input proteins, six remained as isolated nodes (data not shown). Among the 15 connected input proteins, only nine interacted directly with each other, into two distinct clusters. The first cluster is made of three fibrinogen chains (FIBA, FIBB and FIBG, respectively). The second cluster is composed of Dyrk1A (DYR1A), the glucocorticoid-induced transcript 1 protein (GLCI1), the E3 ubiquitin-protein ligase RNF169 (RN169), the trophinin-assisting protein (or tastin, TROAP), the DNA damagebinding protein 1 (DDB1) and the DDB1-and CUL4-associated factor 7 (DCAF7).