dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and Discussion To summarize, several pre-processing procedures were performed on the displayed dataset, including data correction and NA transformation, quality code correction, sensor range correction, as well as temporal alignment. Indeed, high frequency fluorescence data from MAREL Carnot station can contribute to the calibration of satellite observations. In addition, MAREL Carnot perform measurements of nutrients that are not provided by current spacecraft techniques. Hence, it is much more effective than satellites at monitoring water quality  (Lefebvre & Schmitt, 2016). Nonetheless, nutrient signals such as phosphate, nitrate, and silicate are only available until 2010. This is caused by a previous sensor failure and the inability to replace it. Indeed, scientists from several disciplinary backgrounds have utilized MAREL Carnot data to accomplish a wide range of research objectives.
In the following paragraphs, we will go over some of the most significant findings from several research efforts. The scientific community that is interested in the MAREL Carnot dataset may find this evaluation useful in determining which topics may or may not require further study based on the results of this evaluation. In general, this dataset allows researchers to investigate the dynamics of phytoplankton as well as detect blooms caused by human activities and/or climate change. For instance,  Rousseeuw et al. , (2015)  developed an unsupervised Hidden Markov Model for monitoring the marine environment, specifically for detecting algal blooms and understanding phytoplankton dynamics. In his unsupervised Hidden Markov Model, uHMM parameters were estimated using spectral clustering rather than the commonly used iterative Expectation Maximization.
The results obtained using MAREL Carnot dataset showed that the proposed system is efficient to detect the main productive and non-productive periods, as used for the purposes of the EU Water Framework Directive to assess good environmental status, and refine knowledge about phytoplankton bloom dynamics in a temperate ecosystem, temporarily dominated by a harmful algae, Phaeocystis globosa. Thus, the suggested uHMM system successfully characterizes phytoplankton dynamics from new incoming data (in near real-time), and will enable researchers to gain a better understanding of the main controlling or forcing parameters (e. g. , nutrient pressure, light availability, turbidity), the environmental status (e. g. , phytoplankton biomass), and the direct and/or indirect effects of such blooms (e.
g. , oxygen concentration). Most importantly, the ability of uHMM to establish environmental states represents a clear potential to better understand what a good environmental condition is, as defined and applied for the needs of the WFD, MSFD, or other regional sea conventions such as OSPAR. Even though uHMM was only applied to the MAREL Carnot dataset, it could contribute to the processing of huge multivariate time series generated by high resolution platforms, which are increasingly used for the integrated observation of pelagic ecosystems and biogeochemical cycles in oceans  (Rousseeuw et al. , 2015). On the other hand,  Grassi et al. , (2019)  suggested a Multilevel Spectral Clustering (M-SC) to split multivariate time series from general patterns to extreme events without a priori knowledge.
The results obtained from MAREL Carnot dataset have shown that we can extract knowledge on dynamics of events or environmental states. In addition, it was shown that M-SC allows unsupervised labelling of time series, which is a basic part of machine learning and is needed to build an event prediction system and come up with sampling strategies that work close to real time  (Grassi et al. , 2019). As a result, scientists will be able to create a HAB early warning expert system to warn shellfish farmers, and prevent both public health risks and commercial losses in the shellfish farming business. Nevertheless, datasets, notably MAREL Carnot, are typically incomplete and contain a significant amount of missing data due to sensor failures, communication/transmission difficulties, or poor weather conditions for manual measurements or maintenance.
Phan et al. , (2018)  proposed Dynamic Time Warping method to fill in successive missing values of univariate time series as well as low uncorrelated multivariate time series  (Phan et al. , 2017). Furthermore, to compare alternative approaches to studying and understanding HABs, all researchers must have access to the same datasets. For instance, researchers may now assess the effectiveness of new and old machine learning algorithms in understanding the dynamics and forecasting harmful algal blooms. Thus, a comparative study of clustering approaches applied to spatial or temporal pattern discovery gave promising results in the segmentation of both UCI databases and marine time series compared to other approaches  (Grassi et al. , 2020). Therefore, we may conclude that the MAREL Carnot dataset is beneficial not just for marine ecologists, but also for machine learning specialists and data scientists.
It is worth mentioning that all the above algorithms are available and published on the Comprehensive R Archive Network (CRAN). Another study performed by  Derot et al. , (2020)  analysed how forecasts of phytoplankton blooms are impacted by different sampling frequencies. They applied Random Forest (RF) and sliding window strategy on 12 parameters derived from MAREL Carnot dataset. This research demonstrated that the sampling frequency has a direct impact on the forecast performance of a Random Forest (RF) model as high-frequency datasets might provide useful information to the RF. Furthermore, this type of model sets the groundwork for the creation of a numerical decision-making tool that could help mitigate the impact of algal blooms, and can recreate interactions that closely resemble the real biological processes  (Derot et al.
, 2020). Moreover, MAREL Carnot dataset is useful for studying turbulence. In fact, many fields in the marine environment fluctuate over a wide variety of geographical and temporal scales. To study their dynamics and estimate their variations at all scales, high frequency measurements are needed  (Huang & Schmitt, 2014). Hence,  Derot et al. , (2015)  investigated phytoplankton biomass during bloom by applying Empirical mode decomposition (EMD) on fluorescence dataset from MAREL CARNOT. Results revealed that bloom events include considerable internal variations. In other words, blooms are not smooth and "mountain-like", but exhibit high frequency oscillations due to turbulent advection and complex population dynamics  (Derot et al. , 2015). Similarly,  Huang & Schmitt. , (2014)  analysed time dependent intrinsic correlation analysis of temperature and dissolved oxygen time series using empirical mode decomposition.
The anti-correlation between temperature and oxygen showed that higher temperatures may favor larger phytoplankton growth rate, and hence, with a time delay, a lower percentage of oxygen  (Huang & Schmitt, 2014). In addition,  Zongo & Schmitt, (2011)  demonstrated that pH fluctuations in marine waters are strongly influenced by turbulent hydrodynamical transport, and may be considered as a turbulent active scalar  (Zongo & Schmitt, 2011). Overall, the MAREL Carnot station provides automatic, continuous, and long-term observation of various physiochemical and biological parameters that allow for monitoring the general quality of marine environment, detecting harmful algal blooms (HAB) and understanding phytoplankton dynamics. Hence, MAREL Carnot dataset aligns with objectives of SRN (Suivi Régional des Nutriments in French, Regional Nutrients Monitoring Program), especially by assessing the influence of continental inputs on the marine environment, and their implication on possible eutrophication, and can assist in estimating the effectiveness of development and management policies in the marine coastal zone  (Alain & David, 2022).
In other words, MAREL Carnot is the first sampling station for the SRN transect. Thus, it assists in understanding phytoplankton dynamics by determining recurrent, extreme and rare events. Furthermore, MAREL Carnot dataset can contribute to both REPHY (Observation and Surveillance Network for Phytoplankton and Hydrology in coastal waters) (https://doi. org/10. 17882/47248), and REPHYTOX (Monitoring Network for Phycotoxins in marine organisms) (https://doi. org/10. 17882/47251). Actually, the goal of REPHY is to measure the biomass, abundance, composition, and hydrological parameters of marine phytoplankton in coastal and lagoon waters. REPHYTOX is designed to find and track three types of toxins that can build up in bivalve mollusks and cause DSP (Diarrheic Shellfish Poisoning), PSP (Paralytic Shellfish Poisoning), and ASP (Amnesic Shellfish Poisoning)  (Belin et al.
, 2021). Monitoring carried out by MAREL Carnot in parallel with REPHY and REPHYTOX permits continuous adaptation to the objectives, developing analysis strategies with extensive and complex data, thereby ensuring sustainability, which were challenges faced by REPHY and REPHYTOX before. Thus, the contribution of MAREL Carnot to improve assessment based on low frequency renders it important to achieve the objectives of WFD (Water Framework Directive) and MSFD (Marine Strategy Framework Directive). Besides, the lighthouse's sensors provide valuable data for meterological research and may improve local weather forecasts by measuring variables including wind speed, wind direction and air temperature. Additionally, depending on the goals, some of the parameters determined by our station can actually be useful for fisheries research when making the link between the different trophic levels.
While MAREL Carnot has made substantial progress toward automating marine ecosystem monitoring, there are still some significant challenges to overcome. In fact, it can be interrupted by rough sea conditions, such as strong tidal currents and storms. In addition, biofouling presents a major problem for sensors in the coastal environment, which explains why only a few moored autonomous systems have been deployed in the coastal environment despite their ease of maintenance  (Blain et al. 2004 ). Also, new EOV (Essential Ocean Variables) and EBV (Essential Biodiversity Variables) might be added with time. This adds a further obstacle, as it may be necessary to install brand-new sensors for the updated parameters. Above all, the major challenge will continue to be the issue of missing values, especially when it comes to data that has been missing for a long time, as in the case of nutrients, where nitrate, phosphate, and silicate observations have been missing since 2010 due to sensor failure.
In future work, we plan to use a multi-scale, multi-source, multi-criteria, and multi-parameter approach to characterize and predict harmful algal blooms in the Eastern English Channel caused by Phaeocystis globosa and Pseudo-nitzschia spp. We will do this by combining high frequency datasets from MAREL Carnot, satellite, and modeling data with low frequency datasets from other sources. This integrated observing system will be used to identify environmental states present in the region, and develop an early warning system that can anticipate harmful algal blooms.
Data Availability The raw data are present on the official Coriolis website. "These data were collected and made freely available by the Coriolis project and programmes that contribute to it (http://www. coriolis. eu. org). " The dataset after quality control procedures are present on the SEANOE website (DOI: 10. 17882/39754)  (Lefebvre, 2015). In fact, our data are made available according to the FAIR approach (Findable, Accessible, Interoperable, Reusable).
Conclusion In conclusion, this high frequency dataset is useful in many scientific fields, such as phytoplankton ecology, data science, and turbulence. It can be used to describe and predict harmful algal blooms in the Eastern English Channel, which is important to warn shellfish farmers and prevent economic losses and health problems. It can also be used with satellite, modeling, and lowfrequency in situ data to achieve better knowledge and understanding of the marine ecosystem. Most importantly, this data set has been shown to be useful for fulfilling the goals set by the Water Framework Directive (WFD), the Marine Strategy Framework Directive (MSFD), and the Oslo and Paris Conventions (OSPAR). This would help researchers in the future get better results, which would lead to more scientific progress.
Acknowledgement This PhD project is funded by the  Office Français de la Biodiversité (OFB)  and by  Ifremer  (grant agreement  OFB. 21. 0578 ). This work has been financially supported (i) by the  European Union (ERDF) , the  French State , the  French Region Hauts-de-France and Ifremer , in the framework of the project  CPER   MARCO 2015-2021 , (ii) by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  654410  in the framework of the project  JERICO S3 , and (iii) by the  Artois Picardy Water Agency.
( Photosynthetically Active Radiation) variable. Then, we discovered some data for nutrients present at Level 2. Since MAREL Carnot doesn't contain Level 2 data, we knew that these nutrients belong to Level 1, but were wrongly introduced into Level 2. Hence, we made sure values are similar at Level 1 and Level 2, and remove all values from Level 2. In addition, missing values in datasets are typically represented as NA, which stands for Not Available. However, in some cases, NA values are replaced with other numbers such as77. 77, 7777, 999, 999. 999, 9999. 99. Etc. A dataset may also include values like Inf, which stands for infinity, and Nan, which stands for Not A Number. Because these types of observations can.
meadow within each year, and planted plots vs. unvegetated within each year, and determined the species which varied the most. All multivariate analyses were run using the vegan package in R version 3. 6. 2. We then used GLMMs (with normal distribution for diversity and functional metrics, negative binomial distribution for most invertebrate densities; see Tables  S3  and S4  for all distributions) to determine if there were differences between treatments and years for total epifauna and infauna density, selected taxonomic groups (Bivalvia, Gastropoda, Amphipoda, Copepoda, Decapoda [epifauna only], Ostracoda [epifauna only], Chironomidae, Halacaridae [epifauna only], Polychaeta, Oligochaeta [infauna only], Nematoda, diversity and functional metrics).
Acknowledgments This project was funded by the  Swedish Research Council (FORMAS) Dnr. 2019-01192. KG was funded by the  Maj and Tor Nessling Foundation  (grant  201900244 ) and the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement No  730984 ,  ASSEMBLE Plus  project). The eelgrass restoration was carried out in collaboration with the  County Administrative Board of Västra Götaland  and financed by the  European Maritime and Fisheries Fund , and the  Swedish Agency for Marine and Water Management. The authors thank  Maria Asplund  and  Carmen de los Santos  for field assistance,  Kevin L. Haffer  for lab assistance, and  Kristineberg Marine Research Station  for providing excellent working facilities. The authors declare no conflicts of interest.
Three further case studies Before spelling out more details of our analysis, let us briefly illustrate its general workings with three further case studies, namely Buli, Hausa and Wolof. Unless otherwise specified, the data are obtained from elicitation, conducted by e-mail, video call and/or in person with a total of 13 consultants, six for Hausa, six for Wolof and one for Buli. Aside from translations and felicity judgments with contexts, we also used visual stimuli, which were partly taken from the questionnaire developed by  Skopeteas et al. (2006)  and partly self-made. Tone is transcribed where the consultants indicated it.
Two general predictions Both sub-types of FocusTheoryE in (  22 ) entail the following two predictions: Uniform Marking: There is some property that holds equally of all foci in the language, regardless of size, category, or grammatical function. Downward Syncretism: Any broad (i. e. multi-word) focus is syncretic to one or more smaller foci. It is probably obvious why Uniform Marking follows from FocusTheoryE in (  22 ): any focus contains the nuclear pitch accent. As for Downward Syncretism, it should be transparent how it follows under theories on which focus "projects" from an accented terminal to dominating nodes. More generally 'having default prosody' is preserved under syntactic dominance: if a constituent X has default prosody, then every sub-constituent of X also has default prosody, and if X contains the NPA and is not terminal, one of its sub-constituents contains the NPA, and has default prosody, i.
Virus isolates and culture JEVNakayama (GenBank: EF571853), JEVFU (GenBank: AF217620), and Imojev were a gift from Assoc. Prof. Gregor Devine (QIMR Berghofer MRI). JEVNSW/22 (GenBank: OP904182) was a gift from Dr. Peter Kirkland (Elizabeth Macarthur Agriculture Institute, New South Wales, Australia). MVEVTC123130 (GenBank: JN119814. 1) was obtained from the "Doherty Virus Collection" currently held at QIMR Berghofer MRI. Virus stocks were generated by infection of C6/36 cells (for JEV and MVEV) or Vero E6 cells (for Imojev and YFV 17D) at multiplicity of infection (MOI) ≈0. 1, with supernatant collected after ~5 days, cell debris removed by centrifugation at 3000 x g for 15 min at 4°C, and virus aliquoted and stored at -80°C. Virus titers were determined using standard CCID50 assays (see below).
Histopathology and immunohistochemistry Brains and spleens were fixed in 10% formalin and embedded in paraffin. Human cortical brain organoids were embedded in agarose by adding 4% melted agarose and incubating on ice to solidify, prior to standard paraffin embedding. Sections were stained with H&E (Sigma Aldrich) and slides were scanned using Aperio AT Turbo (Aperio, Vista, CA USA) and analyzed using Aperio ImageScope software (LeicaBiosystems, Mt Waverley, Australia) (v10). For anti-flavivirus NS1 immunohistochemistry using 4G4, sections were affixed to positively charged adhesive slides and air-dried overnight at 37°C. Sections were dewaxed and rehydrated through xylol and descending graded alcohols to water. Sections were transferred to Dako Epitope Retrieval Solution (pH 9. 0) and subjected to heat antigen retrieval (100°C for 20 min) using the Biocare Medical de-cloaking chamber, and slides allowed to cool for 20 minutes before transferring to TBS plus 0.
Statistics Statistical analyses of experimental data were performed using IBM SPSS Statistics for Windows, Version 19. 0 (IBM Corp. , Armonk, NY, USA). The t-test was used when the difference in variances was <4, skewness was > -2 and kurtosis was <2. Otherwise, the nonparametric Kolmogorov-Smirnov test was used. Kaplan-Meier statistics were determined by log-rank (Mantel-Cox) test. Area under the curve analysis was performed in GraphPad Prism 8.
JEVNSW/22 infection produces a viremia but is not lethal in C57BL/6J mice The JEVNSW/22 G4 virus was isolated from the brain of a stillborn piglet in New South Wales (NSW), Australia in February 2022 (GenBank accession OP904182). The JEVNakayama G3 prototype was isolated in Japan in 1935 (GenBank accession EF571853), and the JEVFU G2 was isolated in Australia in 1995  34, 35  (GenBank accession AF217620). MVEV was isolated in Australia in 1974 36 (MVEVTC123130, GenBank accession JN119814). The latter three viruses were isolated from human patients. Brief descriptions of the viral isolates and their phylogenic relationships with other flaviviruses are provided in Supplementary Figure  1. Six week old adult C57BL/6J mice were infected subcutaneously (s. c. ) with 5x10 5 CCID50 of the aforementioned viruses.
C57BL/6J mice infected with JEV or MVEV led to viral neuroinvasion, apoptosis and reactive astrogliosis The brains of C57BL/6J mice were analysed by immunohistochemistry (IHC) using the panflavivirus anti-NS1 antibody (4G4). Staining was consistently seen in the cortex for JEV and MVEV infected mice, with staining also seen in the thalamus (Figure  2A , Supplementary Figure  3A-C ). Prevailing infection of the cerebral cortex and thalamus parallels IHC data from postmortem human brains  6, 37. In some mice, virus was detected in other brain regions, including the hippocampus, anterior olfactory nucleus (Figure  2A , JEVNakayama), and caudate putamen (Supplementary Figure  3B , JEVFU). A JEVFU infected mouse that lost ~15% body weight and then recovered (Figure  1E ) had residual virus staining in the cortex at day 29 post infection (Supplementary Figure  3D ), while a mouse that survived infection with minimal weight loss had no detectable viral antigen staining (Supplementary Figure  3E ).
Publically available RNA-Seq data of mouse brains infected with JEV A publically available RNA-Seq data (raw read counts) of C57BL/6 brain infection with JEV isolate P20778 (Vellore strain, genotype 3)  52  (GEO accession: GSE154002) was reanalyzed 53 to provide a list of differentially expressed genes (DEGs) for infected brains versus uninfected brains from C57BL/6 mice. GFAP was significantly up-regulated 5. 4 fold by infection (Supplementary Table  2 , log2FC 2. 4), consistent with the GFAP IHC (Figure  2C ) and reactive astrogliosis seen herein. Iba1 (Aif1) was also significantly up-regulated 5. 5 fold (Supplementary Table  2 , log2FC 2. 5), consistent with the H&E and IHC (Figure  5 ) and microgliosis observed in our study. To provide potential insights into the nature of the cellular infiltrates (e.
JEVNSW/22 also has an additional N-linked glycosylation site at position 175 in NS1 that is lacking in JEVNakayama and JEVFU (Supplementary Table  1 ). However, this N-linked glycosylation site is reported to increase WNV neuroinvasiveness in wild-type mice 90,91 , a trend not seen in our JEV data (Figure  1F ). Thus JEVNSW/22 shows no obvious sequence characteristics that can be ready associated with reduced virulence. Limitations of this study include the use of JEVNSW/22, a pig isolate; however, there are only 4 amino acid differences between this isolate and a JEV G4 sequence from a human brain in the Tiwi Islands (Northern Territory, Australia) in 2021 (Genbank accession OM867669}  17  ). The differences are; envelope-238 F vs. L, NS2A-71 I vs. T, NS2B-59 E vs. G, NS3-436 E vs. G. In addition, JEVNakayama was passaged in suckling mouse brains, which may contribute to the increased neuroinvasion seen herein, although the adaptive mutations acquired during passaging, if any, are currently unknown.
ACKNOWLEDGEMENTS From  QIMR Berghofer MRI  we thank  Dr I Anraku  for managing the PC3 (BSL3) facility, animal house staff for mouse breeding and agistment,  Drs Anthony White  and  Lotta Oikari  for providing the iPSC line,  Crystal Chang ,  Sang-Hee Park andAshwini Potadar  for the histology and immunohistochemistry,  Dr Viviana Lutzky  for proof reading. We thank  Dr. Peter Kirkland  for supplying the JEVNSW/22 isolate. We thank  Dr. Andrii Slonchak  for insights into whether 3'UTR changes may affect sfRNA production.
Loss of hoxa1a results in defective valve development in zebrafish The in vivo effects of the HOXA1 variants were evaluated using a zebrafish (Danio rerio) model. Zebrafish has one HOXA1 ortholog, hoxa1a (Supplementary Fig. 1 )  28. Hoxa1a is expressed in the midbrain and anterior hindbrain during early zebrafish development, which corresponds to a more rostral extension of expression than for HOXA1 in mammals (Supplementary Fig. 1 )  29. Moreover, functional data suggest that a paralog, hoxb1b, may actually perform the same hindbrain patterning role as the mouse Hoxa1 28. Therefore, we targeted both hoxa1a and hoxb1b using antisense morpholino oligonucleotides (MO) targeting the AUG start site to block translation. At 3 days post-fertilization (dpf), both hoxa1a and hoxb1b morphants displayed gross developmental defects including a heart edema (Supplementary Fig.