dataesr/scientific-paragraphs-categorization huggingface dataset:
Performance Requests are in general executed in three phases: • First the initial entry point (event, dataset, run, stream or version) is searched for. This part could be optimized by using natural order, indexes, Elastic Search, Spark or more hierarchical navigation. • The graph is navigated. This part is very fast. The navigational database access time is small in comparison with the data management in the application code. • Finally the results are accumulated. Data can still be accessed directly, without Graph Database API, so the same performance as non-Graph Database can be easily achieved. The navigation step (instead of sub-search) can speed it up. In general, the system exhibits very fast retrieval and slower import, as the latter creates structure that are then used in faster and simpler search.
Using Graph Database extends the parallel-ready functional model from code to data! Relations (edges) can be considered as functions, navigation as a function execution. From the user point of view, there is no difference between creating a new object and navigating to it. Both operations can be lazy (i. e. executed only when needed). Functional processing and graph navigation (Graph Oriented Programming  [4] ) can work well together. They use the same functional syntax. Both are an actual realization of Categories  [19] , where Vertex is an Object and Edge is a Morphism. Functional program can be also modeled as a graph and graph data can be navigated using functions. Graph data are ready for parallel access.
Graph Database for Deep Learning Graph Neural Networks create a natural environment for Deep Learning. A Neural Network is a graph so Graph Database is a natural environment to describe Neural Network itself. In many cases, Neural Network handles graph data (objects with relations), operating either on the individual nodes (Node-focused tasks) or on the whole graph (Graphfocused tasks). Graph Neural Networks can be seen as generalization of a (non-geometric) Convolutional Neural Network. That opens possibilities to impose contraints/knowledge to Neural Networks, either via Inductive Bias or Semantic Induction. More detailed information and references can be found in  [20].
ATLAS Event Index The Graph database part of the new ATLAS Event Index is ready for the new ATLAS offline framework being build for the ATLAS Run 3, which was originally scheduled for 2021, but is delayed because of the Coronavirus pandemic. The Graph database performance is at least as good as the performance of the current system based on pure HBase database. As the full chain has not been delivered yet and all existing data have not been replicated in the new Phoenix database, the overall performance gain cannot be faithfully evaluated. Some functionality enhancements and more intuitive interface has been already acknowledged and are helping in integrating the system in the new ATLAS offline framework.
Graph Databases for HEP Significant HEP effort is spent making execution more structured and parallel by using parallel or functional programming. Less effort has been spent, so far, structuring the data which can lead to simpler and faster access. Graph Databases offers many advantages: • They deliver more transparent code by simplifying data access layer. • Stable data structure is handled in the storage layer. • They are suitable for Functional Style and Parallelism. • They are suitable for Deep Learning. • They are suitable for Declarative Analyses. • They can help with Analysis Preservation as the stored data carry enough information for their interpretaion. • They are language and Framework neutral. There are two possible ways of how to proceed in using Graph databases in new HEP software.
, 2016; Kolody et al. , 2019) , HPLC and microscopy  (Paerl et al. , 2011; Chavez et al. , 2017). However, less is known about the general molecular diversity of eukaryotic algae in the different trophic regimes of the ENP. Here, we examined eukaryotic phytoplankton community structure in the Sargasso Sea over seasonal cycles and compared this data to algal distributions along the ENP Line-67. V1-V2 16S rRNA gene amplicon data from BATS photiczone profiles  (Treusch et al. , 2009)  were analyzed using phylogenetic approaches  (Matsen et al. , 2010; Choi et al. , 2017). At BATS, we found a dominance of non-diatom stramenopile-derived amplicons in the multi-year dataset. This prompted us to develop well-curated, full-length 16S reference alignments and phylogenetic reconstructions for the most highly represented stramenopile groups, specifically the pelagophytes and dictyochophytes.
The other dictyochophyte with a complete pathway, D. speculum CCMP1381, is never numerically abundant in our samples. The four Florenciella transcriptomes in the MMETSP dataset contain only one or no genes from the thiamin pathway (Supplementary Table  S8 ). While absence in transcriptomes is not definitive, it is interesting to note that the environmental Florenciella-like clades are most abundant when nutrients are elevated (although vitamin concentrations are not known), such as in BATS surface waters during deep mixing, the DCM, and ENP mesotrophic waters (Figure  3B ).
, 2018). The Dictyochales order as a whole takes a basal position within the dictyochophyte phylogeny (Supplementary Figure  S5 ). Further, Florenciella which holds a less basal position, encodes the well characterized SIT gene, a Si transporter, also present in diatoms and several other marine eukaryotic lineages  (Marron et al. , 2016)  and is purportedly a mixotroph based on field SIP data  (Frias-Lopez et al. , 2009). Because the dominant clades observed at BATS and the ENP lack cultured representatives, it remains unclear whether the capacity to feed via phagocytosis extends across dictyochophyte diversity. However, as with diatoms  (Durkin et al. , 2016) , which were low in relative abundance at our study sites, dictyochophyte silicification has been associated with efficient carbon export  (Marron et al.
Additional phylogenetic reconstructions were performed in MrBayes v3. 2. 6  (Ronquist et al. , 2012)  with the parameters of lset nst = 6 rates = invgamma ncat = 6, and ngenval = 10,000,000 samplefreqval = 1,000 and tempval = 0. 200, and the final tree figure was produced with assistance from FigTree v1. 4. 3 and topology from RAxML (Supplementary Figure  S2 ). More refined specific reference alignments and phylogenetic trees were constructed for two stramenopile classes, pelagophytes and dictyochophytes (Figure  2B  and Supplementary Figure  S5 ) using the same approaches described above. Briefly, a total of 68 and 61 sequences were used for pelagophytes and dictyochophytes with 1,210 and 1,066 homologous positions, respectively. To improve the resolution of the trees, 16S rRNA gene reference sequences from the MMETSP datasets (10 for pelagophytes, 5 for dictyochophytes) were added.
Due to limited SCM/DCM data, averages and SDs at this depth were computed using monthly data with ≥ 3 samples that met amplicon cutoff criteria (months 0, DM; +4, +6 and +9 stratified). Fifteen ENP samples were analyzed using the same PhyloAssigner approach. Overall ENP sequencing depth was greater, with on average 11,300 ± 6,894 total and 3,366 ± 2,641 plastid-derived amplicons recovered per sample excluding one sample with 63,526 total and 26,672 plastid-derived amplicons.
qPCR Primer Design, Testing and Implementation To generate 18S rRNA gene insert-bearing plasmid standards for qPCR, the 18S rRNA gene was amplified from P. calceolata CCMP1756 using the primers 5 -ACCTGGTT GATCCTGCCAG-3 and 5 -  TGATCCTTCYGCAGGTTCAC-3 (Moon-Van Der Staay et al. , 2001). PCR cloning and plasmids purification was performed as in  (Demir-Hilton et al. , 2011). Clones were bidirectionally (Sanger) sequenced using Big Dye Terminator chemistry (Applied Biosystems) with plasmid primers M13F/M13R and the two internal primers 502f and 1174r  (Worden, 2006). To quantify P. calceolata abundance, a P. calceolata-specific TaqMan primer-probe set was developed (Supplementary Table  S6 ) after retrieving cultured pelagophyte and environmental 18S rRNA gene sequences from GenBank. Primers specific to P.
Stained samples were incubated for 10 to 20 min before being run. Unstained controls were run to discern positive signals from stains. 384-well plates were illuminated with UV for 2 min prior to performing the sorts and selected cells were then sorted using the Single-cell sorting mode from the BD FACS Sortware (software v1. 0. 0. 650), ensuring that only one cell would be sorted into each well. The drop delay and sort quality were controlled by sorting known numbers of fluorescent beads and counting on a microscope. Negative and positive controls on each plate involved leaving a subset of wells empty and having a column of wells receive 20 cells, respectively. Plates were covered with foil and immediately frozen at -80 • C.
For assembly of the uncultured dictyochophyte, paired and unpaired quality filtered reads from the two dictyochophyte-containing wells were initially assembled as individual single cell assemblies as well as a co-assembly of both samples using SPADES v 3. 11. 0 (Bankevich et al. , 2012)  as previously described  (Cuvelier et al. , 2010). Then, mapping of reads to the assembled contigs and identification of chloroplast genomes was performed in Anvi'o v3 as previously described  (Eren et al. , 2015) , and enabled by identification of chloroplast rRNA genes with metaxa2  (Bengtsson-Palme et al. , 2015). The chloroplast contigs from the two wells were determined to have > 99. 9% nt identity to one another via the ani. rb function within the enveomics package  (Rodriguez-R and Konstantinidis, 2016).
ACKNOWLEDGMENTS We thank the team at  BIOS ,  Craig Carlson  and  Rachel Parsons  for BATS sample access. We also thank the captain(s) and crew of the R/V  Western Flyer ,  Tim Pennington  and  Alyson Santoro  (who was the  Co-Chief Scientist of   CN13ID ). We are grateful to  Bank Beszteri  and  Stefan Neuhaus  for PhyloAssigner support.
In the control group, these factors will be examined as possible predictors for the development of depressive symptoms and other mental health problems. The second objective of the trial is to identify the predictors of mental health outcomes from digital behavioural and cognitive data collected using participants' smartphones (see Table  2  for details). Sensor data will be collected from smartphones to provide information about activity levels (accelerometery), movement through space (GPS) and voice patterns to generate a 'digital phenotype' for each participant  [33] [34] [35]. Digital phenotyping can be understood as using sensor data collected by personal devices to quantify the individual-level human phenotype. One of the functions of profiling individuals using digital phenotypes in this way is to make predictions about changes in mental health.
The sensors in smartphones provide a non-intrusive, minimally burdensome and scalable method to investigate the effect of activity and behaviour on mental health. Evidence of the validity of this approach is beginning to emerge, with location variability now showing an association with depression outcomes in small studies of adults  [36, 37]. However, the use of digital phenotyping approaches in young people at scale has not yet been undertaken. For participants in the intervention group, this information will be combined with intervention usage data and other study measures to explore predictors of response and non-response to the intervention apps. In the control group, this information will be used to prospectively examine predictors of psychopathology. The third objective of this trial is to evaluate the implementation of the proposed CBT-based depression prevention programme in the school environment.
The data that will be collected to meet this objective will combine intervention usage with qualitative and quantitative methods. A protocol for the process evaluation of the programme implementation will be detailed elsewhere. Three different forms of data will be collected from this trial to address each of the objectives: self-report questionnaires (first objective), smartphone-recorded sensor data and ecological momentary assessment data (second objective), and usage and implementation data (third objective). This study will also utilise health and education datasets as a fourth form of data that will be linked to the main study datasets 1 year after the trial commences, and then periodically until the trial is complete. Linked data will include academic outcomes, health service utilisation and infant development, alongside births and deaths records.
The follow-up period is the time since baseline. For the subset of participants involved in Stage II of the trial, an additional measurement occasion will take place 6 weeks after the Stage II intervention (Stage II post intervention). The trial will take place in three recruitment waves. It was planned that Wave 1 will commence in Term 3, 2019 (July 2019), Wave 2 will commence in Term 2, 2020 (April 2020) and Wave 3 will commence in Term 3, 2020 (July 2020). The Stage I intervention phase (SPARX) will last 6 weeks and have a follow-up period of 5 years. The Stage II intervention phase (Sleep Ninja) will last 6 weeks, occurs 12 months after Stage I (SPARX) and will have a followup period of up to 4 years. Data from each assessment point will be used to determine safety and whether any modifications to the trial protocol are required (see Analysis section for details).
Smartphone-collected data Additional data will be collected directly via smartphones through a study app developed for this study (the Future Proofing App), as these data cannot be collected from the self-report questionnaires outlined earlier. Three forms of data will be collected, which include actively collected data, passively collected data and app usage data. Actively collected data will assess selfreported mood ratings, voice samples and cognitive tasks. This form of data will overcome biases in retrospective reporting of mood and allow for an investigation of whether changes in voice are related to changes in mental health states. Cognitive tasks include two measures of executive function presented in affective and neutral contexts. Specifically, a measure of affective shifting  [82]  and the Affective Digit Span  [83]  and a typing speed task, which will allow for a mechanistic assessment of the way cognitive function affects psychological outcomes in adolescents.
Linked data Data relating to participants' academic outcomes, physical health, utilisation of health services, infant development, births and deaths will be linked from extant Australian government administrative datasets by an authorised agency of the New South Wales Ministry of Health, the Centre for Health Record Linkage (CheReL, www. cherel. org. au). Informed consent from each participant will be obtained prior to linkage and databases will be linked using personal identifiers such as names, dates of births, addresses and hospital identification numbers with probabilistic methods. Linked data will be provided to the researchers in a de-identified manner and will be used to determine related health and other outcomes for the participant, including birth and perinatal data, educational outcomes in standardised curriculum-based tests, and hospitalisation and mortality outcomes.
For Stage II, individual-level randomisation with a 1:1 allocation using a computergenerated randomisation schedule stratified by gender and depression severity scores will be employed. This procedure is automated through the Black Dog Institute research platform. Permuted block randomisation will be used at both stages but will not be disclosed to ensure concealment. Allocation to arms is not directly communicated to schools. However, because the study has no control intervention, schools, participants and study operational staff will be aware of school allocation to the intervention arm. With the exception of the trial data manager, other individuals not involved in the day-today running of the trial will remain blind to allocation. All outcome assessments are conducted electronically and not subject to assessor bias.
Data collection and management All research data collected in this trial will be stored using a unique participant ID code. A list of identifiable participant information associated with each ID code will be stored separately from the research data. The privacy, security and ownership of the research data will be maintained, and re-identifiable data will not be stored or accessible by another organisation. Access, storage and transmission logs will be recorded and regularly reviewed for anomalies. Annual audits will be conducted by the trial data manager to ensure compliance with data security processes outlined by UNSW and Black Dog Institute. The data that will be collected include questionnaire data, mobile phone data and linked data. Coded questionnaire outcome data will be stored securely on the Black Dog Institute online research platform until ready for export.
The research platform is stored on UNSW servers and supported by enterprise access controls and 256-bit encryption or higher. Data will be exported from the research platform into Microsoft Excel following assessments so that they can be checked by the data manager for data quality and accuracy. After checking, data will be exported into appropriate statistical software for analysis. Data collected by mobile phone apps will be encrypted and transmitted to a secure database hosted by Google Cloud Services in Australia. Data will be securely removed and transferred on a scheduled basis to UNSW servers by the trial data manager. Access to Google Cloud Services and the UNSW server will require authentication and will be restricted to the data manager and named members of the research team. The data manager will be responsible for extracting and securely transferring data to the research team.
Primary outcome Analysis of the primary outcome will be undertaken using an intention-to-treat approach including all participants randomised regardless of intervention received, controlling for baseline differences when appropriate. The primary analysis will be conducted using planned contrasts comparing a change in depression scores on the PHQ-A from baseline to 12 months between the trial arms (SPARX intervention vs. control), using a mixed-effects model repeated-measure analysis (MMRM). MMRM is preferred due to the ability of this approach to include participants with missing data without using discredited techniques such as last observation carried forward  [87, 88]. School will be included as a random effect to evaluate and accommodate clustering effects. Variables used in determining allocation balance will be evaluated and retained in analyses where they are significant or quasi-significant.
Additional analyses Subsidiary complier analyses will be undertaken to compare individuals who complete the intervention relative to those who do not, in both trial stages. Regression models will be used to examine risk factors for symptoms of psychopathology across the study measurement points in the control arm. Mediation analyses will be explored using structural equation modelling. Machine learning approaches will be used to link multiple data types with outcomes to discover novel factors which predict outcomes in terms of specific disorders and their progression as well as investigate individual patterns of data that predict individual mental state or symptom trajectory. Cost-effectiveness analyses will be conducted at the primary endpoint and the final measurement point. Data will be linked to existing records, and education and health outcomes will be reported.
Ethics and dissemination This study has ethical approval from the University of New South Wales Human Research Ethics Committee (HC180836) and NSW Government State Education Research Applications Process Approval (SERAP 2019201), and has applied for approval from Catholic dioceses and the Tasmanian Department of Education. The trial is subject to annual progress reviews with these ethics bodies. The trial is registered with the Australian New Zealand Clinical Trials Registry (ANZCTRN12619000855123). All protocol amendments will be subject to approval by relevant ethics committees and listed on the ANZCTR registry. All trial findings will be presented in aggregate format so that no individuallevel data will be presented. Public access to the full protocol can be granted from the authors on reasonable request.
Access to participant-level dataset will be subject to governance processes set up around a data repository which will contain data from this study. This will be open for researchers to apply for access. Trial findings will be communicated using lay language and will be made available to participating schools for publication in school newsletters and/or school websites. Participants and parents will also be provided with these findings via email. Regular trial progress updates will be provided by the research team to schools for distribution through the school community, at their discretion. These findings will also be provided to other stakeholders in the wider community, including to the government in policy documents, school counsellor bodies, teacher groups and mental health groups. All findings will be provided at aggregate level.
The inclusion of smartphone-collected objective data adds another dimension to the trial, allowing for machine learning approaches to explore whether activity and location data are associated with, or predictive of, changes in depression and other mental health symptoms. Moreover, linking this rich dataset to government education and health records will permit direct inferences to be made about the impact of mental illness on health and academic outcomes. Finally, the continuation of follow-up assessments for 5 years means that the long-term effects of the interventions on depression, anxiety, distress, insomnia and additional outcomes can be assessed. The results of The Future Proofing Study stand to provide a major contribution to the field of digital mental health prevention programmes, and knowledge about the populationlevel impact of preventing depression using these methods.
Acknowledgements The authors would like to acknowledge the many individuals who have been involved in the setting up of this study. Thank you to the schools, parents and young people who have provided their perspectives and guidance on the project. Thanks to members of the  Black Dog Institute Education and Marketing team including Cameron Banks ,  Hiroko Fujimoto ,  Sarah Connor ,  Zoe Jenkins ,  Vivienne Zeaiter ,  Dean Winder  and  Fiona Treweeke. Sincere thanks to the IT team including  Jacinto Santamaria ,  Chaithra Madappa ,  Praveena Cheni Cheri ,  Toby Reid ,  Sam Townsend ,  Cesar Anonuevo ,  Jennifer Biggin , the Deakin team and  Victoria Carr. Thanks also to the many people who contributed their ideas and expertise to this project including  Kathryn Woodcock ,  Dr Yael Perry ,  Dr Simon Baker  and  Dr Jane Hwang , and  Professor Jennie Hudson ,  Professor Sveta Venkatesh  and  Professor Maree Teesson.