dataesr/scientific-paragraphs-categorization huggingface dataset:
Lipid index variants with CAD, T2D and NAFLD datasets The GWAS meta-analysis results of CAD and T2D were acquired from MVP  63  and DIAGRAM Consortium  62  , respectively. For variant rs1229984, the CAD result is from CARDIoGRAMPlusC4D meta-analysis 99 , as it was not present in the MVP results. The NAFLD GWAS and meta-analysis was performed in the UK Biobank and Michigan Initiative (MGI). We determined the association of the lipid index variants with CAD, T2D, and NAFLD, and aligned the alleles across all the traits to the LDL lowering allele. We then highlighted the protective lipid coding alleles associated with CAD.
Acknowledgments GMP, PN and CW are supported by  NHLBI   R01HL127564. GMP and PN are supported by  R01HL142711. AG acknowledge support from the  Wellcome Trust  ( 201543/B/16/Z ),  European Union Seventh Framework Programme   FP7/2007-2013  under grant agreement no. HEALTH-F2-2013-601456  ( CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation 's Grant number  NNF15CC0018486. JMM is supported by  American Diabetes Association Innovative and Clinical Translational  Award  1-19-ICTS-068. SR was supported by the  Academy of Finland Center of Excellence in Complex Disease Genetics  (Grant No  312062 ), the  Finnish Foundation for Cardiovascular Research , the  Sigrid Juselius Foundation  and  University of Helsinki HiLIFE Fellow and Grand Challenge  grants.
EW was supported by the  Finnish innovation fund Sitra (EW)  and  Finska Läkaresällskapet. CNS was supported by  American Heart Association Postdoctoral Fellowships   15POST24470131  and  17POST33650016. Charles N Rotimi  is supported by  Z01HG200362. Zhe Wang ,  Michael Presuss , and  Rith JF Loos  are supported by  R01HL142302. NJT is a Wellcome Trust Investigator ( 202802/Z/16/Z ), is the  PI of the Avon Longitudinal Study of Parents and Children  ( MRC & WT 217065/Z/19/Z ), is supported by the  University of Bristol NIHR Biomedical Research Centre  ( BRC-1215-2001 ), the  MRC Integrative Epidemiology Unit  ( MC_UU_00011 ) and works within the  CRUK Integrative Cancer Epidemiology Programme  ( C18281/A19169 ). Ruth E Mitchell is a member of the  MRC Integrative Epidemiology Unit at the University of Bristol  funded by the  MRC  ( MC_UU_00011/1 ).
05). Then PoPS, computes a joint enrichment of all selected features simultaneously in a leave one chromosome out (LOCO) framework. The gene features employed by PoPS are listed here: https://github. com/FinucaneLab/gene_features. Finally, PoPS computes polygenic priority scores for each gene by fitting a joint model for the enrichment of all selected features. The PoPS score for a gene is independent of the GWAS data on the chromosome where the gene is located. The PoPS analysis returned scores for a total of 18,383 genes per lipid trait. We only kept the top 20% genes among all 18,383 genes. We then annotated our index variants with the nearest S2). The RSS-NET software and related network files are available at https://github. com/suwonglab/rss-net.
Drug targets mining analysis To gain therapeutic insights from our gene prioritization results, we performed a lookup in Therapeutic Target Database (TTD) 2020  43  (http://db. idrblab. net/ttd/). Specifically, we crossreferenced 5,710 unique lipid-associated genes prioritized by our combined algorithm (Table  S2 ) with 1,563 genes corresponding to at least one drug (either under development or approved) with known clinical indication in TTD 2020. As a quality control for this lookup, we excluded all TTD entries related to drugs that were discontinued, terminated, or withdrawn from the market. The full lookup results are available in Table  S6.
Performance of the Ter-qPCR Against Clinical Samples As a final important evaluation step of this work, the Ter-qPCR was applied to 78 individuals belonging to three categories (early LD, late LD patients and healthy volunteers) who were diagnosed by Dr. LT  (Cameron et al. , 2014). We intended to establish the feasibility of the Ter-qPCR to detect LD. Copy numbers were determined from both blood and serum samples with three technical repeats for each sample type. The technical repeats that did not show detectable copy numbers were scored 'zero'. The raw data was presented in the Supplementary Table. Visually, there are many more 'zero' scores from serum than blood samples, and the mean and median values obtained from blood samples in each category are much higher than those from sera (Supplementary Table ).
Phylogenetic Analysis Phylogenetic analysis was constructed using the program Molecular Evolutionary Genetics Analysis (MEGA) 7 according to the previous established method  (Shan et al. , 2012 (Shan et al. , , 2014)). Neighbor-Joining (NJ) with a maximum composite likelihood model and Maximum Likelihood (ML) based on the Tamura-Nei model analyses were conducted on a nucleotide data set of terL genes. Support for clades were estimated using a bootstrap analysis implemented in MEGA using 3, 000 replicates. The trees were rooted with phage Lambda (NC_001416) as an outgroup.
The Ter-qPCR Primers and probe were designed using the PrimerQuest R Tool based on an alignment of phage terL genes from 13 cp32 plasmids (cp32-1 to cp32-13) carried by B. burgdorferi s. l strains. The resulting Ter-qPCR amplified a 147 bp target region  (Shan et al. , 2018). The fluorogenic probe was labeled with 6-carboxyfluorescein (FAM) fluorescent reporter dye at the 5 -end, an internal ZEN TM Quencher and an Iowa Black Fluorescent Quencher (IBFQ) to the 3 (5 FAM/ZEN/3 IBFQ). To rule out PCR inhibition and avoid false negatives, the Ter-qPCR was duplexed with an internal amplification control (IAC) qPCR that generated a 145 bp PCR product  (Deer et al. , 2010). The IAC DNA (accession number FJ357008. 1) was synthesized by IDT and added to each Ter-qPCR template. The IAC probe was fluorescently labeled with the fluorescent dye of JOE at the 5 -end.
Construction of the Standard DNA For absolute quantification, a plasmid carrying the phage terL gene fragment (named as Ter-plasmid) was constructed and used as the standard curve. Specifically, a pair of PCR primers was designed using Primer Blast to amplify a 721 bp region of the phage terL gene (GenBank accession NC_000948), embracing the 147-bp Ter-qPCR product region. The primers were FTer721:AGACTAAGATGCGGGCAAGA and RTer721:TTGCATCAAGAGCGTCATCA. PCRs were carried out in a LabCycler (SensoQuest GmbH) in a total volume of 50 µl, containing 0. 25 mM dNTPs, 3 mM MgCl 2 , 3 µM primers, 50 ng template DNA, 0. 5 unit of Taq polymerase (Bioline), and 5 µl 10 × Taq buffer (Bioline). Amplification conditions were: 94 • C for 2 min, 30 cycles of 94 • C for 30 s, 50 • C for 30 s, 72 • C for 1 min, with a final extension of 10 min at 72 • C.
The analytical sensitivity of the Ter-qPCR was firstly evaluated with five tenfold dilutions of the Ter-plasmid (10 6 to 10 2 ) and B31 DNA (40 ng to 4 pg), respectively. Each dilution was tested with three replicates to determine the PCR linearity and amplification efficiency. The LoD was then estimated by testing Ter-plasmid dilutions from 1000 to  100, 80, 60, 40, 20, 10, 5 , and 1 copies/PCR. Ten replicates were used for each dilution. To mimic the real situation of analyzing DNA extracted from human samples, all Ter-plasmid serial dilution experiments were conducted with the presence of background human DNA 125 ng per PCR. Probit analysis via the SPSS software was performed to calculate the LoD with 95% probability  (Forootan et al. , 2017).
For everyone involved, two tubes of serum and two tubes of Ethylenediaminetetraacetic acid (EDTA)-treated whole blood (approximately one ml in each tube) were provided. Samples were provided in a coded, de-identified manner to preserve patient anonymity. Informed consent was obtained from all participants. The modified phenol chloroform DNA extraction method was applied to all the samples in a duplicate manner. Therefore, there were four DNA samples generated from one individual, two from the whole blood and two from the serum. Triplicate qPCR was applied to each DNA sample, which led to 12 Ter-qPCR data (six from whole blood, six from serum) expressed in copy numbers according to the standard curve for any one individual.
Statistical Analysis Graphpad Prism 8. 4. 3. was used for statistical analysis. Descriptive statistics and the D'Agostino-Pearson normality test were used to assess the data distribution. Mann-Whitney U tests were used to determine the significance of the difference between early, late LD patients and healthy volunteers. Furthermore, Probit analysis was carried out via the SPSS software suite (IBM SPSS Statistics 25) to estimate the LoD. The differences were not considered to be significant when the p-values were greater than 0. 05. Linear regressions were used to establish correlations between the serial dilution of Ter-plasmids/B31 DNAs and Cq values.
ACKNOWLEDGMENTS We would like to thank  Dr. Andrew Millard  and  Stacy Guiock  for their help in proofreading the manuscript. We gratefully acknowledge the main funding received toward the study from the  Phelix Research and Development  ( Phelix R&D , 37  Langton Street ,  SW10 0JL London ,  United Kingdom , the Charity Number  1156666 ), the 'Gift' funding from  Lymefonds ,  Netherlands  (ANBI-number:  858578438 ), and the  University of Leicester Drug Discovery and Diagnostics  ( LD3 ) spring fund 2018.
7A ). The relation between Vt and V0 follows the theoretical expectation for threshold adaptation due to sodium channel inactivation  (Platkiewicz & Brette, 2011; Fontaine et al. , 2014) , where the threshold starts increasing above the baseline Vmin when V0 exceeds the half-inactivation voltage Vi of sodium channels, with a slope  $ / " ≈ 1 in the depolarized range (where ki and ka are the inactivation and activation slope factors, respectively). By fitting the theoretical relation, we find  $ ≈ -55. 8 ± 3. 1 mV (Fig. 7B ),  $ - #$% ≈ -0. 7 ± 2. 9 mV (Fig. 7C ),  " ≈ 4. 1 ± 2. 2 mV (Fig. 7D ), and  $ / " ≈ 0. 9 ± 0. 18 (Fig. 7E ). These values are consistent with expectations if threshold adaptation is due to sodium channel inactivation. We then measured the axial current at spike initiation (just above threshold) as a function of V0 (note that there are fewer data points because current recordings were discarded when Rs changed by more than 30%).
5 Na2-GTP, pH 7. 25 with KOH (275 mOsm) and 1-2 mg/mL biocytin (B4261, Sigma-Aldrich). Open tip resistance was 2. 5-4 MΩ. Reported potentials were corrected for a liquid junction potential of -11 mV. Whole-cell recordings were made with a Multiclamp 700B amplifier (Axon Instruments), filtered at 10 kHz and digitized at 50 kHz using a DigiData 1440A (Axon Instruments) and Clampex 10. 7 running on Windows 10. High-resistance patch seals (>1 GΩ) were obtained before breaking into the cell. Recordings with a series resistance Rs above 25 MΩ, or with a residual Rs (after compensation) above 5 MΩ, were discarded. The resting membrane potential of the cell was recorded in the first minute after breaking in. Passive cell properties were recorded by stepping from -70 to -80 mV in voltage-clamp mode without whole-cell compensation.
2-7. 1 pF) were used. Hyperpolarizing current pulses were injected to measure the cell's capacitance (see Electrophysiological data analysis). Next, for n = 16 cells, 5-20 minutes of spontaneous activity were recorded to analyze spontaneous APs. At the end of the experiment, the pipette was retracted to obtain an outside-out patch. Outside the retina the tip was cleaned with brief, positive pressure to remove the remaining membrane patch and the potential offset was noted to check for any drift in the reference potential. The retina was rinsed in 0. 12 M PBS, fixed for 15 minutes in 4% paraformaldehyde in 0. 12 M phosphate buffer, rinsed again 2 times with 0. 12 M PBS and kept in 0. 12 M PBS at 4 degrees Celsius for immunolabeling procedure at a later stage.
Morphological data analysis Axon tracing in 3D was performed automatically with Vaa3D  (Peng et al. , 2010)  (version 3. 2 64-bit ran on MacOSX10. 13. 6) based on biocytin Z-stacks. The start and end position of the axon tracing was manually chosen by the researcher to include the entire AIS. Axon coordinates (x, y, z, radius) were stored in SWC files and analyzed with custom Python scripts. Coordinates were interpolated with a finer spacing corresponding to the pixel size (including in the z direction also). Interpolation was performed with B-splines using Scipy  (Virtanen et al. , 2020)  to evaluate the spline at each pixel comprised in the axon profile, and coordinates were rounded at the nearest pixel. The ankyrin-G images were then loaded as a 3D stack to get the fluorescence intensity at the interpolated coordinates along the axon profile.
Funding information This work was supported by a grant from  Agence Nationale de la Recherche  ( ANR-14-CE13-0003 ) to R. B. , by a grant from  UNADEV  ( 17UU1166-00 ) to X. N. , and by the  Programme Investissements d'Avenir IHU FOReSIGHT  ( ANR-18-IAHU-01 ). S. G. is supported by the  Ecole des Neurosciences de Paris.
How can researchers identify suitable research data repositories for the deposit of their research data? Which repository matches best the technical and legal requirements of a specific research project? For this end and with a humanities perspective the Data Deposit Recommendation Service (DDRS) has been developed as a prototype. It not only serves as a functional service for selecting humanities research data repositories but it is particularly a technical demonstrator illustrating the potential of re-using an already existing infrastructure -in this case re3data -and the feasibility to set up this kind of service for other research disciplines. The documentation and the code of this project can be found in the DARIAH GitHub repository:.
Introduction The increasing production, dissemination and re-use of humanities research data leads to a growing demand for easy-to-use discovery services for the identification of deposit services and research data repositories. The establishment of research data centres and journals 1 are shaping this trend. Although there are already some meta services 2 making research data repositories visible and searchable, a humanities specific service is still missing. To address this gap, the Data Deposit Recommendation Service 3 (DDRS) has been conceptualised within Humanities at Scale (HaS), a DARIAH 4 -affiliated project. The DDRS offers a simple humanities-specific faceted search for research data repositories applying the already existing, reliable and discipline-spanning database of re3data.
Related to this are online data platforms that do not store data, but bring together metadata of research datasets, making them findable for data users. • Galleries, libraries, archives and museums (GLAMs) are important holders of data in the humanities. Their main concerns lie in preservation of their collec-   the development -forms an important space for dissemination, feedback and stimuli for the improvement of an infrastructure or service. • Research funding agencies benefit from promoting the optimal use and reuse of data in which funds were invested. They can do this by encouraging good data practices, investing in data infrastructure and raising data awareness. Funding agencies, both at the European and national level, increasingly demand that funded research data (and publications) is being published as open access.
This could be implemented similarly to the re3data-interface but with fewer categories. • (D) Register a research data repository: the user -a repository managerwants to register a service for the DDRS. This is conveniently possible directly on re3data via the DDRS. This use case is aimed at extending the visibility of research data repositories and/or enhancing the database quality and quantity of re3data. The DDRS could be leveraged for repositories to improve their dissemination and interoperability. A suggest page exists on the DDRS in order to be able to register new repositories on re3data which then become automatically available to the DDRS. Furthermore, the DDRS system has six management use cases (refer also to Fig. 7 ): • (F) Language localisation of interface: the service has to be designed in a way that future localisations can be incorporated as easily as possible.
This requirement is important for the usability of the services. • (G) Addition of information about a repository that is not available in external sources: in the current design state the service relies on the re3data-database. As this database does not focus on the arts and humanities there is a risk that repositories relevant for the user may not be included. The gap of these "missing repositories" can be addressed at least in two ways: indexing them in the re3data-database or adding the information on the side of the DDRS. Although the latter way seems more challenging, it opens the way for including other information than those included in re3data. 18 As a reminder, the re3data-database relies upon a selected set of properties summarised in the re3data-metadata schema v. 2. 2. 19 This schema covers all research domains and is not arts and humanities-specific.
This is not only important to improve the quality internally but it becomes crucial with regard to two aspects: firstly it becomes possible to use the usage statistics as an enrichment for the identification process, i. e. to rank services based on their popularity; secondly the usage statistics can be used to raise the attractiveness of the service towards repositories that so far have not been included both in our DDRS or in re3data. During the demonstrator phase of the DDRS only rudimentary usage statistics are collected via Piwik/Matomo. 21 • (J) Changes to questions and question structure: the design of the service has to reflect a flexibility to change the set of questions in the future. This can become necessary as soon as the underlying database used for the DDRS changes, e. g. gets more granular in certain areas, or as the users' perceptions of research data changes, e.
Overall approach This section describes the technical implementation of the DDRS 22 within the Humanities at Scale project. It is important to distinguish between an ideal concept of the service and the actual implementation during the project. The latter one has to consider the availability of resources and time as well as the institutional context. As a reminder: the DDRS assists the user in identifying suitable research data repositories for the individual case depending on only a few criteria, like formats of the research dataset, language or affiliation or certain indispensable functions. 23 he result of this step is a ranked list of repositories which can be used by the user as it is. The questions leading to the result list are not mandatory but the result gains quality by answering more questions.
After displaying the result list, the user can decide to proceed to the second functionality layer of the DDRS, which is about the structured description of the specific research dataset. The aim of this step is to gain, as easily and conveniently as possible, a structured and coherent data description which serves as basis for initiating the ingest process with the repository. At this stage, the DDRS serves only as communication handler on behalf of the user, pointing his or her ingest request to the appropriate contact person. Figure  8  provides an overview of the simple infrastructure which has been set up within the project. The result is a functional demonstrator, flexible to be developed further upon or to be enhanced with additional functionalities. This result serves as proofof-concept for the idea and will highlight the community's demand for such a service.
As basic infrastructure for this stage of the DDRS a virtual machine (VM), accessible via the internet is sufficient. The VM consists of all necessary applications and will initially be accessible over an IP address. 24 t was decided that the branding of the service would be quite close to DARI-AH's, obviously including the logo of the project in which the DDRS was created: Humanities at Scale and the logo of the underlying service which provides the data: re3data. The URL was also branded as DARIAH: https://ddrs-dev. dariah. eu/ also keeping in mind that the service is in a demonstration phase. 22 The complete documentation and according code of the DDRS are available at GitHub: https://dariaheric. github. io/ddrs/ 23 These additional criteria don't have to be indicated by the user but are shown in the detailed metadata result for the repositories.
• A simple questionnaire suggests to the user a ranked list of suitable research data repositories for the specific use case. The questionnaire is designed in such a way that adjustments to the questions are possible in an easy way via the administration section. This is necessary as the database used for the requests -initially re3data -will likely change over time. For example, new research funder mandates could be reflected in the metadata and the DDRS had to consider this. • A web form describes the specific research dataset in a structured way (this can be implemented in a similar way as the questionnaire). The questionnaire is also designed in a flexible way to allow further adjustments to the research data criteria that are to be described by the user. This will likely be the case as the research data practices in the humanities develop and new standards emerge.
The current implementation is GDPR compliant as the user data gets submitted only to the selected repository contacts. The submitted user specific data is after sending not available to the DDRS. • Currently 25  the DDRS sends queries directly from the server to the ElasticSearch of re3data. A request API conducts the requests to identify the repositories. The API sends -either filter by filter or all in one -(a) request(s) to the re3data database, displaying in the end a list of repositories fulfilling the respective criteria. On the basis of early tests of the re3data API the data quality and performance seem to be sufficient for our purpose and do not seem to impact on the re3data API's general performance. • A database is used to enrich the request results from re3data with contact details.
This enrichment is necessary as the DDRS not only wants to suggest suitable repositories but also points the user to a specific point of contact to facilitate the ingest of the individual research data. Therefore, someone with expertise in humanities research data is necessary but this information is not available through the re3data database as this is a nondisciplinary service. • A forwarding component, basically a mail server. This component mails the completed data description form to the relevant repositories. • A usage statistics component, currently Matomo. At this point it is not clear what kind of data could be collected by this service in the future. If the DDRS has a considerable user uptake in the future the usage statistics could become a valuable asset to be used for further added value services and to demonstrate the value of the service.
Information retrieval Regarding the quality of the search results one has to consider, first of all, the limitations of the current approach which relies heavily on re3data's database. Initially the design of the DDRS relied on an include-exclude table which meant that the DDRS could select the search results only by applying the filters which are given by the re3data metadata schema v2. 2 and its 39 main properties and related sub properties. 26 The DDRS now includes an additional database containing information on the points of contact for forwarding the ingest request. The re3data schema contains only information on technical points of contact for the repositories but not for research data managers or information specialists. This additional database relies on re3data's external persistent identifiers in order to keep the information always bound to the same repository; contact information can only be connected to a single repository within re3data.