dataesr/scientific-paragraphs-categorization huggingface dataset:
05, Log2FC > 1 and LogFC < -1 were set as thresholds. We then performed pathway analysis using the core analysis function of Ingenuity Pathway analysis (IPA) (Qiagen) and the Gene Set Enrichment Analysis (GSEA) was done using GSEA software version 4. 3. 2 (GSEA; http://software. broadinstitute. org/gsea/ (accessed on 3 October 2022)). All GSEA data showed had a p-value < 0. 05. For Pamgene analysis, image acquisition and data analysis were performed according to the manufacturer's instructions as previously described  [16]. Data and upstream kinase analysis were conducted using the Bionavigator software v. 6. 3. 67. 0 developed by PamGene. Peptides and kinases with an adjusted p-value < 0. 05, LogFC > 1 and LogFC < -1 were set as thresholds.
Supplementary Materials: The following supporting information is available online at www. mdpi. com/xxx/s1, Figure  S1. Comparative analysis of mRNA expression levels (i. e. , Transcripts Per Million (TPM)) between D0 spheroids and D10 adipospheres in 3D hiPSC-BAPs. Figure  S2. Upstream regulator analysis of differentially regulated pathways in 10 days-differentiated 3D adipospheres (D10 vs. D0). Figure  S3. Gene set enrichment pathway analysis (GSEA)-enrichment plots of representative gene sets from differentially regulated gene expression in 10 days-differentiated 3D adipospheres (D10 vs. D0). Figure  S4. Comparative analysis of mRNA expression levels (i. e. , Transcripts Per Million (TPM)) between control and CDKN2A-deficient D0 spheroids in 3D hiPSC-BAPs. Figure  S5. Upstream regulators analysis of differentially regulated pathways between control and CDKN2A-deficient D0 spheroids.
Crystal structure of LmNADK1 in complex with a new linear diadenosine compound As an amide moiety at 5 ′ -end of NKI1 has been recently shown to fit quite well into the active site of LmNADK1 (PDB ID 6Z65)  [22] , we wondered if a sulfonamide group could accommodate as well. Such a NKI1 derivative (hereafter compound 1, Fig. 3A ) was synthesized and successfully observed bound to LmNADK1 by X-ray crystallography. The structural analysis of LmNADK1 in complex with 1 highlighted key interactions and a special orientation of its amino-alkyl side chain in the NAD binding site of LmNADK1. In particular, the X-ray structure reveals a distance of 3. 4 Å between the amino group at the 5 ′ end and the oxygen atom of the propargyl linker of compound 1 (Fig. 3B ). We hypothesized that joining these two positions (by an appropriate linker) would restrict the conformational flexibility of the molecule and provide more intra-and inter-molecular interactions.
Structure-based design of novel cyclic diadenosines From the crystal structures of LmNADK1 with various nucleosidic compounds and especially the lead compound NKI1, we deduced possible substitutions at the 5 ′ end of the free ribose (i. e. , not yet linked to the propargyl moiety). The best possible cyclization paths from that 5' position to the polar head of the propargyl were first deduced from the position occupied by substitutions of the same ribose in various monoadenosines previously screened by X-ray crystallography  [15, 20, 22]. Beside the sulfonamide group as in compound 1 (PDB ID 8A9V), an amide group will also fit well according to a recent complex (PDB ID 6Z65)  [22]. From these two structures, a urea group was also envisioned to bring a stabilizing internal hydrogen bond. On the propargyl side, the main constraints learned from previous structures come from the knowledge of the orientation of the methyl group on an amino propargyl linker  [15]  and that of a small linear alkyl chain (PDB ID 6Z64)  [22].
X-ray structures of LmNADK1 bound to cyclic compounds The soaking procedure described previously  [15]  was used to get crystals of the target bound to the synthesized macrocycles MC2-6. For MC1, direct soaking failed and instead co-crystallization at alkaline pH was attempted and showed partial occupancy of the macrocycle into the active site. Full occupancy was achieved by soaking more compound prior to mounting and freezing the corresponding crystals. The X-ray structures were solved by molecular replacement using PDB ID 6RGC  [15]  as a starting template and refined to 2. 2-2. 4 Å resolution using the software Phenix  [42]. Manual rebuilding in the electron densities were performed in COOT  [40]. A few rounds of optimization rapidly led to convincing refinements for all the structures.
Buffer, NAD and inhibitor solutions (total volume 25 μL, concentration 4 × ) were dispensed into the microplate using an OT-2 lab robot (Opentrons). 25 μL of a mixture containing G6PDH, MgATP and glucose-6-phosphate at a 4 × concentration were dispensed using a manual repeating pipette. Finally, the reaction was started at time 0 by adding and mixing 50 μL of LmNADK1 at a 2 × concentration using a multichannel pipette. OD 340 were measured every 32 s during 32 min. The values of the inhibition constants were determined by global fittings of the steady state rate constants, k ss , at different concentrations of NAD and inhibitor, with a competitive inhibition equation using GraFit software (version 7. 0. 3, Erithacus Software Ltd). The inhibition assays were carried out in independent triplicates.
Accession codes The crystal structures of LmNADK1 bound to the macrocycles MC1-6 are deposited under accession codes PDB: 8B47, 7ZZH, 7ZZJ, 7ZZF, 7ZZG and 7ZZE, respectively. The complexes of LmNADK1 with the acyclic precursors are available in the PDB under the accession codes: 7ZZ7, 7ZZ9, 7ZZA, 7ZZB, 7ZZC, 7ZZD and 8A9V.
Acknowledgments This work was funded by the  Agence Nationale de la Recherche  ( NADKiller ,  ANR-17-CE18-0011-02 ) and supported by  Institut Pasteur , the  Centre National de la Recherche Scientifique (CNRS) ,  Institut National de la Santé et de la Recherche Médicale (INSERM)  and  University of Montpellier. C. L. and D. A. C. acknowledge  ANR  for financial support. This work was also supported by the  French Infrastructure for Integrated Structural Biology (FRISBI ,  ANR-10-INSB-05-01 ). We acknowledge the experimental assistance from the staff of the  European Synchrotron Radiation Facility (ESRF, Grenoble, France , beamlines  ID23-2 ,  ID30-A1 ,  ID30-A3 ) and the synchrotron  ALBA (Barcelona, Spain , beamline BL13-XALOC) during data collection. The authors would like to thank  Frédéric Bonhomme  (CNRS,  UMR3523 ,  Institut Pasteur)  for assisting with HRMS analysis.
Data analysis Transcripts were pseudonymised and NVivo 12 was used to support analysis. An exploratory approach was used in the analysis to avoid imposing a pre-existing framework on the data. As the interviews with EU participants took place first, the core team read the interviews and immersed themselves in the data. Thematic analysis was used to analyse the transcripts  (18). They were read line-by-line by author MEH and detailed codes were first created inductively for all EU transcripts and grouped to create categories and sub-categories. These categories and sub-categories were then discussed within the core team (MEH, SA, ST-C) and amended accordingly creating an initial data-driven framework based on the EU transcripts. Interviews of Belgian participants then took place and were coded deductively into the initial framework.
Comparison with existing literature To date, there are limited qualitative studies that explore stakeholders' views and experiences on implementation of POCTs for CA-ARTI. Qualitative studies conducted in the UK involved stakeholders with similar roles to our study but took place prior to the pandemic  (7, 13)  while another focussed on pediatric ambulatory care  (14). These studies, however, reported similar findings to our UK data and extended to our EU and Belgian data  (7, 13). Policy-level facilitators included robust reimbursement policies and incentivizing the use of POCTs in primary care  (23, 24). In line with our study, one study in the UK posited that a viable strategy to facilitate uptake of POCTs would be to procure them on a national-level and supply primary care practices with them  (7).
Funding This work is being funded by the  Innovative Medicines Initiative 2 Joint Undertaking (JU)  under grant agreement No. 820755. This JU receives support from the  European Union 's  Horizon 2020 research and innovation programme  and  EFPIA  and  Bio-Rad laboratories ,  BD Switzerland Sàrl ,  Accelerate Diagnostics  S. L. and The  Wellcome Trust Limited. ST-C was supported by the  National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance  ( NIHR200915 ) at the  University of Oxford  in partnership with the  UK Health Security Agency (UKHSA).
Patients and methods From November 1, 2015, to December 31, 2019, 42 patients with ESHF were consecutively treated using HM III LVAD in the Lausanne University Hospital cardiac surgery department and included in a prospectively maintained database. All preoperative data, including the primary cause of heart failure, patients' demographic data (age, BMI), and comorbidities (such as diabetes, renal failure, metabolic syndrome, and malnutrition), were collected from patients' ICU and anesthesia records. Operative protocols, hospital letters, and outpatient reports were used to evaluate the hospital stay and the follow-up period. We recorded major cardiac-related complications after LVAD implantation, including pump failure or thrombosis, bleeding, stroke, infection, cardiac failure, or dysrhythmia.
Cognitive aspects relating to the candidate Participants expressed that a fundamental element that underpinned candidates' reasoning approach was their level of background knowledge and areas of potential knowledge deficit. Comments about candidates' knowledge ranged from their understanding of pathology, epidemiology and symptomatology for particular conditions through to how this knowledge was then not only extended to, but also compared and contrasted with, Fig. 2  OD Domains and corresponding descriptors. For each domain, examiners scored the candidate's performance against the following scale understanding of associated conditions. Having a sufficiently robust baseline level of knowledge was seen to be a core pre-requisite upon which to apply reasoning skills. The second cognitive aspect related to how candidates internally analysed and then reported the data they obtained through the preceding history station.
01 • and 5 s. The X-ray diffraction spectra were recorded with a PANalytical High-Score Plus software. Crystalline phases were identified by comparison with standard reference patterns (Powder diffraction file PDF-2, International Centre for Diffraction data (ICDD)). In a second time, the oxidation at fixed temperature (around 1570 K, for which the mass loss of the samples is quite important as shown in Fig. 2 ) for different holding times from 50 to 480 s was carried out at a total pressure of 2000 Pa in flowing air plasma conditions. The evolution of the mass variation per unit area as a function of the holding time of oxidation allows obtaining a kinetic law. Fig. 5  reports these data and a kinetic law can be obtained, the mass loss is linearly proportional to the holding time at around 1570 K:.
Acknowledgements The authors thank the  European Space Agency (ESA)  for the partial funding of this study through the contract with  RTech  ( CNRS  n •  CT239424 ) and the programme " Investissements d'Avenir" (Investment for the Future)  of the  Agence Nationale de la Recherche (National Agency for Research) of the French State  under awards numbers  ANR-10-EQPX-49-SOCRATE.
Typically, these are rarely documented in the literature and this can limit the reproducibility of research findings  (Hutton et al. , 2016). In line with Principle 3 for Open Hydrology advocated for by  Hall et al. (2022) , the tool must be open access. The tool must also be polyglot, which means that it must be usable in a variety of open source programming languages, in order to be usable by as many users as possible. In practice, this means that a separate package must be available for each programming language, thus forming a software stack. 3 Design principles 3. 1 A compiled core with thin satellite bindings The software stack, named evalhyd, features a core library written in C++, named evalhyd-cpp, which implements all the available functionalities and evaluation metrics. The implementation of the core is written in a compiled language to be computationally efficient.
This is important since large-sample studies (see e. g. Gupta et al. , 2014)  and large hydrometeorological ensembles (see e. g. Schaake et al. , 2007)  have become very common in hydrological model evaluation studies. The core library is also directly usable in C++ as an evaluation library. In addition, the stack features satellite bindings which are distinct packages whose only purposes are to interface the core with high-level programming languages. These are named evalhyd-python for Python and evalhyd-r for R. They are intended to be as thin as wrappers can be to avoid duplicated efforts across bindings. A command line interface, named evalhyd-cli, also exists for those not using any of the programming languages mentioned. The only drawback with the latter is that it is only able to work via intermediate data files, which presents the same limitations as EVS of relying on a specific data file format.
On the contrary, the Python and R bindings work directly with native data structures of those languages, thus completely eliminating the need to decide on a specific file format, and allowing for any model output to be processed without requiring for them to be reformatted. Note that both the core library and the satellite bindings use the libraries from the xtensor software stack underneath (https://github. com/xtensor-stack, last access: 12 May 2023) and, in particular, they leverage the bindings available for multiple programming languages (i. e. xtensor-python and xtensor-r). As such, there is scope for the development of a satellite binding for Julia in the future (i. e. using xtensor-julia). For the remainder of this article, we will keep referring to the software stack as the one tool for convenience, since all the functionalities and evaluation metrics are available in all packages.
A two-entry-point interface Across the software stack, the interfaces are intentionally kept as identical as possible given the syntactic differences across languages (as illustrated in Figure  1  and Figure  A1 ). Thus, users of different evalhyd packages can easily help each other, and this also minimises inconveniences when users transition from one language to another. -rbind(c(4. 7, 4. 3, 5. 5, 2. 7, 4. 1)) prd !<-rbind  (c(5. 3, 4. 2, 5. 7, 2. 3, 3. 1), c(4. 3, 4. 2, 4. 7, 4. 3, 3. 3), c(5. 3, 5. 2, 5. 7, 2. 3, 3. 9 )) obs !< 1 2 3 4 (c) R interface. /evalhyd evald ". /obs. csv" ". /prd. csv" "NSE" 1. /prd. csv. 3,4. 2,5. 7,2. 3,3. 1 4. 3,4. 2,4. 7,4. 3,3. 3 5. 3,5. 2,5. 7,2. 3,3. 9   [4. 7, 4. 3, 5. 5, 2. 7, 4. 1]]) prd = numpy. array  ([[5. 3, 4. 2, 5. 7, 2. 3, 3. 1], [4. 3, 4. 2, 4.
For example, even if the problem only features one site and one lead time, the input must remain four-dimensional. For deterministic evaluation, streamflow predictions are at the minimum one-dimensional, that is {time}, corresponding to a simulation for a given site. However, it seems opportune to accommodate for multiple simulation time series, as is the case in Monte Carlo simulations or multi-model approaches for example. This is why the predictions are expected to be two- dimensional, that is {series, time}, and the observations to be two-dimensional, that is {1, time}. Note that for convenience, the Python and R bindings allow for one-dimensional inputs, which can be useful when used in a model parameter estimation context. 4 Key functionalities.
In a hydro-meteorological context, streamflow forecasts are often produced as deterministic forecasts or as ensemble forecasts to estimate the predictive probability distributions. However, they can also be issued as continuous predictive probability distributions. evalhyd only offers a solution for the first two situations, 250 and the third situation is not currently supported. As part of the design process, there was a focus on computational efficiency. This lead to the decision to rely on a compiled language and to resort to memoisation. Arguably, the former complicates the development effort compared with interpreted languages. In addition, the latter complicates the algorithms because the metric computations need to be decomposed, and the temporal reduction needs to be delayed for the masking functionality.
Conclusions and perspectives In this article, a new evaluation tool for streamflow predictions, named evalhyd, was presented. A first version of this tool gives hydrologists access to a large variety of evaluation metrics, commonly used to analyse streamflow predictions. It also offers convenient and hydrologically-relevant functionalities such as sample stratification or metric uncertainty estimation. The tool is readily available to a diversity of users, as it is distributed as a C++ library, as a Python package, as an R package, and as a command line tool. These packages come with extensive online documentation, accessible at https://hydrogr. github. io/evalhyd (last access: 22 May 2023). Some of the developments envisaged for future versions of the tool include the addition of other evaluation metrics, especially multivariate ones, the implementation of additional bindings for other open source languages (e.
g. Julia, Octave), and the support of configuration files common across the software stack to further simplify collaboration and reproducibility. Through its polyglot character, this tool is aimed at the hydrological community as a whole, and we hope that it can foster collaborations amongst its users without a programming language barrier. For example, through the organisation of user workshops, new evaluation metrics and new evaluation strategies could emerge that could be implemented in the tool to directly benefit our community at large. -rbind(c(4. 7, 4. 3, 5. 5, 2. 7, 4. 1)) prd !<-array  ( rbind(c(5. 3, 4. 2, 5. 7, 2. 3, 3. 1), c(4. 3, 4. 2, 4. 7, 4. 3, 3. 3), c(5. 3, 5. 2, 5. 7, 2. 3, 3. 9 )), dim=c(1, 1, 3, 5) ) thr !<-rbind(c(4. , 5. )). /evalhyd evalp \ ". /obs/" ". /prd/" "BS" \ !--thr ".
Acknowledgements. The authors acknowledge the financial support of the  French Ministry of Environment  ( DGPR /SNRH/SCHAPI)  for this work. The authors would like to thank  Shaun Harrigan  from  ECMWF  for the help with pre-processing the GloFAS reforecasts,  Antoine Prouvost  and  Johan Mabille  from  QuantStack  for the help with using the xtensor software stack, and  Guillaume Thirel ,  Laurent Strohmenger , and  Léonard Santos  from  INRAE  for their feedback on this paper.
Code availability. The package used for the illustrative example is evalhyd-python. It is available from HAL (https://hal. science/ hal-04088473, last access: 11 May 2023) (Hallouin and Bourgin, 2023). The scripts used to produce the figures in the illustrative example are available upon request from the corresponding author. Data availability. The observation data used in this study, i. e. the GloFAS-ERA5 v2. 1 river discharge reanalysis data, can be downloaded from Copernicus' Climate Data Store (https://doi. org/10. 24381/cds. a4fdd6b9, last access: 10 May 2023)  (Harrigan et al. , 2021). The predic-.
Study Design This analysis combined data from the D:A:D and RESPOND cohort collaborations. D:A:D and RESPOND are prospective, multi-cohort collaborations from across Europe and Australia, including 11 cohorts and approximately 49,000 individuals with HIV in D:A:D, and 17 cohorts and approximately 35,000 individuals in RESPOND. D:A:D was initiated in 1999 and ran until 2016; RESPOND was initiated in 2017 and is ongoing. Several cohorts contributed data to both D:A:D and RESPOND, and a subset of individuals was included in both collaborations (i. e. , they contributed data to both D:A:D and RESPOND). Further details on each study have been published elsewhere  [30, 31]. D:A:D and RESPOND use the same underlying methodology and collect similar data. In both collaborations, clinical and demographic data are collected on individuals during routine clinical care.
Data are reported at the time of enrolment into the collaboration and prospectively annually thereafter. Data in RESPOND are also collected on the 5 years prior to enrolment, and earlier if available. Data on clinical events, including cancers, are collected in real time. Clinical events occurring during the study's validation period (in D:A:D: 2004 onwards; in RESPOND: 12 months prior to the last local cohort visit before RESPOND enrolment onwards) are reported using a study-specific case report form. These events are centrally validated by a clinician at the study coordinating centre using a prespecified algorithm  [32, 33]. Any missing information is queried, and a selection of all cancer events are externally reviewed further by an oncologist.
Individuals in D:A:D were followed until the earliest of first cancer event, 6 months after last clinic visit, or 1 February 2016 (D:A:D end date)  [34]. Individuals in RESPOND were followed until the earliest of the first cancer event, final follow-up, or 31 December 2021 (RESPOND censoring date), with final follow-up defined as the latest of the most recent CD4 count, VL measurement, ART start date, drop out date, or date of death. Individuals who were included in both D:A:D and RESPOND were followed until the latest of the final follow-up in D:A:D or the final follow-up in RESPOND. Data for these individuals were merged (i. e. , any repeated data from the overlapping time period between D:A:D and RESPOND was deleted), and for individuals with conflicting data in the two collaborations, data were used from D:A:D until the date of the RESPOND baseline, and data from RESPOND were used thereafter, unless there was a clear error in one of the studies or data were missing from one study and not the other.
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/cancers15143640/s1, Table  S1 : Cancers reported during followup, split into AIDS-defining and non-AIDS-defining cancers; Table  S2 : Cancers reported during follow-up, split by infection-related, smoking-related, and BMI-related cancers; Figure  S1   S3 : Results from a range of sensitivity analyses for all cancer, AIDS-defining cancer, non-AIDS-defining cancer; Table  S4 : Results from a range of sensitivity analyses for infection-related cancer, smoking-related cancer, and BMI-related cancer. Informed Consent Statement: Participants consented to share data with RESPOND according to local requirements. Participants were pseudonymised at enrolment by assignment of a unique identifier by the participating cohort before data transfer to the main RESPOND database.
According to national or local requirements, all cohorts have approval to share data with RESPOND. Ethical approval was obtained, if required, from the relevant bodies for collection and sharing of data. Data are stored on secure servers at the RESPOND coordinating centre in Copenhagen, Denmark, in accordance with current legislation and under approval by The Danish Data Protection Agency (approval number 2012-58-0004, RH-2018-15, 26 January 2018), under the EU General Data Protection Regulation (2016/679). All participating cohorts followed local or national guidelines or regulations regarding patient consent and ethical review. Of the countries with participating cohorts, only Australia and Switzerland require specific ethical approval for the entire D:A:D cohort in addition to that required for their national cohorts (the Australian HIV Observational Database and the Swiss HIV Cohort Study); France (Nice and Aquitaine cohorts), Italy (ICONA cohort), and Belgium (Brussels Saint-Pierre cohort) do not require specific ethical approval more than that required for the individual cohorts; and the Netherlands (AIDS Therapy in the Netherlands project) does not require any specific ethical approval, because data are provided as part of HIV care.