dataesr/scientific-paragraphs-categorization huggingface dataset:
Proof-of-concept tools and libraries have been already developed at CU/LASP, UCB/SSL, and UCLA, with support for loading and plotting data from the THEMIS/ARTEMIS spacecraft and several ground magnetometer networks. Under the PySPEDAS structure, "*_load_xxx" routines have been developed to access CDF files and import data to populate pytplot variables in a generic way. Pytplot variables have been demonstrated to have a similar functionality as tplot variables in SPEDAS. Currently the SPEDAS team under NASA/HQ encouragement is in the process of reaching out to other Python developers in the space physics community, in order to better coordinate the various ongoing Python software development projects and to minimize duplication of effort. There are a number of Python libraries available for reading CDF files and other data types, and then conducting standard numerical analysis and plotting.
However, many SPEDAS capabilities (for example, computing moments from 3D plasma instrument data) do not exist yet in Python and will have to be developed. The advantages of Python-based code are that: (i) a new generation of scientists and programmers are already quite versed in it; (ii) it is free; (iii) it has significant general-purpose libraries associated with it; and (iv) it is insurance for NASA Heliophysics, in case IDL becomes too expensive or falters. It is expected that within a year the nucleus of a PySPEDAS system will be in place for experimentation and further contributions by a wider sector of the community, with a set of guidelines that will render such contributions most constructive.
Other Potential Uses In past years, distributed datasets and VxOs have enhanced data accessibility to the Heliophysics community though SPASE, whereas inter-and intra-project data distribution and interoperability through common analysis tools have been facilitated by CDAWeb, Autoplot and more recently SPEDAS. However, as datasets become larger and more widely distributed, further abstraction of the data interfaces is expected, in order to facilitate or enhance accessibility. Toward that goal, NASA's Heliophysics community has created a framework for a common set of web services for delivering digital time series data, the Heliophysics Application Programmer's Interface (HAPI). HAPI specifies a set of minimum but sufficient set of requirements (Data Access Specifications, currently in version 2.
0. 0, see: https://github. com/hapi-server/data-specification/) for a server to deliver arbitrary time series data. A few "endpoints" (access descriptors) allow clients (data users) to fully determine the data holdings and request data from the server. This specification generalizes a server's delivery requirements to be accessed by external software engines which are cognizant of the HAPI specifications. The SPEDAS team intends to support this development with the goal of implementing mature, stable, comprehensive interfaces to the new types of servers, and especially to bridge some of the differences in metadata representation and usage among the various external tools and services. The recent SPEDAS release supports data provision by servers which adhere to the HAPI specification through its command-line routine "hapi_load_data" as demonstrated in "crib_hapi.
pro", and its GUI button "Load_Data_using_HAPI". HAPI usage across the Heliophysics community would further relax (obviate) the SPEDAS desirements for data providers to supply data in ISTP-compliant CDF files and in SPEDAS-friendly directory structures, as the data is queried and loaded into the user's SPEDAS session from the data providers' own HAPI server through the above load data procedure. In addition, HAPI support simplifies the process of creating a command-line plug-in in SPEDAS, as developers would simply need to create a wrapper-routine around the SPEDASprovided "hapi_load_data". This wrapper routine supplies the "hapi_load_data" subroutine the provider's HAPI server and dataset information; the subroutine then queries the remote server, downloads the data and maps it into tplot variables.
The development of a commandline routine for bringing data into SPEDAS is then simply a matter of ensuring that various plotting metadata are filled-in (e. g. , default y-axis and z-axis limits on data products), documenting the data products, and ensuring the routine is well-documented and quality assured via testing. This would be ideally done by the data provider through the load routine, or can be done as a last resort even by the user, if, for example, resources do not exist at the project's own institution to perform such documentation. The HAPI interface to arbitrary datasets has already been put to use in SPEDAS to write generic code to introduce new plug-in data to the system in a most efficient way (under Developers' Guide → Command Line → Load Data). More information on the SPEDAS-HAPI interfaces can be found in the SPEDAS wiki (Developers' Guide → HAPI).
Other data access specification models, similar to HAPI, also exist. By virtue of prescribing clearly defined interfaces to the data they are also easy to link with SPEDAS. Such is the example of the "DAS" model (http://das. org), currently in version 2 (DAS2, http://www-pw. physics. uiowa. edu/das2/Das2. 2. 2-ICD_2017-05-09. pdf) developed to support waves instruments at the University of Iowa. It is used to distribute waveform data to teams in IDL (primarily to be viewed via Autoplot) and in Python. Given the large number of projects served by this tool, the complementarity of the data being served to those that exist at SPDF and at VxO repositories, and the use of this tool for current space physics missions (e. g. , NASA's JUNO mission) the SPEDAS team is considering investing the time to create the necessary interface with those tools.
As Heliophysics is moving towards constellationclass missions, and reliance on large arrays of ground-based datasets is also increasing  (Su-perDARN; Transition Region Explorer, TREX, Donovan 2017; ULTIMA, etc. )  this trend for efficient analysis is going to continue. We anticipate that standardization of interfaces such as HAPI under the auspices of the HPDE will further accelerate the ease, accuracy and comprehensiveness of data distribution, and seamlessly enable the impending increase in data sources, data volume and efficiency in scientific interactions. Further, we anticipate the exchange, peer-review and publication of crib-sheets and GUI documents to become commonplace, as a means of accurately reproducing scientific information from large, disparate sets of data. In that regard, the personnel and software management structure, quality assurance, version control, implementation heritage, excellence in user support and responsiveness to community feedback that SPEDAS has introduced are expected to be far more important and enduring than the specific low level language used to implement its calls at any point in its history.
BRAF mutations are detected in 8% to 12% of mCRC cases, with a 90% prevalence of BRAF p. (Val600Glu) mutations, and are associated with a poor prognosis in this pathology  [8, 9]. For patients with mCRC, KRAS, NRAS and BRAF tumour genotyping has therefore a major impact on clinical management and genotyping results should be available within 7 working days to ensure a rational first-line treatment selection based on validated molecular data  [7]. Genotyping of mCRC is commonly assessed by next-generation sequencing (NGS) which allows the determination of mutational status of a panel of relevant genes. NGS workflow from DNA extraction to results often takes 2 to 4 days depending on the sequencing technology and instrument, but is often closer to 10 days in a clinical testing routine context since all samples are analysed in batch.
Sample selection A total of 38 extracted DNA from 38 different FFPE tumour samples with a minimal purity of 10% and a NGS-confirmed KRAS, NRAS or BRAF mutation were retrospectively selected among the biological samples collection of Institut de Cancerologie de Lorraine (ICL, Nancy, France). These samples have been collected after routine tumour sequencing for patient cancer management. All patients involved in this study gave their verbal informed consent for the research of KRAS, NRAS and BRAF mutations. The experimental protocol of this study has been approved by the ethical and scientific board of Institut de Cancerologie de Lorraine. All methods were performed in accordance with the relevant guidelines and regulations. All data were anonymized prior to analysis. Among the 38 patients selected for this study, tumour localisation was colon in 29 (76.
Analysis of mCRC tumour samples using NGS DNA was previously extracted from routine mCRC tumour samples. FFPE tissues have been macrodissected after hematoxylin-eosin slide examination and tumour purity determination by a senior pathologist. DNA has been extracted using QIAamp DNA FFPE tissue kit (Qiagen, Hilden, Germany) and DNA concentration measured using the Qubit dsDNA HS assay kit (Qubit 3. 0 Fluorometer, ThermoFisher Scientific Inc, Massachusetts, USA). DNA quality was assessed by a cycle of quantification (Cq)-based measurement using the TruSeq FFPE DNA Library Prep QC kit (Illumina, San Diego, USA) following the manufacturer's recommendations. Delta-Cq (ΔCq) were calculated for each sample using LightCycler 480 Software W UDF 2. 0. 0 (Roche Diagnostics) by subtracting the Cq value of the sample from the Cq value of the kit's internal control.
ΔCq close or inferior to 0 are retrieved in samples with high DNA quality, whereas ΔCq are greater than 6 for DNA with a quality not suitable for sequencing. Samples with ΔCq included in the [0-6] range have an intermediate DNA quality. For samples with ΔCq < 6, libraries were prepared using the "Panel INCa" TruSeq Custom Amplicon Library Preparation Kit v1. 5 (that includes 16 genes of interest in theragnostic: AKT1, ALK, BRAF, EGFR, ERBB2, ERBB4, FGFR2, FGFR3, HRAS, KIT, KRAS, MAP2K1, MET, NRAS, PDGFRA, PIK3CA) (Illumina, San Diego, CA, USA) and then sequenced using MiSeq instrument (Illumina) as previously described  [12]. After sequencing, data were analysed using Sophia DDM software v. 4. 3 (Sophia genetics, Saint-Sulpice, Switzerland). Sequences were aligned using reference genome GRCh37/hg19 and SNV and indels provided by the software with their Variant allele frequency (VAF).
All steps are automatically performed up to the final report  [12]. A Cycle of quantification value (Cq) is calculated for every valid curve by the Idylla console software. Sample Processing Control (SPC) signals reported the amplification of the DNA regions external of the mutational hotspot in each cartridge and testified to sufficient DNA quantity and quality for the analysis. The presence of a mutant genotype is determined by calculating the difference between the SPC Cq and the Cq obtained for the mutant signal. This difference between the control signal and the mutant signal is defined as the ΔCq. The Idylla KRAS mutation test is designed for the qualitative detection of 21 hotspots mutations in exons 2, 3 and 4 of the KRAS gene with results available after a 130 minutes run. The Idylla NRAS-BRAF mutation test is designed for the qualitative detection of 18 mutations in exon 2, 3 and 4 of the NRAS gene and 5 mutations in exon 15 of the BRAF gene with results available after a 110 minutes run (Table  2 ).
Cells were rinsed with PBS and microfluidic devices were topped with 90% glycerol. Samples were imaged with a LSM 880 confocal microscope (Zeiss, Oberkochen, Germany) equipped with a 63× 1. 4 NA objective. Images were acquired at zoom 2 in z-stacks of 0. 5 µm interval. Typically, four images were acquired per device from the synapse chamber near the postsynaptic chamber such the image contains multiple dendrites. Images were deconvoluted using AutoQuantX3 software (BitPlane, Zurich, Switzerland) for synaptic connectivity analysis. In a subset of experiments, neurons were plated only in the presynaptic chamber, only in the postsynaptic chamber, or only in the synaptic chamber. Neurons were fixed and immunostained at DIV14 as described to reveal nuclei, GFAP (ABD95; Millipore) and β3-tubulin (MAB1637, Millipore).
Quantification of synapse integrity We developed an image analysis workflow based on image segmentation using Imaris software (BitPlane) and assignment of postsynaptic signals to the nearest presynaptic signal using a custom Matlab (Mathworks; Natick, MA) code. Briefly, signals obtained for pre-and postsynaptic structures were first deconvoluted in Autoquant X3 (Media Cybernetics; Rockville, MD) and segmented into distinct volumes using the surfaces function of Imaris in the batch mode that permits the same parameters to be applied to all images (Supplementary Fig. 1A-D ). Postsynaptic spots were then assigned to the nearest presynaptic spot (according to the 3-D Euclidean distance between intensity centers) within a predefined cut-off distance (Supplementary Fig. 1E ). It is important to note that pre-and postsynaptic structures are smaller than the axial resolution of the confocal microscope; the distances measured are thus not true physical distances between synaptic structures, but the distances between respective intensity centers.
We empirically determined the cut-off distance by testing a range of values on a large set of control cultures (Supplementary Fig. 1F-G ). The fraction of Syp puncta with at least one Homer assignments and the average number of Homer assignments per Syp were determined to be the most robust readouts of synapse connectivity. The numbers of assigned pre-and postsynaptic puncta per image area (in the xy-plane) were also provided. Note that a small fraction of control samples for CHO-APP WT and CHO-APP LDN co-cultures (three and two devices, respectively) do overlap in the data reported.
Analysis of gene expression alterations in human brain samples RNA sequencing (RNAseq) data from the Mount Sinai/JJ Peters VA Medical Center Brain Bank (MSBB)  (Wang et al. , 2018) , the ROSMAP database  (De Jager et al. , 2018) , and the Mayo Clinic whole genome and transcriptome data  (Allen et al. , 2016)  were downloaded from AMP-AD Knowledge Portal (https://adknowledgeportal. synapse. org/) according to the terms and conditions concerning the use of the data. Data were aligned using pseudoaligner Kallisto version 0. 43. 1  (Bray et al. , 2016)  using a pre-built index to align fastq files. Differential gene expression analysis was performed using DESeq2  (Love et al. , 2014). First, a DESeq2 object was created using DESeqDataSetFromTximport function and rows with sum of all counts less than 10 were filtered out.
Next, DESeq function was used with default parameters. Temporal cortex was analyzed in the Mayo Clinic dataset (82 cases and 78 healthy controls). Dorsolateral prefrontal cortex was analyzed in the ROSMAP dataset (222 cases and 201 controls). The following brain areas were analyzed in the MSBB dataset: Brodmann area (BA) 10, which corresponds to the anterior prefrontal cortex (105 cases and 71 controls); BA 22, which is part of the Wernicke's area in the superior temporal gyrus (98 cases and 61 controls); BA 36, which corresponds to the lateral perirhinal cortex (88 cases and 64 controls); and BA 44, which corresponds to the inferior frontal gyrus (90 cases and 63 controls).
Analysis of phosphorylated and total Pyk2 levels in human brain samples The brain samples were collected through a brain donation program dedicated to neurodegenerative dementias coordinated by the NeuroCEB Brain Bank Network. The informed consent for post-mortem examination and research studies was signed by the legal representative of each patient in patient's name, as allowed by the French law and approved by the local ethics committee and the brain bank has been officially authorized to provide samples to scientists (agreement AC-2013-1887). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committees and with the 1964 Helsinki declaration. The brain banks fulfill criteria from the French Law on biological resources including informed consent, ethics review committee and data protection (article L1243-4 du Code de la Santé publique, August 2007).
Western blots of the conditioned media showed that DAPT treatment eliminated all Aβ secretion and induced a slight increase in the levels of APP (Fig. 3A ). However, WBs of conditioned media following DAPT treatment exhibited a 15 kDa band that overlapped with oligomers of low MW Aβ 1-42. Treatment with neither antibody affected the presence of low-MW Aβ 1-42 forms. Interestingly, the 3D6 antibody induced a strong increase in monomeric Aβ 1-42 levels in both CHO-APP WT and CHO-APP LDN media, in accordance with the idea that 3D6 arrests the peptide in monomeric form and precludes oligomer formation. We also performed Alpha-LISA and ELISA measurements from media collected from the co-culture chamber, following the treatment of CHO cells with the aforementioned antibodies for 5 d. However, the 3D6 antibody interfered with the measurements and data could not be reported.
Pyk2 overexpression in "postsynaptic" neurons blocks CHO-APP LDN -induced decrease in synapse connectivity We next assessed the relevance of our microfluidic tool to establish whether genetic risk factors of Alzheimer's disease may be involved in Aβ-dependent synaptotoxicity. Several lines of evidence indicate that genetically driven synaptic failure may occur in Alzheimer's disease  (Dourlen et al. , 2019)  and among the different genetic risk factors susceptible to be studied in our model we focused on PTK2B which has been already described to be involved in synaptic functions  (Giralt et al. , 2017). We evaluated potential changes in the expression of PTK2B in the brains of Alzheimer's disease patients compared to healthy individuals by taking advantage of three publicly available RNAseq datasets: Mayo Clinic data that probed the temporal cortex  (Allen et al.
, 2016) ; ROSMAP data that probed the dorsolateral prefrontal cortex  (De Jager et al. , 2018) ; and the MSBB data that probed four different brain areas: BA 10, BA 22, BA 36, and BA 44  (Wang et al. , 2018). This allowed us to investigate potential gene expression changes in brain regions that are affected at different pathological stages of Alzheimer's disease  (Braak and Braak, 1991). We observed a decrease in PTK2B expression in Alzheimer's cases compared to healthy controls in all brain regions analyzed (Table  S2 ); however, after multiple testing correction this decrease was significant only in the BA 22 (16. 69% decrease; p adj = 1. 99×10 -02 ), BA 36 (28. 89% decrease; p adj = 8. 49×10 -05 ), and BA 44 (15. 18% decrease; p adj = 3. 87×10 -02 ) regions of the MSBB dataset, as well as in the ROSMAP dataset (28.
Acknowledgements This work was partly supported by the  French RENATECH network  ( P-16-01891 ). This study was funded by  INSERM ,  Institut Pasteur de Lille , the  EU Joint Programme -Neurodegenerative Diseases Research  (JPND; 3DMiniBrain),  Agence Nationale de la Recherche  ( ANR-19-CE16-0020 ), and  Fondation Vaincre Alzheimer  ( FR-17006p ). This work was also funded by the  Lille Métropole Communauté Urbaine  and the  French government's LABEX DISTALZ program  (Development of innovative strategies for a transdisciplinary approach to Alzheimer's disease). D. M. -C. was supported by a PhD scholarship from  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). This work was also co-funded by the  European Union  under the  European Regional Development Fund (ERDF)  and by the  Hauts de France Regional Council  (contract no.
No accumulation of glycolate, formate, or acetate was observed, indicating that these products of nonenzymatic DHA degradation are coconsumed with DHA by wild-type E. coli. In order to infer quantitative flux information from these data, we developed an R program, PhysioFit, a mathematical model to estimate the growth rate and exchange (uptake or production) fluxes. This model, which takes into account nonenzymatic degradation of substrates, is described in detail in Materials and Methods. This program calculated a growth rate of 0. 15 Ϯ 0. 03 h Ϫ1 with a DHA uptake rate of 5. 2 Ϯ 0. 19 mmol g DW Ϫ1 h Ϫ1 (corresponding to 15. 6 Cmmol g DW Ϫ1 h Ϫ1 ; g DW is grams of dry weight). Based on the nonenzymatic conversion yields of DHA, we estimated that DHA accounted for 74. 3% of E. coli carbon uptake (expressed in Cmmol), with the remaining carbon taken up as formate (15.
The images were analyzed with the software DEVA (v1. 2. 1). All array procedures were performed using the GeT-Biopuces platform (http://get. genotoul. fr/). For the wild-type strain, gene expression of batch cultures with DHA was expressed relative to the ratio of gene expression of chemostatic growth with glucose at low ( ϭ 0. 1 h Ϫ1 ) and high ( ϭ 0. 6 h Ϫ1 ) growth rates (data from reference  [60] ). This allowed us to focus only on genes whose level of expression changes in relation to the nature of the substrate while avoiding highlighting of genes whose expression changes because of the very different growth rate. For the ΔdhaKLM and ΔptsA::kan mutants, gene expression on DHA was compared to the wild type's gene expression on DHA. Gene ontology analyses were performed using EcoCyc  (61).
NMR and HPLC analysis. NMR analyses were performed on an Avance III 800-MHz spectrometer (Bruker, Rheinstetten, Germany) equipped with a 5-mm QPCI cryogenic probe head at 280K. Supernatants were analyzed by quantitative 1 H one-dimensional NMR at 280K using a zgpr30 sequence with water presaturation. A total of 32 scans were accumulated after 8 dummy scans. The time domain function (the FID) was converted to the frequency domain function (the spectrum) by Fourier transform. The phase of the spectra was adjusted manually, the baseline was corrected automatically, and the spectra were aligned using the signal from 3-trimethylsilylpropionic-2,2,3,3-d4 acid sodium salt (TSP-d4), an internal standard, with the Bruker software TopSpin (v3. 5). Propane-1,2,2,3-tetrol was identified using 2D heteronuclear single quantum correlation spectroscopy (HSQC) and heteronuclear multiple-bond correlation spectroscopy (HMBC) spectra.
The HSQC experiment was acquired with 2,000 by 1,024 points for a spectral width of 13. 95 by 140 ppm in the 1 H and 13 C dimensions, respectively. It was processed with 4,000 by 1,024 points. The HMBC experiment was acquired with 2,000 by 1,024 points, for a spectral width of 13. 95 by 220 ppm in the 1 H and 13 C dimensions, respectively. It was processed with 8,000 by 1,024 points. The HSQC and HMBC data were acquired with 16 scans per increment. Both 2D spectra were calibrated in the frequency domain by setting the peak from TPS-d4 to 0 ppm in both dimensions. The spectra were processed using TopSpin 3. 5. For transcriptomic analysis, HPLC analyses were performed using a column made of an H ϩ chromatography resin (Zorbax-C 18 ; Agilent Technologies). A solution of sulfuric acid (5%, vol/vol) was used as the eluent at a flow rate of 0.
Integrating equations 1 and 2 provides the following analytical functions: X ͑ t ͒ ϭ X 0 • e t (3) M i͑ t ͒ ϭ q M i • X 0 ϩ k • ͑ e t Ϫ e Ϫkt ͒ ϩ M i 0 • e Ϫkt (4) This formalism has been implemented in PhysioFit, a flexible R program that allows growth rates and exchange fluxes to be quantified by fitting time variations of extracellular metabolite and biomass concentrations using the nlsic algorithm  (62). PhysioFit includes options to account (or not) for the degradation of extracellular compounds and a lag phase before cells start to grow, and it implements sensitivity analyses to evaluate the precision of the estimated fluxes. PhysioFit is provided open source at https://github. com/MetaSys-LISBP/PhysioFit. A conversion factor was used to obtain cellular dry weights (DWs) from OD 600 (0.