dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: This work was funded by the  Institut Lejeune  (grant number  EGCG-16RRSMT-IJL-1-JANEZ ) and  French state funds through the "Agence Nationale de la Recherche" program  (grant number  ANR-18-CE16-0020 DYRKDOWN ). MD is supported by the  CRG Severo Ochoa excellence  grant (grant number  SEV-2016-0571 ); the  CIBER of Rare Diseases; and Secretaria d'Universitats i Recerca del Departament d'Economia I Coneixement de la Generalitat de Catalunya  (grant number Grups consolidats 2017SGR926).
Erythrocyte preparations Experiments involving human erythrocytes were approved by our institutional review board at Centre Hospitalier Universitaire (La Reunion, France). Samples were obtained from the Alb-Ox ERMIES an ancillary, pilot study of ERMIES (NCT01425866). All data were analyzed anonymously. Blood from healthy volunteers with informed consent was collected in EDTA tubes (BD vacutainer1). After centrifugation, erythrocytes were washed 3 times with a sterile isotonic solution (NaCl 0. 15 M, pH 7) and suspended to 20% hematocrit in sterile PBS. This solution was subsequently diluted with glucose stock solutions made in PBS to form incubation mixtures of erythrocytes with 0, 5, 25 and 137 mmol/l glucose (corresponding to 0, 0. 9, 4. 5 and 25 mg/mL glucose, respectively). After 5 days of incubation at 37˚C, erythrocytes were washed 3 to 4 times with 0.
15 M NaCl. For specific studies, erythrocytes from 12 type-2 diabetic individuals (HbA1c (%) = 11 ± 2. 6) and from 9 non-diabetic individuals of the same age (HbA1c (%) = 4. 4 ± 1) were obtained from the Alb-Ox ERMIES an ancillary, pilot study of ERMIES (NCT01425866). Erythrocytes were isolated as previously described and then suspended to 10% hematocrit in sterile PBS before incubation at 37˚C for 5 days. Erythrocyte preparations were either directly analysed by FACS and ektacytometry, lysed with an equivalent volume of distilled water (enzymatic activities, redox status) or subjected to "ghost" preparation (membrane oxidative damage). In lysates, optical density was measured at 280 nm and protein concentration was calculated according to a standard curve of hemoglobin (50-500 μg) and expressed in μg per μL.
Flow cytometry assays Erythrocyte shape, eryptosis evaluation and intracellular reactive oxygen species (ROS) production in our different erythrocyte preparations were measured by flow cytometry using Beckman Coulter's CytoFLEX and Cytexpert software. A specific erythrocyte cell population was selected by gating and could be characterized by its typical location in a forward scatter (FSC) versus a side scatter (SSC) parameter graph. For phosphatidylserine exposure determination, erythrocytes were incubated with 2 μg/ml Annexin V-FITC in binding buffer (BioLegend) for 30 min at RT before flow cytometry analysis. Annexin V protein exhibits a high affinity for phosphatidylserine (PS) and was measured with an excitation wavelength of 488 nm and an emission wavelength of 530 nm. For evaluation of intracellular reactive oxygen species (ROS) production, erythrocytes were incubated with 2 μM of the fluorescent probe dihydroethidium (DHE; Sigma-Aldrich, D7008) or dichlorodihydrofluoresceindiacetate (DCFH-DA; Sigma-Aldrich, D6883) for 30 min at RT.
Statistical analysis Data are expressed as the mean ± standard deviation (SD) or as the mean ± standard error of the mean (SEM) from at least three independent experiments performed in triplicate. Statistical analyses were performed with Prism (GraphPad Software Inc. , San Diego, CA, USA). Statistical significance was determined using the Student's t-test or one-way ANOVA followed by Dunnett's test, with a p-value < 0. 05 required for significance.
These data clearly indicate that the glycation phenomenon induced by the hyperglycemic incubations renders erythrocytes less deformable (about four times less) and more rigid than erythrocytes incubated under low glycemic conditions. It is worth noting that impaired deformability of glycated erythrocytes was also observed under low shear stress conditions (3 Pa), which are similar to those that can be encountered under standard physiological conditions  [29]. Erythrocyte morphology and deformability were subsequently investigated in order to get further insights into erythrocyte fragility. Flow cytometry was used to determine the relative size of our different erythrocyte preparations. By using the FSC and SSC parameters, each Table  2. Characterisation of glycation percentage in the different erythrocyte preparations by mass spectrometry.
Data for the four main peak of the mass spectra corresponding to α-hemoglobin (α-Hb; 15130 Da), glycated α-hemoglobin (g-α-Hb; 15330 Da), β-hemoglobin (β-Hb; 15890 Da), glycated β-hemoglobin (g-β-Hb; 16100 Da) (S1 Fig-see supplemental data). G0, G5, G25 and G137 represent the four conditions of incubation to which erythrocytes were subjected: 0, 5, 25 and 137 mmol/l glucose, respectively. Δmass and % glycation were calculated as explained in the methods section. Results are the mean ± SD and statistical analyses were performed using Tukey's post hoc analysis following a significant one way ANOVA: ��� p<0. 001, �� p<0. 01, � p<0. 05 (vs. G0), ###p<0. 001, #p<0. 05 (vs. G5). preparation containing a mix of erythrocytes was analysed and specific populations were gated according to cell size and granularity.
Discussion Despite the fact that erythrocytes represent a key player in vascular complications, very little is known about how structure, redox status and capacity of erythrocytes to be phagocytosed by endothelial cells can be affected by glycation. In this study we revealed that in vitro glycation ## p<0. 01 (vs. G5) using Student's t test. (D) Phosphatidyl serine (PS) exposure in erythrocytes preparations was evaluated by cytometry as described in method section. Data are expressed as mean ± SEM, � p< 0. 05, ��� p< 0. 001 vs. G0 (one-way ANOVA followed by Dunnett's test, n = 4). (E) Internalized red blood cell in cultured EA. hy926 cell lines was determined by DAF assay and are expressed in arbitrary unit as mean ± SEM (n = 3), � p < 0. 05, �� p< 0. 01 (one-way ANOVA followed by Dunnett's test).
Results Tuberculosis-associated microenvironments induce Siglec-1 in macrophages TB-induced M(IL-10) macrophages are highly susceptible to HIV-1 infection and spread  (Souriant et al. , 2019). To assess the global gene expression landscape in these cells, we performed a genome-wide transcriptome analysis (GEO submission GSE139511). To this end, we employed our published in vitro model  (Lastrucci et al. , 2015) , which relies on the use of conditioned medium from either mock-(cmCTR) or Mtb-infected (cmMTB) human macrophages. As we described and observed before and herein, cmMTB-treated cells were positive for the M(IL-10) markers (CD16 + -CD163 + MerTK + and phosphorylated STAT3), and displayed a high rate of HIV-1 infection, as compared to those treated with cmCTR  (Lastrucci et al. , 2015).
120 TNT in at least 1000 cells were analyzed from four independent donors. (F) Representative immunofluorescence images of cmMTB-treated macrophages infected with HIV-1-ADA loaded with MitoTracker (MitoT, green), and stained for extracellular Siglec-1 (red) and WGA (grey). Inserts are 3x zooms. Green arrowheads show mitochondria inside TNT. Scale bar, 10 mm. Source data 1. Raw data and statistical analyses supporting a role for Siglec-1 in TNT length, HIV-1 and mitochondrial cell-to-cell trasfer, and exacerbation of HIV-1 infection. Figure supplement 1. Siglec-1 is required for the capture and transfer of HIV-1 in cmMTB-treated macrophages. Figure supplement 1-source data 1. Supplemental raw data and statistical analyses supporting the functional role of Siglec-1 in human macrophages using an siRNA-mediated gene silencing approach.
The samples were then incubated for 5 min at 65˚C, and 5 min at 42˚C before loading for 17 hr at 42˚C, according to manufacturer's protocol. After washing, the microarrays were scanned with MS200 microarray scanner (Roche NimbleGen), and using Feature Extraction software, the Agilent raw files were extracted and then processed through Bioconductor (version 3. 1) in the R statistical environment (version 3. 6. 0). Using the limma package, raw expression values were background corrected in a 'normexp' fashion and then quantile normalized and log 2 transformed  (Ritchie et al. , 2015). Density plots, boxplots, principal component analyses, and hierarchical clustering assessed the quality of the hybridizations. Differentially expressed genes between macrophages exposed to cmCTR or cmMTB supernatants were extracted based on the p-value corrected using the Benjamini-Hochberg procedure.
Gating on macrophage population was set according to its Forward Scatter (FSC) and Size Scatter (SSC) properties before doublet exclusion and analysis of the median fluorescence intensity (MFI) for each staining. To determine Siglec-1 expression, we applied a quantitative FACS assay. Briefly, cmCTR-and cmMTB-treated macrophages were detached using Accutase solution (Gibco) for 10 min at 37˚C, washed, blocked with 1 mg/mL human IgG (Privigen, Behring CSL), and stained with mAb 7-239 a-Siglec-1-PE or matched isotype-PE control (Biolegend) at 4˚C for 30 min. The mean number of Siglec-1 mAb binding sites per cell was obtained with a Quantibrite kit (Becton Dickinson) as previously described  (Izquierdo-Useros et al. , 2012b). Samples were analyzed with FACSCalibur using CellQuest software to evaluate collected data.
Quantification and statistical analysis Information on the statistical tests used and the exact values of n (donors) can be found in the Figure Legends. All statistical analyses were performed using GraphPad Prism 8. 0. 0 (GraphPad Software Inc). Two-tailed paired or unpaired t-test was applied on data sets with a normal distribution (determined using Kolmogorov-Smirnov test), whereas two-tailed Mann-Whitney (unpaired test) or Wilcoxon matched-paired signed rank tests were used otherwise. p<0. 05 was considered as the level of statistical significance (*p 0. 05; **p 0. 005; ***p 0. 0005; ****p 0. 0001). technical help and advice provided. We greatly thank Y-M Boudehen for technical expertise provided in molecular biology, M Dalod and B Raynaud-Messina for fruitful discussions, and S Benichou for providing HIV-1 strains.
This work was supported by the Centre National de la Recherche Scientifique, UniversitePaul Sabatier, the Agence Nationale de la Recherche (ANR16-CE13-0005-01, ANR-11-EQUIPEX-0003), the Agence Nationale de Recherche sur le Sida et les Hepatites virales ( ANRS2014-049, ANRS2018-01, ANRS2020-01), the ECOS-Sud program (A14S01), the Fondation pour la Recherche Medicale (DEQ2016 0334894; DEQ2016 0334902), INSERM Plan Cancer, Human Frontier Science Program (HFSP: RGP0035/2016), the Argentinean National Agency of Promotion of Science and Technology (PICT-2015-0055 and PICT-2017-1317). We also thank the AIDS Research and Reference Reagent Program, Division of AIDS, NIAID. The NHP study was supported by NIH award OD011104, AI111943, AI111914, AI097059 and AI110163. The genetic analyses were realized within the framework of the Swiss HIV Cohort Study (SHCS Project number 717), which is supported by the Swiss National Science Foundation (Grant Number 148522) and by the SHCS research foundation.
MD is supported by an ATP (Axes Thematiques Prioritaires) doctoral scholarship from UniversitePaul Sabatier, SS by a 4 th -year doctoral scholarship from Sidaction, and S R by a scholarship from Toulouse University Hospital to perform a Master's degree. JM-P and NI-U are supported by the Spanish Secretariat of State of Research, Development and Innovation through grant SAF2016-80033-R, JM-P by the Spanish AIDS network Red Tema´tica Cooperativa de Investigacion en SIDA, and SB by the Rio Hortega programme funded by the Spanish Health Institute Carlos III (No. CM17/00242).
Acknowledgements We greatly acknowledge  F Capilla  and  T Al Saati ,  US006/CREFRE  for histology analyses; P Constant,  F Levillain ,  F Moreau  and  C Berrone , IPBS and  Genotoul Anexplo-IPBS , for accessing the BSL3 facilities;  E Na ¨ser ,  E Vega ,  A Peixoto ,  S Mazeres  and the  Genotoul TRI-IPBS  facilities for imaging and flow cytometry. We thank  F Quiroga  and  C del Carmen Melucci Ganzarain ,  Instituto de Investigaciones Biomedicas en Retrovirus y SIDA, INBIRS UBA -CONICET, Buenos Aires, Argentina , for the.
Model description The mechanical interactions between the storage solution and the surrounding compressible firn are modelled using the code Elmer/Ice  (Gagliardini et al. , 2013). Elmer/Ice is an open-source finite-element software for ice sheets, glaciers and ice flow modelling, which also includes a module for the rheology of compressible firn  (Gagliardini and Meyssonnier, 1997)  that has already been applied in several studies  (Zwinger et al. , 2007; Gilbert et al. , 2014; Licciulli et al. , 2019). All variables and parameters used in this study are summarized in Table  1.
2 m. The cave/firn interface is treated as a stress-free surface, which will closeoff over time due to the flow of firn. From a mechanical point of view, this is equivalent to state that the cave/firn interface has no rigidity at all. At each time step, the displacement δx k of a node k belonging to this interface is given by: δx k = u k δt x + w k δt z , ( 17 ) where u k and w k are, respectively, the horizontal and vertical components of the flow velocity computed at node k, and δt is the timestep size. To deal with these displacements, we use a module available in Elmer/Ice which displaces the nodes through a deformation of the whole mesh, with the constrain of keeping the nodes located on the lateral and bottom boundaries of the domain at fixed positions. However, a problem arises after a few tens of years of simulation, when the mesh is too deformed with elements overlapping each others.
The 21 st century marks the emergence of "big data" with a rapid increase in the availability of data sets with multiple measurements. In neuroscience, brain-imaging datasets are more commonly accompanied by dozens or even hundreds of phenotypic subject descriptors on the behavioral, neural, and genomic level. The complexity of such "big data" repositories offer new opportunities and pose new challenges for systems neuroscience. Canonical correlation analysis (CCA) is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities. Importantly, CCA is well suited to describing relationships across multiple sets of data and so is well suited to the analysis of big neuroscience datasets. Our primer discusses the rationale, promises, and pitfalls of CCA.
INTRODUCTION The parallel developments of large biomedical datasets and increasing computational power have opened new avenues with which to understand relationships among brain, cognition, and disease. Similar to the advent of microarrays in genetics, brain-imaging and extensive behavioral phenotyping yield datasets with tens of thousands of variables  (Efron, 2010). Since the beginning of the 21 st century, the improvements and availability of technologies, such as functional magnetic resonance imaging (fMRI), have made it more feasible to collect large neuroscience datasets  (Poldrack and Gorgolewski, 2014). At the same time, problems in reproducing the results of key studies in neuroscience and psychology have highlighted the importance of drawing robust conclusion based on large datasets  (Open Science Collaboration, 2015).
The UK Biobank, for example, is a prospective population study with 500,000 participants and comprehensive imaging data, genetic information, and environmental measures on mental disorders and other diseases  (Allen et al. , 2012; Miller et al. , 2016). Similarly, the Human Connectome Project  (van Essen et al. , 2013)  has recently completed brain-imaging of >1,000 young adults, with high spatial and temporal resolution, featuring approximately four hours of brain scanning per participant. Further, the Enhanced Nathan Kline Institute Rockland Sample  (Nooner et al. , 2012)  and the Cambridge Centre for Aging and Neuroscience  (Shafto et al. , 2014; Taylor et al. , 2017)  offer cross-sectional studies (n > 700) across the lifespan (18-87 years of age) in large population samples. By providing rich datasets that include measures of brain imaging, cognitive experiments, demographics, and neuropsychological assessments, such studies can help quantify developmental trajectories in cognition as well as brain structure and function.
While "deep" phenotyping and unprecedented sample sizes provide opportunities for more robust descriptions of subtle population variability, the abundance of measurement for each subject does not come without challenges. Modern datasets often provide more variables than observations of these variable sets  (Bzdok and Yeo, 2017; Smith and Nichols, 2018). In this situation, classical statistical approaches can often fail to fully capitalize on the potential of these data sets. For example, even with large samples the number of participants is often smaller than the number of brain locations that have been sampled in highresolution brain scans. On the other hand, in datasets with a particularly high number of participants, traditional statistical approaches will identify associations that are highly statistically significant but may only account for a small fraction of the variation in the data  (Miller et al.
, 2016; Smith and Nichols, 2018). In such scenarios, investigators who aim to exploit the full capacity of big data sets to reveal important relationships between brain, cognition and disease require techniques that are better suited to the nature of their data than are many of the traditional statistical tools. Canonical correlation analysis (CCA) is one tool that is useful in unlocking the complex relationships among many variables in large datasets. A key strength of CCA is that it can simultaneously evaluate two different sets of variables, without assuming any particular form of precedence or directionality (such as in partial least squares, cf. section 4. 2). For example, CCA allows a data matrix of brain measurements (e. g. , connectivity links between a set of brain regions) to be simultaneously analyzed with respect to a second data matrix of behavioral measurements (e.
, 2019; Liu and Calhoun, 2014; Marquand et al. , 2017; Smith et al. , 2015; Tsvetanov et al. , 2016; Vatansever et al. , 2017; Wang et al. , 2018a; Xia et al. , 2018). In this context our conceptual primer describes how CCA can deepen understanding in fields such as cognitive neuroscience that depend on uncovering patterns in complex multi-modal datasets. We consider the computational basis behind CCA and the circumstances in which it can be useful, by considering several recent applications of CCA in studies linking brain to behavior. Next, we consider the types of conclusions that can be drawn from applications of the CCA algorithm, with a focus on the scope and limitations of applying this technique. Finally, we provide a set of practical guidelines for the implementation of CCA in scientific investigations.
For example, the 'Big Five' describes a set of personality traits that are identified by latent patterns that are revealed when PCA is applied to how people describe other people's time-enduring behavioral tendencies  (Barrick and Mount, 1991). This approach tends to produce five reliable components that explain a substantial amount of meaningful variation in data gathered by personality assessments. A strength of a decomposition method such as PCA is that it can produce a parsimonious description of the original dataset by re-expressing it as a series of compact dimensional representations. These can also often be amenable to human interpretation (such as the concept of introversion). The ability to re-express the original data in a more compact form, therefore, has appeal both computationally and statistically (because it reduces the number of variables), and because it can also aid our interpretations of the problem space (as it did in the case of the 'Big Five' as main personality traits).
This coordinate system rotation is formally related to singular value decomposition (SVD). SVD is perhaps the most common means to compute CCA  (Healy, 1957). Assuming X and Y are centered, the CCA solution can be obtained by applying SVD to the correlation matrix (for detailed mathematical proof, see  Uurtio et al. , 2017). From a practical application perspective, there are three properties of CCA that are perhaps particularly relevant for gaining insight into the variable-rich datasets available to cognitive neuroscience: (1) joint-information compression, (2) multiplicity and (3) symmetry. We consider each of these properties in turn.
The algorithm seeks to re-express the datasets as multiple pairs of canonical variates that are highly correlated with each other across subjects. Each pair of the latent embedding of the left and right variable set is often referred to as 'mode'. (B) In each domain of data, the resulting canonical variate is composed of the weighted sum of variables by the canonical vector. (C) In a two-way CCA setting, each subject can thus be parsimoniously described two canonical variates per mode, which are maximally correlated as represented here on the scatter plot. The linear correspondence between these two canonical variates is the canonical correlation -a primary performance metric used in CCA modeling.
The use of orthogonality to constraint CCA modes is analogous to what happens using PCA. Consequently, the different modes produced by CCA are ordered by the total variation explained in the domain-domain associations. To the extent that the unfolding modes are scientifically meaningful, interpretations can afford complex data sets to be considered as being made up of multiple overlapping descriptions of the processes underlying question. For instance, much genetic variability in Europe can be jointly explained by orthogonal directions of variation along a north-south axis (i. e. , one mode of variation) and a west-east axis (i. e. , another mode of variation)  (Moreno-Estrada et al. , 2013). The ability for CCA to produce many pairs of canonical variates we refer to as 'multiplicity'. Figure  1  illustrates how the three core properties underlying CCA modeling and guidance of neuroscientific interpretation make it a particularly useful technique for the analysis of modern biomedical datasets -joint information compression, symmetry and multiplicity.
The resultant dimensions were relatively consistent with existing clinical diagnoses, but additionally cut across diagnostic boundaries to a significant degree. Furthermore, each of these dimensions were associated with a unique pattern of abnormal connectivity. However, a loss of network segregation was common to all dimensions, particularly between executive networks and the default mode network. As network segregation is a normative feature of network development, loss of network segregation across all dimensions suggests that common neurodevelopmental abnormalities may be important for a wide range of psychiatric symptoms. Taking advantage of CCA's ability to capture common sources of variation in more than one datasets, these findings support the idea behind NIMH Research Domain Criteria that specific circuitlevel abnormalities in the brain's functional network architecture may give rise to a diverse psychiatric symptoms  (Cuthbert and Insel, 2013).