dataesr/scientific-paragraphs-categorization huggingface dataset:
It is not clear which explanation is responsible for the discrepancies in this dataset, and as mentioned in the section Pigment and Packaging Indices, the a miss term solution of Bricaud et al. [53]  did not seem to be correct for our dataset. Nevertheless, the general trends in Q * a (regardless of the absolute magnitude) as a function of [Tchla] and cell volume are as expected. Very low a * φ (440) at chlorophyll a concentrations below 0. 3 mg m -3 were observed compared to previously described fits from temperate and tropical oceans [Fig. 10(a) ] and also the SO (i. e. , NOMAD). However, in the ACE dataset, although values of a * φ (440) measured on samples with > 0. 3 mg m -3 chlorophyll a still depart strongly from global surface waters values of B04 and B04+10  [53, 54, 64]  they more closely resembles values that include deeper waters from B98  [64]  and NOMAD SO data.
Chlorophyll-specific absorption at 440 nm is typically lower in microphytoplankton  [31]. This is clearly illustrated in this dataset which is dominated by microphytoplankton (Fig. 5 ). The a * φ (440) is also impacted by changes in the concentration of accessory pigments which overlap with chlorophyll a absorption at 440 nm. According to a * φ (440) vs. [Tchla] relationships parameterised in non-polar waters, a * φ (440) is typically much higher at low chlorophyll accounting for the predominance of picophytoplankton when chlorophyll is low in temperate and tropical waters. Pigment packaging at 440 nm does appear to explain the trends in a * φ (440) within the ACE dataset, similar to Q * a (676), Q * a (440) also decreases with [Tchla] and cell volume [Fig. 9 (b)], as has been observed by others  [18].
However, it does not appear to fully explain why a * φ (440) is so much lower than observations in temperate and tropical oceans and varies so little with [Tchla] compared to previously described trendlines. Unlike at 676 nm, pheopigments do not contribute substantially to absorption at 440 nm (Fig. S1  in Supplement 1), and so there is a smaller difference between Figs. 10(a  Figures  11(a ) and 11(b) show a * φ (440) and a * φ (676), respectively, as a function of the median light intensity within the mixed layer (I g ). Overall, there is very little change in the chlorophyllspecific absorption coefficient at 440 or 676 with increases in I g except at I g > 200. This suggests that variation in a * φ (440) and a * φ (676) in this dataset are due to changes in cell size and pigmentation driven by changes in taxa.
High photosynthetic and [Tchlc] together with low photoprotective and [Tchlb] pigments complement characterise the Southern Ocean in summer Bricaud and coauthors  [54]  (B10) surveyed the ultra-oligotrophic waters of the South-Pacific gyre and found that for low chlorophyll samples a φ (440) (and hence a * φ (440)) were 30%-40% lower than in other oceanic areas with the same chlorophyll. They attributed this to both a  decrease in the concentration of accessory pigments, specifically photoprotective carotenoids, relative to chlorophyll a ([PPC/Tchla], and an increase in relative cell size. In the ACE dataset, the relative contribution of two of the major pigment groups, [Tchlb] [Fig. 12(a) ] and [PPC] [Fig. 12(d) ] are generally lower (compare mauve and ACE points in Fig. 12 ) for the same [Tchla] than in other oceans.
HPLC) and definition of chlorophyll a, application of integrating spheres for measuring optical density, pathlength amplification factors for calculating a p and method for decomposing a p and a φ add uncertainties and variability to these measurements and relationships. We have limited these potential discrepancies in our comparison with the Bricaud datasets by: (1) running the HPLC samples in the same laboratory, (2) using both the [Tchla] and [Tchla + Pheoa] normalization approach, (3) verifying that the decomposition approach provided consistent results at 440 and 676 nm with the Bricaud and Stramski  [112]  approach used in some of campaigns of the Bricaud datasets. Both the inversion method presented here, and the Bricaud & Stramski approach produced reliable estimates of a φ as compared to a small number of values from ACE derived using the traditional methanol extraction method of Kishino et al.
13 ). To represent these unique relationships, different spectral models are needed. Power law functions between log-transformed a φ (λ) and log-transformed [Tchla] were fit at 2 nm wavelength intervals (see Data File 1, Data File 2, and Data File 3 and Supplement 1 for coefficients). The resulting phytoplankton absorption spectral shapes normalized to a φ (440) (Fig. 14 ), and their chlorophyll-specific equivalents (Fig. 15 ), show very different shapes and amplitudes compared to other oceanic regions. The equivalent shapes from the combined datasets of B04  [53]  and B10  [54]  are in mauve in Figs. 14(a)  and Fig. 15(a). Overall the spectral shapes are noticeably different from the B04+B10 shapes, but  In addition to the higher values above ∼ 500 nm reflecting the flatter, more packaged spectra, the most prominent feature in the ACE spectral shapes are a shoulder at 460 nm and between 550-600 nm which intensify at low chlorophyll a concentrations.
Conclusion Southern Ocean waters are optically unique  [116] , although much of the in situ bio-optical data collected in the SO so far has been spatially constrained. The ACE voyage gave us the opportunity to collect a unique dataset of phytoplankton particulate absorption, pigments and particle size across the entire longitudinal and latitudinal range of the SO. From this dataset, we confirm strikingly different relationships between chlorophyll-specific absorption and community composition and size when compared to other oceans (particularly in low wavelengths and at low chlorophyll a concentrations). In particular, we find high fractions of microplankton across all chlorophyll a concentrations and as a result, the phytoplankton absorption spectrum is flatter than other oceans at all chlorophyll a, and overall phytoplankton absorption and chlorophyll-specific absorption is lower than other oceans.
Constants and Exponents From the Power Law Relationships Between Chlorophyll a (either [Tchla] or [Tchla+Pheoa]) and a) a p (440), b) a φ (440), or c) a φ (676) from the ACE dataset (ACE), B98 [64], B04 [53], B10 [54], B04+B10 [53,54] Combined Datasets (our fit to combined original datasets; dashed red line), and Reynolds et al. [23] in the APFZ (RAPFZ) and Ross Sea (RRS). a a) Study a p (440)=Const[Tchla] exp a p (440)=Const[Tchla+Pheoa] exp ACE a p (440)=0. 043[Tchla].
Data availability. The datasets containing phytoplankton pigments can be found at Antoine et al. [51] , particulate absorption at Antoine et al. [52] , and photosynthetically active radiation at Thomalla et al. [48]. The dataset containing particle size distribution measurements will be made available at  [120]. Other datasets from the Antarctic Circumpolar Expedition including latitude, longitude, thermosalinograph data, CTD and XBT (Haumann et al. [43]  and Henry et al. [45] ) data can be found at  [120]. The power law relationships between phytoplankton absorption and chlorophyll a are available in Data File 1, Data File 2, and Data File 3.
The total linear thermal transmittance of the thermal bridges was calculated based on the sum-up of the linear thermal transmittance of the individual components, and the thermal transmittance of the stone wool construction, in accordance to Eq. (  5 ). ψ TS = ψ SWB + Δψ S  (5)  or by considering the linear thermal transmittance of the complex structure, in accordance to Eq. (  6 ). ψ TC = ψ SWB + Δψ C  (6)  The analytical solutions of the above set of equations for the investigated case studies were obtained with the use of the THERM  [12]  and the HEAT3  [13]  software. Particularly THERM was used for the two dimensional solutions whereas HEAT3 was applied for 3D problems. The caluclations were conducted in two steps: -By considering the installation of the window structure in a masonry layer, without the existence of an insulation element, using THERM -With the use of insulation materials and thermal fasteners, with the use of HEAT3.
Introduction Protein-tyrosine phosphatases (PTPs) represent the largest family of human phosphatases. 1 PTPs are signaling molecules that regulate a variety of cellular processes, including proliferation, differentiation, and oncogenic transformation. 2 Protein-tyrosine phosphatase, non-receptor-type, 4 (PTPN4 [MIM: 176878]) encodes a non-receptor tyrosine phosphatase that in neurons interacts with glutamate receptor delta 2 and epsilon subunits and mediates signaling downstream of the glutamate receptors through tyrosine dephosphorylation. 3 4] [5] [6] [7] [8]  Genome Aggregation Database (gno-mAD) data indicate that PTPN4 has a high probability of loss-of-function (LoF) intolerance with a loss-of-function observed/expected upper bound fraction (LOEUF) of 0. 28, indicating that no more than 28% of the expected loss-of-function variants were observed and therefore PTPN4 is likely under selection against loss-of-function variants.
9 ere, we describe six individuals with neurodevelopmental phenotypes and growth or morphological anomalies who carry rare, putatively deleterious sequence variants in PTPN4. Clinical data were obtained through written informed consent. In all cases, the procedures followed were in accordance with the ethical standards of the respective institution's committee on human research and were in keeping with national standards. PTPN4 sequence variants were identified in all of these individuals by clinically based exome sequencing (except subject who had a targeted gene panel) performed in CLIA-or ISO15189-certified laboratories and confirmed by Sanger sequencing. The clinical phenotypes of these individuals and a description of their PTPN4 sequence changesincluding in silico prediction of effects on protein function via PolyPhen-2,  9  SIFT,  10  and MutationTaster  11  and allele frequencies reported in the gnomAD, ClinVar, and Human Gene Mutation Database (HGMD)-are summarized in Table  S1.
Kinetics experiments Kinetics experiments were carried out on a BMG Pherastar FS (BMG Labtech). Absorbance values were measured at 405 nm at 25 C for each time point. Experiments were performed by mixing increasing concentrations of each of the PTPN4 variants, ranging from 100 nM top concentration with two-fold serial dilution versus a fixed concentration of pNPP (5 mM) in a 50 mL reaction volume, with a 10 mL 1 N NaOH stop reaction. The buffer was phosphate-buffered saline (PBS), 10 mM Na 2 HPO 4 , 1. 8 mM KH 2 PO 4 , 137 mM NaCl, 2. 7 mM KCl. Initial linear reaction rates were calculated during a 20-60 min reaction. The enzyme kinetics were deduced from fitting the Michaelis-Menten equation with the GraphPad Prism software. Circular dichroism 70 mg of WT and R808H PTPN4 were buffer exchanged into 20 mM sodium phosphate (pH 7.
5), 150 mM NaCl and concentrated to % 400 mL using Amicon centrifugal filters (Millipore). Note that TCEP was left out of the buffer to avoid any spectral artifacts due to oxidation of TCEP, which is unstable in phosphate buffers. The buffer-exchanged samples were spun at 10,000 rpm for 5 min at 4 C to pellet any aggregates. No pellet was observed. Circular dichroism measurements were taken using a Jasco J-815 spectrophotometer with an MPTC-490 6-cell 52 Peltier thermostatted cell holder. Spectra were recorded at 20 C from 190 to 250 nm at 0. 1 nm intervals using a 1 mm pathlength quartz cuvette. The samples were scanned at 50 nm/min with a 1 nm bandwidth and a 4 s data integration time (DIT) four times and plotted as an average of all four reads. A spectrum of buffer alone was subtracted from the data.
The data were fit to a Boltzmann sigmoid using Prism to extract the temperature of unfolding (Tm). Neuronal studies PTPN4, PTPN4 p. Gly239Arg, and PTPN4 p. Arg808His cloning into the expression vectors The synthetic human gene PTPN4 was assembled from synthetic oligonucleotides and/or PCR products by the GeneArt Gene Synthesis (Thermo Fisher Scientific) as reported previously. 12 The single-nucleotide substitution variants PTPN4mutG715A (amino acid sequence PTPN4 p. Gly239Arg) and PTPN4mutG2423A (amino acid sequence PTPN4 p. Arg808His) were ordered from the GeneArt Gene Synthesis (Thermo Fisher Scientific) as the variants of PTPN4 sequence. The mutated genes were inserted into pMA cloning vectors. The final constructs were verified by sequencing. The PTPN4, PTPN4 p. Gly239Arg, and PTPN4 p.
Imaging and quantification All images were acquired using confocal microscope LSM 700, Axio Imager Z2 (Zeiss), using EC Plan-Neofluar 403/1. 30 Oil DIC M27 and Plan-Apochromat 633/1. 40 Oil DIC M27 objectives and Zen software (Zeiss). The images were captured by snap as well as in a stack of nine 0. 3-mm-thick z sections (2. 7 mm total range) by frame scan mode. For the whole neuron visualization, the 403 snap images are presented with the 0. 83 zoom factor on frame size 1,024 3 1,024 (pixel size is 0. 20 mm). For dendrite images, the maximum intensity projection pictures of 633 flattened z stacks are presented with 0. 73 zoom factor on frame size 2,048 3 2,048 (pixel size equals 0. 07 mm). The images were analyzed using Zen Blue software (Zeiss). For quantification, the number of EGFP-expressing dendritic spines was counted on 50 mm length of three secondary dendrites from 14 neurons (n ¼ 42 dendrites per variant).
Primary dentition was extracted at age 3 years due to decay. A brain MRI scan at 3. 5 years of age revealed a large cavum septum pellucidum and vergae, a slight reduction in white matter around the posterior horns, and associated thinning of this region of the corpus callosum. He had an early working diagnosis of Sotos syndrome I (MIM: 117550). Nuclear-receptorbinding set domain protein 1 (NSD1 [MIM: 606681]) testing at age 4. 5 years identified neither a pathogenic variant nor copy-number variant (CNV) at this locus. Subject 5 is a 15-year-old female of Asian descent who carries a c. 2512 C>T (p. Arg838Ter) change in PTPN4. This variant was present in gnomAD in 1 person (1/ 251,432) and dbSNP (rs1259252500; frequency T ¼ 0. 000004, derived from gnomAD data above), but was absent from ClinVar.
Genomics England, https://www. genomicsengland. co. uk/ gnomAD, https://gnomad. broadinstitute. org/ Human Gene Mutation Database, http://www. hgmd. cf. ac. uk/ac/ index. php International Genome Sample Resource, https://www. internationalgenome. org/ National Center for Biotechnology Information, https://www. ncbi. nlm. nih. gov/gene/5775/ortholog/? scope¼89593#genes-tab NHLBI Exome Sequencing Project, https://evs. gs. washington. edu/ EVS/ OMIM, https://www. omim. org/ UCSC Genome Browser, https://genome. ucsc. edu/.
Acknowledgments We would like to thank the families who kindly consented to participate in our genetics study. We would also like to acknowledge  Erin Torti of GeneDx  for assistance in subject accrual and data retrieval. This work was supported by a  NCATS/NIH/Case Western Reserve Clinical and Translational Science Collaborative (CTSC)  Award (A. A. ), the  US National Institutes of Health  ( R01MH113106 , A. W. B. ), and  NCN  grants ( 2014/14/E/NZ3/ 00375 , M. D. ;  2017/27/N/NZ1/01381 , J. J. C. ). Subject 4 was ascertained through the  Deciphering Developmental Disorders (DDD) study. The DDD study presents independent research commissioned by the  Health Innovation Challenge Fund  (grant no. HICF-1009-003 ). This study makes use of DECIPHER (http:// www. deciphergenomics. org), which is funded by  Wellcome.
Experimental Design Three groups of four NHPs were included in the study of adaptive response. Randomization was performed based on their weight and MHC haplotypes. The experiments and data analysis were performed following protocols which were previously validated during in-house studies  (23). Animals were immunized two times with MVA at a 58-day interval. Blood samples were collected at baseline and at days 8, 14, 28, and 57 post-prime and days 8, 14, 28, 57, and 104 (month 4) post-boost three additional groups of six NHPs, of equivalent average weight and MHC profile, were included to study the MVA-induced innate response. We evaluated the systemic response with blood sampling at baseline and at 3 h, 6 h, and 24 h after MVA immunization. An additional group of five cynomolgus macaques was used for studying the injection site after IM administration of MVA.
Plates were washed five times, and 100 mL 3,3',5,5'-tetramethylbenzidine (Thermo Scientific) added and the plates incubated for 30 min at RT in the dark. The reaction was stopped by adding 2N H 2 SO 4. Absorbance was measured at 450 nm using a spectrophotometer (Multiskan FC, Thermo Scientific) and the data analyzed using SoftMax Pro software (version 4. 6; Molecular Devices). Ab titers were calculated as described  (23).
Flow-Cytometry Staining of Blood Samples 100 µL blood was stained for 30 min with 90 µL antibody mix in BD Horizon ® staining buffer (BD) containing the panel of antibodies listed in Table  S2  The red blood cells were removed with 1 mL BD FACs Lysing ® (BD) buffer for 10 min at RT and the remaining cells washed twice with PBS. The design of the cytometry panel and rationale for the gating strategies were mostly based on previous studies  (26). We standardized the flow cytometry results based on complete blood count data to retain the absolute values in the blood, obtaining the number of cells per liter. The CBC-based number of cells per liter was calculated using the following formula: count obtained in flow cytometry of a given cell population multiplied by the CBC number of leukocytes divided by the count of total blood cells obtained by flow cytometry.
Analysis of Transcriptomic Data Functional-enrichment analysis of the various lists of differentially expressed genes was performed using Ingenuity Pathway Analysis software (IPA ® , Qiagen, http://www. qiagen. com/ingenuity). Upstream regulators with a q-value < 10 -3 and belonging to the cytokine-signaling, humoral immune-response, or pathogeninfluenced signaling categories are represented with the exception of FSH, LH, and prolactin regulators. Canonical pathways with a qvalue < 10 -3 and belonging to the cellular immune-response, cytokine-signaling, humoral immune-response, or pathogeninfluenced signaling categories are represented. Raw and normalized microarray data are available in the ArrayExpress database  (27)  under accession number E-MTAB-10309.
Preprocessing of Mass-Cytometry Data FCS files were normalized separately for each administration route experiment with the MATLAB Compiler software normalizer (33) using the signal from the 4-Element EQ beads (Fluidigm). Replicates were concatenated using R software. CD4 + and CD8 + T lymphocytes were manually gated using Cytobank ® software (34) as illustrated in Figure  S1. Beads, doublets, and dead cells were removed. HLA-DR -and CD3 + cells were retained and considered to comprise mainly of T lymphocytes. CD4 + T cells were considered to be CD4 + and CD8 -whereas CD8 + T cells were considered to be CD4 -and CD8 +. The isolated CD4 + and CD8 + T cells were then analyzed separately using automatic gating tools  (30).
Identification of Cell Populations The Spanning-tree Progression Analysis of Density-normalized Events (SPADE) algorithm was used to identify CD4 and CD8 Tcell populations with similar marker intensities. SPADE was performed using the publicly available R/Bioconductor package  (35). Six separate SPADE analyses were performed, one for each of the following conditions: CD8 SC, CD8 ID, CD8 IM, CD4 SC, CD4 ID, and CD4 IM (Figure  S1 ). For each SPADE analysis, we first randomly selected and concatenated an equal number of cells corresponding to the smallest sample uniformly for each sample to reduce bias during the SPADE down-sampling step caused by large variances between the numbers of cell events per macaque. We then performed the SPADE density-based downsampling using a target of 30% of the preselected cells.
Gating Strategy of Blood Cells by Flow Cytometry and Analysis The gating strategy is provided on Figure  S3A. We optimized the panel using both NKG2a and CD33 markers on the same fluorochrome. Indeed, these markers are not expressed by the same cell populations (Figure  S3B ). First, doublets and debris were excluded. SSC-A + , CD66 + cells were considered to be granulocytes, which comprised mainly of neutrophils as demonstrated by the recruitment kinetic data derived from complete blood counts. However, we cannot exclude the presence of eosinophils and basophils in this gate. B lymphocytes were gated as Lin (CD3, CD20) + ,CD4 -, HLA-DR + cells; CD8 + T lymphocytes as Lin (CD3, CD20) + , HLA-DR -, CD8 + cells; CD4 + T lymphocytes as Lin (CD3, CD20) + ,CD4 -, HLA-DR -cells; and NK cells as Lin (CD3, CD20) -, CD16 + , CD8 + , NKG2a + cells.
MVA Immunization Through the ID/IM Route Favors Polyfunctional Specific CD8 + T Cells The MVA-specific CD8 + T-cell responses were characterized by the intracellular production of cytokines following PBMC stimulation ex vivo with MVA for 18 h. Assays were performed over the kinetics of immunization at baseline (BL), D8PP, D28PP, D8PB, and D28PB. Cells were stained with a panel of 29 antibodies coupled to heavy metals (Table  S1 ) and the data acquired by mass cytometry. The SPADE algorithm  (39, 40)  was used on gated CD8 + T cells to identify cell clusters based on the expression of 13 functional and homing markers; Bcl2, Granzyme B, CD62L, CD69, CD154, CD197, IFNg, IL-2, IL-17, Ki67, MIP1b, Perforin, and TNFa (Figure  S1 ). Due to different experiment timeframe and different antibody batches, one SPADE analysis per administration route was performed.
Furthermore, our results suggest that the time to reach the draining lymph node is dependent on the route of administration. Indeed, more abundant levels of E3L were detected in the subcapsular area of draining lymph nodes after SC injection, in accordance with literature on murine models  (63, 64) , whereas it was moderate after ID injection (Figure  S4 ). Thus, the antigen-presenting cells that are mobilized might differ depending on the administration route. The persistence of the antigen in the skin after ID immunization suggests the possible uptake by local DC subsets, such as Langerhans cells or dermal DCs  (57, 65). Despite the data integration, this study did not identify key immune parameters that triggered the difference between the administration routes. Several relevant immune parameters were not included in this study such as NKT cells or innate lymphoid cells due to the design of the gating strategies.
Further investigation is also needed to better understand why a given administration route induces more suitable immunity relative to the others. Such understanding will require identification of the key elements that drive the relationship between innate and adaptive responses. The use of systems vaccinology approaches combined with mathematical modeling to identify predictive biomarkers of adaptive immunity appear essential to harness and take advantage of these immune processes. (IDMIT, Fontenay-aux-Roses, France). Funds were also received from the European Commission: ADITEC, FP7-HEALTH-2011-280873; Transvac, EU H2020 GA 730964; EHVA, EU H2020 GA 681032.
FUNDING This work was supported by the IDMIT infrastructure and funded by the  ANR  via grant No  ANR-11-INBS-0008. N. T. held a fellowship from the  ANRS (France Recherche Nord&Sud Sida-HIV Heṕatites). This work was also supported by the " Investissements d'Avenir " programs managed by the  ANR  under reference  ANR-10-LABX-77-01  funding the  Vaccine Research Institute (VRI) ,  Creteil  ( ImMemory research program ) and  ANR-10-EQPX-02-01  funding the  FlowCyTech  facility.