dataesr/scientific-paragraphs-categorization huggingface dataset:
| Genetic testing Thirteen sporadic and 14 familial cases were noted. Inheritance mode was in favour of an autosomal dominant (AD) way in 13 cases, and an autosomal recessive (AR) one in 1 case. By screening the 36 genes known to be involved in both IPN and HL, pathogenic variants were identified in 16 patients out of 27 (59. 26%): 1 ). PMP22 (n = 5), SH3TC2 (n = 4), MPZ (n = 2), NEFL (n = 2), PRPS1 (n = 1), TRPV4 (n = 1), ABHD12 (n = 1) (Figure The already known variants were: PMP22 duplication of 1. 5Mb in three cases, PMP22 deletion of 1. As a consequence, seven novel variants, that could be classified as pathogenic or probably pathogenic, were discovered in five different genes PRPS1, MPZ, SH3TC2, NEFL and ABHD12. These variants were all absent from the different databases, and in silico studies were in favour of pathogenic variants (Tables  3  and 4 ).
Example 2. Extending Example 1, letting ρ ≡ ρ sq FinGraph , note first that by definition for all (isomorphism classes of) finite multigraphs G ∈ obj(FinGraph)∼ = , |G = ρ(δ (G ← ∅ → ∅)) |∅. With D := ρ(δ (∅ ← ∅ → )) , X := ρ(δ ( ← ∅ → ∅)) , |n := • n (n ≥ 0) , (17) as a consequence of (  14 ) of Example 1 one may verify that D |0 = 0 FinGraph , D |n = n |n -1 (n > 0) , X |n = |n + 1. (18) In other words, the data of (  17 ) and (  18 ) furnishes a representation of the famous Heisenberg-Weyl algebra that is of fundamental importance in combinatorics and physics (see e. g. [9, 10, 11] ). An alternative such representation is given by the linear operators x (multiplication by x) and ∂ x (derivation by x) acting on the R-vector space spanned by monomials x n , which reproduces equations isomorphic to (  17 ) and (  18 ), with ∂ x x n = nx n-1 and xx n = x n+1.
( 40 ) Together with an initial condition such as e. g. |Ψ(0) = |G 0 for some (finite) directed graph G 0 with N V vertices and N E edges, and computing the closed-form expression for the first contribution in (40) from our previous solution (37) (as ∂ λ (∂ λ -1)M V (t; λ ) followed by setting λ → 0), the initial value problem for the mean edge count evolution may be easily solved in closed form via the use of a computer algebra software such as MAPLE, MATHEMATICA or SAGE. It is also straightforward to verify that for an arbitrary initial state |Ψ(0) = |G 0 , the limit value of the mean edge count for t → ∞ reads lim t→∞ | O E |Ψ(t) = ν 2 + ε + ν 2 -(2ν -+ε -). (41) Since the rates ν ± and ε ± are free parameters, the above result entails that in this model one may freely adjust the limit value of the average vertex count as encoded in (36) (whence ν + /ν -) as well as the limit value of the average edge count via suitable choices of the parameters ε ±.
G93 samples are selected from the Lyon-Meudon Extragalactic Database  (Paturel et al. 1988 ) with cut-offs of B-band apparent magnitude of 14. 0 and V CMB (recession velocity) ≤5500 km s -1. The NGC 5135 galaxy group was then re-grouped as LGG 351 in the G93 catalogue. LGG 351 consists of 16 galaxies, with 6 galaxies overlapping with the KF89 identification. NGC 5126 is not listed in LGG 351 but a relatively small and nearby galaxy, PGC 46903, is included. Since the B-band magnitude of PGC 46903 is fainter than 14. 0, it is likely a misidentification of NGC 5126 in G93. We also find that PGC 47574 is a duplicate of PGC 47573 for the given coordinates. In this paper, we consider NGC 5126 instead of PGC 46903 as part of LGG 351 and exclude PGC 47574. We refer readers to G93 for a detailed description of the sample and membership selection criteria.
SOFIA generates outputs of individual cubelets and associated mask cubes for detected sources. For each source, a spectrum, integrated H I intensity (0th moment), velocity field (1st moment), velocity dispersion (2nd moment), and position-frequency maps are also generated based on the mask cube. To verify the detected sources, we follow these steps: (i) Overplot the source positions on to images of the same region from the NVSS and the SUMSS to identify incomplete continuum subtraction. It was a known issue with this version of ASKAPSOFT that bright continuum sources ( 0. 5 Jy) are unlikely to be subtracted completely. (ii) Cross match with the NED and SIMBAD databases  (Wenger et al. 2000). The search radius is first set to 45 arcsec and then 60 arcsec if no source is found within 45 arcsec (∼1 synthesized beam size).
Analysis of HIPASS data shows no H I detection and gives an upper limit of M HI < 8. 5 × 10 6 M to these dwarf galaxy candidates  (Müller et al. 2017 ). Nevertheless, we search for an H I signature of some of these ultra-faint dwarf galaxies given that they fall within the studied area. We find a detection of H I for dw 1328-29 (#11), which has M HI of 3. 4 × 10 8 M and is at the low end of H I masses of galaxies within LGG 351 (see Fig. 12 ). This also rules out dw 1328-29 as a member of the M 83 group. We also identify LEDA 98614 (#14) as a dwarf candidate based on the combined g and r-band DECam image.
(2012)  created the GALEX Source Catalog in the Kepler Field (GCAT 7  ), in which specific apertures were used to obtain fluxes for the identified extended sources. We obtain the GALEX NUV AB magnitudes by matching our galaxies' coordinates to the GCAT via the GALEX CasJobs interface. GCAT provides an homogeneous photometry, which includes data from the GALEX Medium Imaging Survey (MIS), the All-Sky Imaging Survey (AIS), and the Deep Imaging Survey (DIS) through GR7. We adopt the MIS over AIS magnitudes whenever available. The associated magnitude uncertainty is ∼0. 15 mag  (Seibert et al. 2012). We follow the same procedure as described in Section 5. 2. 1 to correct NUV AB magnitudes for the Galactic dust extinction, where A NUV = 8. 2 × E(B -V)  (Wyder et al. 2007 ) is adopted. The intrinsic monochromatic flux F ν,mono in units of erg s -1 cm -2 Hz -1 is calculated with M intrinsic (AB mag) = -2.
To investigate if our galaxies are actively forming stars, we compare SFRs with the fitted SFR-M * relations derived from a sample of low surface brightness galaxies (LSBG; McGaugh, Schombert & Lelli 2017) and star-forming galaxies in xCOLD GASS (extended CO Legacy Database for GASS;  Saintonge et al. 2017). This relation forms the star formation main sequence (SFMS), where galaxies are undergoing exceptionally high star formation if lie above the SFMS and are quenched if lie below the SFMS. In Fig. 16 , we overplot our data (filled orange and green squares), the LSBG data (blue dots) and the xCOLD GASS data (grey and black dots) on to the SFR-M * plane. The filled orange and green squares represent the total SFR and the lower limit, respectively. Errors in total SFR are relatively small compared with the size of the symbol.
AC K N OW L E D G E M E N T S This research was supported by the  Australian Research Council   Centre of Excellence for All Sky Astrophysics in 3 Dimensions  ( AS-TRO 3D ), through project number  CE170100013. The Australian SKA Pathfinder is part of the  Australia Telescope National Facility  that is funded by the  Australian Government  with support from the  National Collaborative Research Infrastructure Strategy and Industry Endowment Fund. ASKAP uses the resources of the  Pawsey Supercomputing Centre  with funding provided by the  Australian Government  under the  National Computational Merit Allocation Scheme  (project  JA3 ) We acknowledge the  Wajarri Yamatji  as the traditional owners of the  Murchison Radio Observatory (MRO)  site and thank the operational staff onsite. This research has made use of the NASA/IPAC Extragalactic Database (NED) that is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration.
Results We identified more than 3000 proteins and phosphosites, from which we quantified more than 1000 proteins and 500 phosphosites after stringent filtering. Extensive exploration of this proteomics and phosphoproteomics dataset allowed characterizing housekeeping as well as cell-line specific proteins, phosphosites and functional features of each cell line. In addition, by comparing the sensitive and resistant cell lines, we identified protein and phosphosites differentially expressed in the resistance context. Further data integration in a molecular network highlighted the differentially expressed pathways, in particular migration and invasion, RNA splicing, DNA damage repair response and transcription regulation.
In particular, a recent study integrating phosphoproteomics with transcriptomics and genomics data revealed the diversity of activated signaling pathways in metastatic PC patients, in relation to their resistance to the anti-androgen therapy  [18]. This work further demonstrated the utility of combining -omics approaches to better understand PC and CRPC progression. Here, we used a SILAC-based Mass Spectrometry approach, and identified and quantified the proteomes and phosphoproteomes of four widely used prostate cell lines representative of different cancerous and hormonal status. We first identified a common set of housekeeping proteins highly expressed in all cell lines, and enriched in biological processes related to RNA metabolism and oxidative stress. We further detected that each cell line possesses specific protein, phosphosite and functional features, in particular related to cellular metabolism, transport and protein localization.
99, -32. 66, and -24. 49 Thompson relative to the precursor ions) with collision induced dissociation (AGC target 3000, NCE 35%, maximum IT 200 ms). The Mass Spectrometry proteomics data, including search result, have been deposited to the ProteomeXchange Consortium (www. proteomexchange. org) [22]  via the PRIDE partner repository with datasets identifiers PXD004970 and PXD004992.
Protein identification and quantification Relative intensity-based SILAC quantification was processed using MaxQuant computational proteomics platform, version 1. 3. 0. 5  [23]. First the acquired raw LC Orbitrap MS data were processed using the integrated Andromeda search engine  [24]. Spectra were searched against a SwissProt human database (version 2014. 02; 20284 entries). This database was supplemented with a set of 245 frequently observed contaminants. The following parameters were used for searches: (i) trypsin allowing cleavage before proline  [25] ; two missed cleavages were allowed; (ii) monoisotopic precursor tolerance of 20 ppm in the first search used for recalibration, followed by 6 ppm for the main search and 20 ppm for fragment ions from MS/MS; (iii) cysteine carbamidomethylation (+57.
02146 Da) as a fixed modification and methionine oxidation (+15. 99491 Da) and N-terminal acetylation (+42. 0106 Da) as variable modifications; (iv) a maximum of five modifications per peptide allowed; and (v) minimum peptide length was 7 amino acids. The re-quantify option was enabled to search for missing SILAC partners. The quantification was performed using a minimum ratio count of 2 (unique+razor) and the second peptide option to allow identification of two co-fragmented co-eluting peptides with similar masses. The false discovery rate (FDR) at the peptide level and protein level were set to 1% and determined by searching a reverse database. For protein grouping, all proteins that cannot be distinguished based on their identified peptides were assembled into a single entry according to the MaxQuant rules.
Preliminary treatment of the datasets Statistical analyses were done with the Perseus program (version 1. 3. 0. 5; freely available at www. maxquant. org) from the MaxQuant environment  [26]. The relative intensity-based SILAC ratio, iBAQ normalised intensities and peptide intensities were uploaded from the pro-teinGroups. txt and Phospho(STY)Sites. txt files for proteome and phosphoproteome studies, respectively. Proteins marked as contaminant, reverse hits, and "only identified by site" were discarded. One DU145 cell line replicate in the phosphoproteome study was discarded due to high divergence. In all other cases, for each experiment and for each cell line, the measurements of three replicates were considered.
Data analyses R statistical programming environment  [27]  was used for the treatment of the proteomic and phosphoproteomic datasets. Expression ratios towards the internal standard were base-2 logarithmized and normalized using z-scores. Clustering. Unsupervised hierarchical clustering using average method was performed for the proteomic and phosphoproteomic datasets based on Euclidean distances of the expression ratio after normalization. Identification of the highly-expressed housekeeping proteome. The abundance of each protein in each cell line was computed as the sum of the IBAQ values of every replicate. The housekeeping proteome was obtained by selecting the 10% most abundant proteins matching across all cell lines. Identification of differentially expressed proteins and phosphosites.
Pathway and biological process analyses Functional enrichments. Enrichment Analyses were conducted with G:Profiler  [29] , and the significance threshold set to 0. 01 after FDR multiple testing corrections. The list of 1229 proteins used for quantification analyses was used as statistical background. Additionally, the strong filter option was selected on G:Profiler to display solely the most significant ontology in each ontological group, and reduce annotation redundancy. ROMA. ROMA (Representation and Quantification of Module Activity) is a software focused on the quantification and representation of biological module activity using expression data  [30]. The reference gene sets used for this analysis were selected from pathway databases including Reactome  [31]  and HALLMARK  [32]. For each of these pathways, a score corresponding to the weighted sum of the protein expression was computed.
The weights are based on the first principal component (PC1). ROMA quantifies the statistical significance of the amount of variance explained by the PC1, and is referred to as the gene set overdispersion. Overdispersed pathways are selected based on a p-value set to 0. 01, and the resulting list of pathways can be interpreted as the pathways that contribute significantly to the total expression variance. A detailed presentation of the computational method and use of software can be found at  [30]. For this study, we applied on the proteomic dataset an R implementation of ROMA (https://github. com/Albluca/rRoma), which is an improved version of the initial software. The results are presented as a heatmap where the mean value of the scores was computed by types of cancer cell lines: CS for castration-sensitive and CR for castration-resistant, and scaled between -1 and 1.
Ingenuity Pathway Analysis (IPA). Proteomic datasets were also analyzed with Ingenuity Pathway Analysis (IPA) software (Qiagen, http://www. ingenuity. com/) to predict pathway activation or inhibition. The IPA knowledgebase, derived from literature, compute a score based on one-tailed Fisher test. The final score corresponds to the negative log of p-value, and thresholds were set to 0. 01. Kinase-Substrate Enrichment Analysis (KSEA). In order to use the KSEA App (https:// casecpb. shinyapps. io/ksea/) [33]  on the phosphoproteomic datasets, we computed the foldchanges (FC) between DU145 and LNCaP, and between PC3 and LNCaP, using the mean raw expression values of the replicates. We selected the sites where the expression values are over or under-expressed in both CR cell lines in comparison with LNCaP.
The most highly expressed proteins include the core histones, tubulins as well as heat shock proteins. Both the most abundant proteins detected as well as the lowest ones have been previously reported in other studies with a similar approach  [43]. We kept for further analysis only those proteins containing at least two valid quantification values over the 3 replicates in each cell line. Doing so, we used for subsequent analyses the quantitative expression data of 1229 proteins (S1 Table ). A similar strategy was used to identify and quantify phosphopeptides (Materials and methods). We identified 3746 phosphosites, of which 563 were kept for expression analysis considering the strong filters we defined (S2 Table ). These 563 phosphosites correspond to 381 proteins. Overall, 135 proteins were associated with quantitative expression values both at the proteomic and phosphoproteomic levels, with a correlation ranging from 0.
Proteome and phosphoproteome integration in a molecular network We finally sought to provide a larger-scale functional interpretation of resistance-associated candidate biomarkers. The separated analysis of the proteomics and phosphoproteomics datasets provided one-dimensional views of cellular processes. We expect to obtain a comprehensive perspective of cellular processes and their interplays by integrating the information about protein abundances, activation status and molecular interactions  [68, 69]. Toward this goal, we devised a network-guided integration of CS and CRPC cell lines proteome and phosphoproteome, by mapping the candidate biomarkers to molecular complex interaction data (Materials and methods). The resulting network is composed of 356 nodes and 1161 edges, including a large connected component encompassing 194 nodes and 1098 edges (Fig  4 ).
We next focused on a small subnetwork composed of proteins underexpressed in the CR context. It contains prohibitin (PHB), a putative tumor suppressor protein involved in the inhibition of DNA synthesis and regulating proliferation, and prohibitin-2 (PHB2), a mediator of transcriptional repression by nuclear receptors, also potentially involved in mitochondrial respiration. Indeed, the subnetwork also contains the VDAC1 mitochondrial membrane and plasma membrane channel, involved in apoptosis. The role of this subnetwork is unclear, but the proteins are depicted as members of the same complexes in the Hu. map dataset  [35]. The subnetwork is tightly linked to another subnetwork containing many mitochondrial membrane ATP synthase proteins (e. g. , ATP5F1, ATP5B, ATP5H), also downregulated in CR cell lines.
Discussion We here generated and explored a SILAC proteomics and phosphoproteomics dataset of prostate cell lines. We selected the PNT1A, LNCaP, DU145 and PC3 cell lines first because they are frequently used in PC research  [97, 98] , and molecular profiling and comparisons would thereby be highly valuable for researchers using them in routine. In addition, the elected cell lines are representative of normal, cancerous and CR prostate tissue, and therefore reflect progression of the disease. We decided to monitor proteome and phosphoproteome jointly as they can give a complementary picture of the molecular dynamics of the cells. Phosphoproteome characterization provides insights into proteins' phosphorylation levels, which are not strictly correlated to proteins level of expression, but also reflect protein activity status  [99].
Acknowledgments The authors thank  Christine Brun ,  Andreas Zanzoni  and all the partners of the  Hsp27BioSys  project for fruitful discussion. The differential proteomic analyses were done using the  Mass Spectrometry facility of Marseille Proteomics  (http://marseille-proteomique. univ-amu. fr/)supported by  IBISA (Infrastructures Biologie Santeet Agronomie) , the  Canceropole PACA , the  Provence-Alpes-Cote d'Azur Region , the  Institut Paoli-Calmettes  and the  Centre de Recherche en Cancerologie de Marseille.
Photophysical and structural characterization of InP QDs Absorbance measurements were performed using Hewlett Packard 8452A spectrophotometer whereas the emission spectra were recorded using a Fluorolog FL3-22 spectrometer from Horiba-Jobin Yvon equipped with a 150 W xenon lamp for steady-state measurements and a NanoLED laser diode from Horiba with a wavelength of 350 nm and 1 MHz repetition for time-resolved measurements. Decay curves were fitted using Decay Analysis software from Horiba Scientist. Photoluminescence quantum yield measurements were performed at room temperature using an integration sphere, Hamamatsu Quantaurus Absolute PL quantum yield spectrometer or by relative fluorescence measurements using Fluorescein 27 in 0. 1 M sodium hydroxide (NaOH) (QY = 93%) as standard. The hydrodynamic diameter of the waterdispersible InZnP and InZnPS QDs was measured by dynamic light scattering (Malvern Zeta Sizer NanoZS).
Spectra were recorded at 15-20 K using a liquid He cryostat. The beamline is equipped with a Si(220) monochromator  (Proux et al. , 2006). Spectra were recorded in fluorescence mode using a 30element Ge detector. Three to seven spectra of 40 min each were averaged, depending on the signal. Data were normalized and treated using the Demeter software package  (Ravel and Newville, 2005). Spectra for the pristine QDs were treated by shell fitting using ARTEMIS software  (Ravel and Newville, 2005). In-O, In-P and In-S paths were calculated using FEFF code, based on the structure of In phosphate dehydrate  (Sugiyama et al. , 1999) , InP and InS  (Wyckoff, 1963) , respectively. Spectra for the aged QDs and cells exposed to pristine and aged QDs were treated by linear combination fitting (LCF) using Demeter/ATHENA  (Ravel and Newville, 2005)  using the pristine QDs, In acetate and In phosphate as reference spectra.
, 2016). As positive control for alkaline comet assay, cells were exposed to 100 μM of methane methyl sulfonate (MMS) for 24 h. As positive control for Fpg-modified comet assays cells were exposed to 1 μM of riboflavin for 20 min, followed by irradiation with UVA (10 J/cm 2 ). Comets were scored using image analysis Comet IV software (Perceptive Instruments, Suffolk, UK), and median % DNA in tail was calculated for at least 50 comets per slide. Net Fpg sensitive sites (Net Fpg) were calculated as the difference in % DNA in tail between samples with Fpg incubation and samples with buffer incubation ((% DNA in tail from alkaline assay) -(% DNA in tail from Fpg-modified assay)). The whole experiment was repeated three to five times independently, on keratinocytes from different donors (n = 3-5).
Acknowledgements The authors thank  ESRF  and  CRG  committees for the provision of beamtime on FAME beamline ( BM30B ), and  R. Soulas  for imaging on TEM OSIRIS, supported and managed by  Agence National de la Recherche (ANR) ,  program 'Investissements d'Avenir' , reference  ANR-10-EQPX-39.
Funding This work is a contribution to the  Labex Serenade  ( ANR-11-LABX-0064 ) funded by the  French Government 's  "Investissements d'Avenir" ANR program , through the A* MIDEX  project ( ANR-11-IDEX-0001-02 ). It was supported by the  French National Research agency ANR  (grant  NEUTRINOS ,  ANR-16-CE09-0015-03 ),  CNRS  and  Communauté Université Grenoble Alpes  ( ComUE ,  PEPS  project  SABYDE ), and the  French Environment and Energy Management Agency (ADEME). This work used the platforms of the Grenoble Instruct-ERIC Center (ISBG:  UMS 3518 CNRS-CEA-UGA-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB). The electron microscope facility is supported by the  Auvergne Rhône-Alpes Region , the  Fondation Recherche Medicale (FRM) , the fonds  FEDER  and the  GIS-Infrastrutures en Biologie Sante et Agronomie (IBISA).
Methodology/Principal findings A smartphone was equipped with a USB Video Class (UVC) microscope attachment and Kankanet, which was trained to recognize eggs of Ascaris lumbricoides, Trichuris trichiura, and hookworm using a dataset of 2,078 images. It was evaluated for interpretive accuracy based on 185 new images. Fecal samples were processed using Kato-Katz (KK), spontaneous sedimentation technique in tube (SSTT), and Merthiolate-Iodine-Formaldehyde (MIF) techniques. UVC imaging and ANN interpretation of these slides was compared to parasitologist interpretation of standard microscopy. Relative to a gold standard defined as any positive result from parasitologist reading of KK, SSTT, and MIF preparations through standard microscopy, parasitologists reading UVC imaging of SSTT achieved a comparable sensitivity (82.