dataesr/scientific-paragraphs-categorization huggingface dataset:
The penetration depth is between 0. 5 and 1 mm. The PeriScan PIM 3 System automatically scans a defined skin surface area (4 milliseconds per pixel, resolution of 255 × 255 pixels). The data are computed and visualized as a twodimensional colour-coded image, mapping skin perfusion. The mean blood flow is computed to yield an average of pixels in a region of interest within the scanned area with the software provided by the manufacturer (LDPIwin software). The total time required for a complete image at these settings is approximately 5 minutes. The scanning Laser Doppler provides a skin perfusion mapping and the LDPIWin software led us to measure skin perfusion, expressed in Perfusion Units (PU), in the same area that we quantified the mottling score.
Moreover, perfusion indices were calculated by the laser Doppler imager software and the physician who analyzed the data was blind to the mottling score and therefore was not influenced by the result of the clinical observation. Skin temperature was not measured in the study, and we cannot exclude variations of skin temperature between T1 and T2 that could affect microvascular skin blood flow. Finally, we explored global microvascular blood flow, but we did not analyze the precise mechanisms that lead to skin perfusion changes. Several intricate mechanisms, such as intravascular coagulation, increased leucocyte adhesion, or vascular tone modifications  [18] , potentially participated to the changes of microvascular perfusion.
). Immunoreactive bands were visualized by Immobilon Western HRP substrate luminol reagent (Millipore Corp. , Billerica) using a Fujifilm LAS3000 imaging system. Electron Microscopy and Quantitative Analysis-1-l aliquots of Tau monomer, LMW aggregates, and fibrils were adsorbed onto 200 mesh formvar/carbon-coated nickel grids until dry. The grids were washed with water and stained with 2% uranyl acetate. Images were examined and captured on a Phillips CM 12 microscope operated at 65 kV. Size of Tau aggregates was measured and analyzed using ImageJ software (NIH)  (32). For ultrathin cryosectioning and immunogold labeling, HeLa cells treated with Tau aggregates were fixed with a mixture of 2% (w/v) paraformaldehyde and 0. 125% (w/v) glutaraldehyde in 0. 1 M phosphate buffer (pH 7. 4). Cell pellets were washed with phosphate buffer, embedded in 10% (w/v) gelatin, and infused in 2.
Peter Davies) overnight at 4 °C. Tissues were washed, incubated in HRP polymer conjugate for 10 min, and visualized using 3,3Ј-diaminobenzidine substrate. Images were collected on an Olympus light microscope. Five images were processed and quantified using ImageJ software (NIH). Confocal Microscopy and Co-localization Analysis-Fluorescence signals were captured on a Carl Zeiss LSM710 laser scanning confocal microscope with a ϫ63 oil immersion (1. 4 NA) objective. Tile (mosaic) and sequential scans were used to cap-ture large fields of the MFs and to maximize signal separation. All images were taken at the same laser intensity, detector gain, etc. Additionally, to reduce the impact of background intensity differences, pixel values for all images were normalized and cleaned by background noise remover.
7, C-E ). Our data thus show that LMW aggregates formed from full-length wild type protein can be internalized by endocytosis at axonal or somatodendritic compartments and can be transported both retrogradely and anterogradely. Tau Aggregates Bound to HeLa Cells and Were Internalized through Endocytosis-Results from neurons were confirmed and extended by studies in HeLa cells. Unlike primary neurons, HeLa cells can be subjected to trypsin digestion to remove surface-bound Tau aggregates and re-plated, which allows for a wider range of experiments to be undertaken. HeLa cells were treated with different Tau aggregates for 12 h, immunostained, and examined by confocal microscopy. Consistent with results from neurons, only aggregated Tau, including LMW aggregates, fibrils, and filaments readily bound to cells (Fig.
At 72 h post treatment, RFP translocation as a marker for HCV infection and GFP fluorescence intensity as a marker for gt1 RNA replication were measured. Additionally, the total cell number, which serves as an indicator of cytotoxicity, was determined. This phenotypic strategy supports multi-parameter data acquisition from a single well to access cross-genotypic activity, provides an indication of the stage of the viral life cycle targeted, and also assesses compound cytotoxicity. Taken together, this multiplex phenotypic platform facilitates the identification of novel compounds for drug development and chemical probes for continuing efforts to understand the HCV life cycle. Ó 2013 The Authors. Published by Elsevier B. V. Hepatitis C virus (HCV) is a major cause of chronic liver disease affecting 3% of the world's population  (WHO, 2012).
Acknowledgements This work was supported by the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MEST /No. 2011-00244 ),  Gyeonggi-do and KISTI. C. T. J. and C. M. R. were supported by grants from the  NIH  ( CA057973  and  DK085713 ), the  Starr Foundation  and the  Greenberg Medical Research Institute.
The expected ( H e ) and observed heterozygosity ( H o ) were evaluated for C. reticulata , C. maxima , C. medica , the Citrus genus, and the "true citrus fruit trees" excluding the Citrus genus. Data analysis was conducted with PowerMarker version 3. 25  ( Liu and Muse, 2005 )  and DARwin  ( Perrier and Jacquemoud-Collet, 2006 )  software. The missing data rate was very low in Citrus (0. 9%) and, generally, in the "true citrus fruit trees" group (0. 6%, excluding the Citrus genus). The missing data rate increased to 6. 5% and 6. 7% in the close citrus and primitive citrus groups of the Citrinae subtribe, respectively, reaching a level of 9. 8% and 22. 4% for the two other subtribes of the Citreae tribe, the Triphasilinae and the Balsamocitrinae, respectively. Missing data reached 26. 8% in the Clauseniae tribe.
The resulting data were evaluated against the 3-D Monte Carlo reference solution. Contrary to efforts comparing model simulations against in situ observations at specific test sites, the RAMI4PILPS approach eliminates uncertainties arising from an incomplete or erroneous knowledge of (1) the structural, spectral, and illumination related characteristics of the canopy target, and (2) the uncertainties introduced into the reference solution by calibration, sampling and upscaling errors (Fig. 2 ). The complexity of the RAMI4PILPS scenes was adapted to the typical capability of available shortwave RT formulations. More specifically, RAMI4PILPS proposed two homogeneous plant canopy types, i. e. , grasslands (GRA) and closed forest canopy (CFC) scenes that differ only in their predominant leaf orientations as well as in the height of the canopy.
The minimum of these represents the net photosynthesis rate, which was summed across all layers to provide the net photosynthesis rate [µmol C m -2 s -1 ] for a given virtual experiment. Absolute differences between net photosynthesis rates between the RAMI4PILPS scenes and each of the DGVMs were compared. The Farquhar scheme was parameterized according to  Sharkey et al. (2007)  which currently provides the most comprehensive analysis of the variables involved. Estimates of the maximum rates of Rubisco carboxylase activity (V c max ) and the maximum rates of photosynthetic electron transport (J max ), were however taken to represent the mean observed values at a reference temperature of 25 • C for tropical broadleaved evergreen forests and boreal (needleleaved) evergreen forests, according to the TRY database  (Kattge et al.
Canopy absorbed radiation The investigated models generally underestimate the radiation absorbed in the canopy for the RAMI4PILPS experiments analyzed (Fig. 3 ). Especially for the open canopy cases (SHR,OFC), a strong underestimation of faPAR is observed with increasing negative bias for increasing solar zenith angle. The 1-D-canopy RT models without clumping (JULES, JSBACH-no-clumping) perform best for the closed canopy cases (CFC,GRA), as expected. Some positive biases are observed for the isotropic (ISO) illumination conditions for JULES and JSBACH-no-clumping. In general, results of these models are almost identical due to the same basis for the canopy RT simulation code as discussed in Sect. 2. 3.
, 2001; Pinty, 2004; Ni-Meister et al. , 2010; Haverd et al. , 2012). New globally available dataset of effective RT model parameters as derived from satellite observations  (Pinty et al. , 2011)  could facilitate RT model parametrizations of DGVMs for different plant functional types. Given the fact that it has been shown that 1-D models can provide appropriate RT fluxes when parametrized in the right way, these novel dataset provide a novel source for the re-parameterization of canopy RT models at the global scale. -As an alternative, effective vegetation model parameters or structural parameters may be obtained through re-calibration using global satellite observations. Global satellite observations provide longterm and high resolution estimates of surface variables, like surface albedo, faPAR and canopy radiation flux partitioning  (Tucker et al.
calculated for each combination of PFT fraction f pft and Γ ↑ a and Γ ↓ a. Table 2 provides global mean estimates of the one-way atmospheric transmissivity (Γ ↓ ) as well as global means of R ↓ TOA and R ↓ surf based on various data sources over land areas. The one-way atmospheric transmissivity ranges from 0. 41 ≤ Γ ↓ ≤ 0. 58, dependent on the dataset.
[W m -2 ]. Thus a change of surface albedo of e. g. ∆α = 0. 04 would correspond to a radiative forcing of -0. 22 (-0. 52) [W m -2 ]. The use of different global radiation datasets as input has a minor impact on the radiative forcing estimates. The standard deviation of RF caused by different radiation data is 0. 3 ≤ σ ≤ 0. 45 (1. 03 ≤ σ ≤ 1. 41).
g. different definitions of geophysical variables among different products as well as between a DGVM and an EO based observational dataset. Unless the canopy RT schemes in DGVMs do not satisfy basic principles of energy conservation and the appropriate representation of canopy scattering processes, an assimilation of EO based surface albedo or faPAR products in DGVMs might lead to erroneous results. As an example, faPAR satellite products show a wide range of variability due to different definitions and algorithms applied for the retrieval. A comprehensive review is given by Gobron and Verstraete (2009). Dahlke et al. (2012) compared different globally available faPAR satellite products and found considerable biases between the different data products, whereas the vegetation seasonality was consistently captured by the different investigated data products.
Acknowledgements. Collaboration among the authors was supported by the  TERRABITES COST Action   ES0805  and by the  International Space Science Institute  (nr. 263 ). The main author was supported through the  Cluster of Excellence "CliSAP"  ( EXC177 ),  University of Hamburg , funded through the  German Science Foundation (DFG)  and the  ESA Climate Change Initative Climate Modelling User Group  (contract:  4000100222/10/I-AM ). Juliane Otto  was funded through  ERC starting  grant  242564. Atmospheric radiation data were obtained.
These analyses are ongoing and will be reported separately. All participating cohorts followed local national guidelines/regulations regarding patient consent and/or ethical review. In particular, of the countries represented by the participating cohorts, only Switzerland and Australia require specific ethical approval for D:A:D in addition to that required for their national cohorts (Swiss HIV Cohort Study and AHOD), both of which have obtained this approval. France, Italy, and Belgium do not require specific ethical approval over-and-above that required for the individual cohorts (Nice/Aquitaine, Brussels St. Pierre and IcoNA, respectively). Neither the Netherlands (ATHENA) nor the United States (CPCRA) require specific ethical approval as data are provided as part of HIV care in the first instance, and as the datasets are non-identifiable public use datasets in the second instance.
End-points Eight of the 11 D:A:D cohorts have provided ongoing information on all new NADC (any non-AIDS malignant disease other than basal or squamous cell skin cancer and pre-cancers) occurring after the protocol change on 1st February 2008 (the Swiss HIV Cohort, ATHENA, the Nice HIV Cohort, the ANRS C03 Aquitaine Cohort, EuroSIDA, the Australian HIV Observational Database, the ICONA Foundation Study and the Brussels St. Pierre Cohort). However, as the eight cohorts had been systematically collecting information on all new NADC that occurred from 2004 or earlier, information was also collected retrospectively on events occurring from 1st January 2004 until 31st January 2008. Detailed information on all NADC is collected on a specific case report form (CRF); information collected includes date of diagnosis, type/location of cancer and histology/cytology report or other diagnostic method.
Stage of disease (localised, disseminated or unknown) was available for some specific types of cancers only (particularly lung/ anal cancers and Hodgkin's lymphoma). Written guidelines for the completion of CRFs were distributed in 2007, and training was provided to staff at participating cohorts prior to data collection. Intensive quality assurance, including event monitoring, took place in 2009/2010. All reported events are evaluated and classified, where possible, as definite, possible or probable cancers by an independent committee that includes an oncologist. Classification reflects the degree of certainty in the NADC diagnosis: Definite: based on supportive histology/cytology reports or a detailed summary of histopathological findings; Possible: based on a precise clinical description of the case, and where treatment had been initiated to support the description of an invasive NADC, but lacking supportive histopathological findings; Probable: based on clinical suspicion, biochemical or ultrasound findings.
The present analysis is based on data collected up to 1st February 2010. By that date, reports had been received on 1993 potential NADC; after excluding NADC events occurring prior to baseline (the latest of 1 January 2004 or entry in the D:A:D Study) and apparent NADC events that were subsequently rejected as they did not meet the study criteria for a definite, possible or probably NADC (AIDS-defining cancers, dysplasias, subsequent events following a first event, in situ cancers particularly for the anal and head/neck regions), 880 NADC were ultimately included in analyses. The majority of rejected events occurred prior to baseline. Of the 880 included events, 472 were accompanied by a completed CRF and could be classified as either definite (n = 383, 81. 3%), probable (n = 38, 8. 1%) or possible (n = 44, 9.
Statistical methods Follow-up for the analyses of NADC incidence started on the latest of 1st January 2004 or the date of enrolment in the D:A:D Study, and finished at the earliest of the date of diagnosis of a new NADC, 1st February 2010 (the cut-off date for the present dataset), death or six months after the patient's last clinic visit. Crude incidence rates were estimated for each year of follow-up from 2004, overall and stratified by gender, age attained and mode of HIV acquisition. Incidence rates were also estimated separately for the three most frequently occurring cancers (Hodgkin's lymphoma, anal and lung cancers). Age-adjusted incidence rates (adjusted to the age distribution of the cohort in 2004) were also estimated for all NADC combined. As it was not possible to estimate age-adjusted rates for specific cancers due to the small number of these events, the impact of the ageing population on calendar trends for specific cancers was investigated via Poisson regression with adjustment for age (as a time-updated covariate).
As most of the deaths that did occur were cancer-related, these findings may highlight a possible role of immune suppression in the development, and subsequent control, of cancers. Subsequent analyses of this cohort will investigate associations with immune suppression in more detail. However, it is important to note that other explanations for an association between survival and the CD4 count cannot be ruled out. For example, the finding may reflect bias that may be introduced if patients with lower CD4 counts were not offered, or could not tolerate anti-cancer treatments (we have limited data on use of chemo-and/or radio-therapy, or on surgical treatments for cancer). Our mortality rates among patients with Hodgkin's lymphoma are similar to those previously reported in the setting of HIV perhaps reflecting our contemporary HIV population where the majority are well treated on cART and with access to appropriate cancer care.
7, Cohortes], to the Aquitaine Cohort; The Australian HIV Observational Database (AHOD) is funded as part of the Asia Pacific HIV Observational Database, a program of  The Foundation for AIDS Research, amfAR , and is supported in part by a grant from the  U. S. National Institutes of Health's National Institute of Allergy and Infectious Diseases (NIAID)  [grant number  U01-AI069907 ] and by unconditional grants from  Merck Sharp & Dohme ;  Gilead Sciences; Bristol-Myers Squibb ;  Boehringer Ingelheim Pharmaceuticals Inc. ;  Roche ;  Pfizer ;  GlaxoSmithKline ;  Janssen Pharmaceuticals. The Kirby Institute is funded by The  Australian Government Department of Health and Ageing , and is affiliated with the  Faculty of Medicine, The University of New South Wales. By grants from the  Fondo de Investigación Sanitaria  [grant number  FIS 99/0887 ] and  Fundación para la Investigación y la Prevención del SIDA en Espanã  [grant number  FIPSE 3171/00 ], to the  Barcelona Antiretroviral Surveillance Study (BASS) ; by the  National Institute of Allergy and Infectious Diseases, National Institutes of Health  [grants number  5U01AI042170-10  ,  5U01AI046362-03 ], to the  Terry Beirn Community Programs for Clinical Research on AIDS (CPCRA) ; by grants from the  BIOMED 1  [grant number  CT94-1637 ] and BIOMED 2 [grant number  CT97-2713 ].
The human skin microbiome could provide another example, after the gut, of the strong positive or negative impact that human colonizing bacteria can have on health. Deciphering functional diversity and dynamics within human skin microbial communities is critical for understanding their involvement and for developing the appropriate substances for improving or correcting their action. We present a direct PCR-free high throughput sequencing approach to unravel the human skin microbiota specificities through metagenomic dataset analysis and inter-environmental comparison. The approach provided access to the functions carried out by dominant skin colonizing taxa, including Corynebacterium, Staphylococcus and Propionibacterium, revealing their specific capabilities to interact with and exploit compounds from the human skin.
Human subjects remain anonymous and the informed consent was written. For each individual, environmental (skin surface) DNA samples were extracted and purified according to the protocol described by Griffiths  [9]  and subsequently pooled. The same sampling effort was repeated for a second week in order to constitute a second replicate of the global human skin microbiota for each of the two individuals tested (detailed protocol accessible in Protocol S1). These four DNA samples (. 3 micrograms each) were tagged separately and sequenced (Titanium pyrosequencing technology, reads size average of 1846138 bp). Artificial duplicates were deleted using cd-hit-454 with default parameters  [10]  and human genome related reads were deleted prior to data analysis. Human DNA contamination was low in both datasets: individual 1 (0.
Generating and Annotating Metagenomic Datasets Sequence reads were compared to reference databases in order to annotate and classify them in functional subsystems: the generated metagenomic datasets (read average number = 3365764835) were uploaded on the MG RAST v3 public server  [11]  for both taxonomic (Lowest common ancestor approach  [12] ) and functional annotations (e-value cut-off of 10 25 ). The relative distribution of these sequences was normalized as a function of the total number of annotated reads in each dataset  [13]. Skin metagenome datasets were compared to 65 publicly available metagenomic datasets (MG-RAST, accession numbers in table  S3 ) from different environments, such as soil, oceans, hot springs, acid mine drainage, sludge wastewater treatment plant, air, deep ocean, human gut, cow, mouse and chicken gut.
Specificity of the Human Skin Microbiota Using the SEED annotation, 72. 2% (62,2%) of the 22 456 (62765) predicted proteins from human skin microbiota were functionally annotated. Most of the sequences were related to a limited number of general subsystems (fig. 1 ) like carbohydrates, amino acids and derivatives, as well as protein metabolism. In contrast, other general subsystems are less represented (e. g. , motility and chemotaxis). Homogeneity of the functional distributions among the different datasets can be explained by the functions observed at the general subsystem level (e. g. photosynthesis, respiration…). Specifically, hundreds of subsystems can be simultaneously compared when studying datasets at more detailed functional levels. However it was not possible to explain the functional specificity of the skin microbiome without comparing it to other environments.
Thus, principal component analysis (PCA) based on the relative functional distribution of the reads from 69 datasets (annotated by SEED functional level 3 subsystem, 800 functions compared), including human skin and feces, revealed an apparent specificity of skin microbial communities since their related datasets clustered separate from those generated on the 12 other investigated environments (fig. 2 ). While methodological differences could induce biases among the different metagenomic datasets, these do not compromise their comparison as has been demonstrated previously  [13]. Among the 626 functional subsystems detected in human skin related datasets, 52 possessed a distribution significantly different from the 65 other datasets (table  S2 ). These 52 functional subsystems were reviewed to decipher the specific nature of the skin core-(meta)genome and its potential contribution to human health.
Specific Functional Distributions One of the major advantages of metagenomic approaches is the possibility of describing a complex community without any a priori views concerning taxa and/or functions prior to exploiting sequence data. The human skin surface microbial communities potentially possess strong capacities for interacting with their environment based on inter-ecosystem comparisons (fig. 3 ). The functional potential of skin bacteria indicates that these bacteria are strongly adapted to the exploitation of compounds produced by the human skin, including sugars, lipids (and iron), and petrobactin-mediated iron uptake systems were detected in the metagenome datasets. The presence of lactic acid generated by sweat (. 99 mg/ml)  [15]  and of a sugar, such as sialic acid, common to humans and animals justifies the predominance of respective catabolic genes in our skin metagenome datasets.