dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA Analysis Total cellular RNA was extracted using and RNeasy Mini Kit (Qiagen). 50ng RNA was used for firststrand cDNA synthesis with AffinityScript qPCR cDNA Synthesis Kit (Agilent Technologies). Quantitative RT-PCR (qRT-PCR) analysis was performed on the Corbett Rotorgene 3000 (Qiagen) using Brilliant II SYBR Green QPCR Mastermix (Agilent Technologies, Stratagene). Validated RT-PCR primers for hK2, TMPRSS2 and GAPDH were from Qiagen, and PSA reported in  (12). qRT-PCR data was normalized using the GAPDH and calculated relative to control cells using the comparative 2 -Ct method.
Microscopy Microscopy was performed at Monash Microimaging, Australia, using Leica TCS NT and Nikon C1 confocal microscopes. Prostate cancer sections were imaged using an Olympus Provis Ax70 microscope fitted with an Olympus DP70 camera (DAB) and using AnalySis software. Image processing was performed using Image J software (National Institutes of Health, USA) and was limited to alterations of brightness, subjected to the entire image.
ACKNOWLEDGEMENTS The  Australian Prostate Cancer BioResource  is supported by the  National Health and Medical Research Council of Australia Enabling  Grant (no. 614296 ), and by a research infrastructure grant from the  Prostate Cancer Foundation Australia. We also acknowledge for supply of human tissues;  Assoc. Prof. John Pedersen  ( Department of Anatomy and Cell Biology, Monash University ), and  Assoc. Prof. Mark Frydenberg  ( Department of Surgery, Monash University ). Thanks to  Dr Melissa Papargiris  ( Australian Prostate Cancer Collaboration ) for assisting with the acquisition of patient samples.
Interpolated fits that are not constrained by data on both sides of the mask tend to diverge and no longer reasonably approximate noise within the mask. Conversely, smaller masks avoid this problem but leave residual Galactic signal for the polynomial fit, causing higher order polynomial fits to remove true Galactic signal instead of only noise. These results are shown in the last two panels of Figure  7. While mean subtraction does not remove large-scale noise from the data, FRM modulation mitigates some of the 1/f atmospheric fluctuations that would otherwise need to be filtered. The optimal mask cuts maximal Galactic signal from the fits while retaining sufficient off-Galactic data to yield statistically significant fits for the mean. Given the Galactic coordinates defined by (b, l) = (latitude, longitude), the radius of the masked region was reduced from | b |< 3 • (the maximal possible mask for the deep integration scans) until the DC offset fits converged for the majority of half-scans.
Instrumental Polarization Removal Temperature to polarization leakage from a bright source such as the Galaxy can cause significant artificial structure in polarization. Laboratory testing revealed that FRM150A showed approximately 1% IP, the highest of any of the devices; a value that could distort Galactic polarization maps. To remove this temperature to polarization leakage, the IP value for each half-scan was calculated using a fit to the preceding elevation nod. The derived fractional IP value was then multiplied by the half-scan sum data and subtracted from the difference data. Figure  8  shows an example of IP leakage derived from elevation nods. Because the atmosphere is unpolarized, the elevation nods should cause a change in the summed (but not differenced) data for each demodulated PSB.
Map Making After timestream data processing was complete, Stokes T , Q, and U were derived following the formalism in  Jones et al. (2007). Temperature data were obtained by binning the filtered detector timestreams into map pixels, p, as T (p) = n i jǫp w + ij d + ij n i w + ij , where the indices i and j denote the PSB channel number and timestream sample number respectively. The variable d + is the sum demodulated timestream, n is the number of PSBs, and w + is the weight assigned to each demodulated sum. Stokes Q and U were calculated by ∆ Difference (V) Time (s) n i ǫp w - ij d - ij α ij d - ij β ij Figure  8. From top to bottom: Elevation angle, FRM150A demodulated sum, and FRM150A demodulated difference data for an elevation nod versus time for FRM 150A. The mean has been subtracted from both the sum and the difference data so that small changes can be seen.
The difference data is approximately 1%, of the sum data, indicative of instrumental polarization. 1 2 n i jǫp w - ij α 2 ij α ij β ij α ij β ij β 2 ij Q(p) U (p) (5) where w -is the weight assigned to the demodulated timestream difference and α and β are the differenced orientation factors α ij = γ i (cos[2(ψ ij + 45 • )] -cos[2(ψ ij -45 • )]) (6) β ij = γ i (sin[2(ψ ij + 45 • )] -sin[2(ψ ij -45 • )]) (7) The factors of ±45 degrees come from the Faraday rotation of the nominal polarization angle of the i th PSB. The same correction factor describing cross polar leakage, γ i = 1-ǫi 1+ǫi , is applied to both terms α ij and β ij. Equation 5 is degenerate for a single FRM PSB at time j. To break the degeneracy, the same sky pixel p is observed by the same modulated detector at a different polarization angle ψ ij.
Absolute Calibration To relate detector units to CMB units, BICEP measurements are cross-correlated with WMAP temperature maps to derive an absolute gain calibration. A complete description is given in  (Chiang et al. 2010). This same comparison cannot be made for the FRMs, as the FRMs were never biased and observing during CMB observations. Instead, FRM detectors were calibrated via comparison with the calibrated 3-year BICEP temperature maps of the bright arm of the Galaxy. The absolute calibration factor was computed as the slope of a linear fit to the pixel-pixel scatter plot of the two maps. The variance of the difference between the fit data and the BICEP data is used to compute the un- certainty. This method yields values of 1. 1288 ± 0. 0023 and 1. 0011 ± 0. 0017 times the nominal absolute calibration factors for BICEP at 100 and 150 GHz respectively.
MAP RESULTS Figure  9  shows both the integration time and Galactic temperature maps derived from the shallow FRM observation at 100 GHz and 150 GHz. Because the shallow observation featured long sweeps of the Galaxy, half-scans were filtered using a second order polynomial rather than mean subtraction. The maps were binned with Healpix  (Gorski et al. 2005 ) pixelization 0. 25 •. A σ = 0. 5 • Gaussian smoothing function has been applied to all maps. Due to glitches in the rotator bias signals, only the first and last 9 hour sections resulted in usable data, a total of approximately seventeen hours of integration time including data cuts. The deepest integration time was ≈ 15s per pixel, with an average of 4. 1 and 5. 4 s per pixel at 100 and 150 GHz, respectively. Data from 5 out of 6 of the FRM pixels (two at 100 and three at 150) were used for these maps; one was omitted due to biasing problems.
25 • , prohibiting a direct comparison of a single non-FRM PSB pair to a single FRM PSB pair; for a single non-FRM PSB pair, the integration time is too low and the number of boresight angles is insufficient to accumulate polarization. As such, maps were accumulated using all non-flagged PSB pairs in the focal plane. PSB pairs containing FRMs (among others) were flagged and excluded from analysis. The data were taken over all three BICEP observing seasons, totaling 763 observing hours for the entire shallow observation region. Non-FRM data were processed using the same analysis pipeline as the FRM data with a few exceptions. The initial deconvolution was applied with a low-pass filter at 5 Hz. Following deconvolution, the data were downsampled to 10 Hz before preliminary processing and relative gain calibration.
The demodulation step was omitted; the sum and difference data were taken between the two PSBs within each pair. The data were then subjected to the same masking and filtering strategy as the FRM pixels, with the additional complication that all data outside the region of deep integration were also masked from the fits. This is to keep large portions of off-Galactic data from weighting the DC offset subtraction, which was not possible with the FRM deep integration scan strategy. Maps were accumulated using the same formalism as Section 4. 2. 6, except Equations (  6 ) and (  7 ) are modified to α i,j = γ i (cos[2(ψ i+1,j )] -cos[2(ψ ij )]) (8) β ij = γ i (sin[2(ψ i+1,j )] -sin[2(ψ ij )]) (9) where i and i + 1 are the indices of the two PSBs within the pair that have been subtracted. The binning and smoothing of the maps is identical to the FRM deep observations.
The right two columns of Figure  10  show the integration time as well as Galactic T, Q, U, polarization vector, and Q and U difference maps at both 100 GHz and 150 GHz from the BICEP observations accumulated using the non-flagged focal plane PSBs. A total integration time of approximately 324 hours and 194 hours was taken at 100 and 150 GHz respectively, with a mean integration time of 412 seconds per pixel at 100 GHz and 246 seconds per pixel at 150 GHz. The maps exhibit very good agreement with the FRM maps in both temperature and polarization at both frequencies. As evidenced by the polarization vector maps in Figure  10 , both data sets show the same strong Galactic polarization, roughly perpendicular to the Galactic plane, with the highest polarized signal in the upper left quadrant.
The scandirection split, where the two data sets are separated based on half-scan direction, can generate noise based on thermal instability at half-scan endpoints. The scandirection difference maps are shown in the last two rows of Figure  10. PSB pair difference maps subtract the maps accumulated from individual detectors within a pair. This difference map is unique to FRM analysis, as a FRM turns a single BICEP PSB into a polarimeter, allowing T, Q, and U maps to be accumulated for individual detectors within a PSB pair. This difference map is perhaps the most robust data quality test for the FRMs as it is sensitive to many factors including relative gain mismatches, demodulation errors between detectors, and thermal stability. The PSB pair difference maps are shown in Figure  11. Finally, observation difference maps subtract maps accumulated from odd and even numbered observing runs.
This jackknife probes for sensitivity to weather changes. The FRM maps show the highest sensitivity to changes in weather, especially in the 150 GHz PSB. The main source of the elevated signal at 150 GHz is a very high noise contribution at the lowest declination, where the least integration time occurs. Difference maps of the two data sets, the FRM and non-FRM BICEP, are displayed in Figure  12. The rms map noise per pixel is given by 20. 6 µK rms/pixel at 100 GHz and 35. 6 µK rms/pixel at 150 GHz. This differencing should remove all true sky signal and leave only uncorrelated noise, which should combine as the quadra- ture sum of the noise in the two individual maps. To estimate the map noise in the non-FRM BICEP map, the scan direction difference map was utilized, yielding 12. 5 and 12.
8. ACKNOWLEDGMENTS S. M. gratefully acknowledges the  American Association of University Women  for a fellowship supporting this research. B. G. K also acknowledges  NSF PECASE  Award No. AST-0548262. G. R. gratefully acknowledges support by the  NASA Science Mission Directorate  via the  US Planck  project. BICEP is supported by  NSF  Grant  OPP-0230438 ,  Caltech Discovery Fund, Caltech Presidents Fund PF-471 ,  JPL Research and Technology Fund , and the late  J. Robinson.
Introduction While the pathologist population tends to dramatically drop, the number of pathological cases to be examined increases sharply, mainly due to early screening campaigns; developing automated systems would thus be useful to help pathologists in their daily work. As Virtual Microscopy (VM) is more and more introduced in pathology departments  [1]  where it holds immense potential despite the large amounts of data to be managed, its combination with image processing techniques can allow to find objective criteria for differential diagnosis or to quantify prognostic markers. Thus, many works try to develop computer-aided diagnosis systems (CADS) based on image retrieval and classification  [2, 3]. The first step consists in building a knowledge database involving many features extracted from a set of well-known images; it is an 'off-line' procedure conducted once.
These features are represented by vectors of non-linear data acting as a signature for the original images. In a second step, signatures are obtained from new unknown images to analyze and compared with the database; it is an 'on-line' procedure. Because of tumor heterogeneity, it is essential to build knowledge databases containing representative features of the multiple morphological types of lesions before considering to implement a CADS. But, as it is almost impossible for a pathologist to manually segment large virtual slide images (VSI), the usual practice consists in manually selecting some 'representative areas'. A bias is then introduced in the process as this choice is obviously subjective. It is then mandatory to find wiser solutions leading to an unbiased collection of these 'representative areas' (and later called 'patches').
In a previous work  [4] , we have proposed an original strategy: starting from a collection of breast cancer VSI, then taking advantage of stereological sampling methods and diffusion maps, a knowledge database is obtained from a reduced number of patches that are representative of given histological types. The sampling tools offered by stereology are well-suited in this context  [5]. Systematic sampling starting from a random point with a fixed periodic interval is able to reduce the area to be analyzed, while preserving the collection of distinctive regions encountered in a tumor. However, even if the working area becomes smaller, the number of selected patches can be very large and may include many redundant elements. A data reduction has then to be conducted. Among the available methods, the diffusion maps technique  [6, 7]  has been retained since it provides a very attractive framework for processing and visualizing huge non-linear bulk data.
Materials VSI come from histological sections of breast tumors stained in the same laboratory according to the Hematoxylin-Eosin-Safron protocol and acquired with the same digital scanner (a ScanScope CS from Aperio Technologies). The aim being to develop a generalized CADS, it is mandatory to manage color calibration of each device used along the process, from histological staining up to image acquisition  [9]. For this study, we have collected image patches from two histological types: Invasive Ductal Carcinoma (IDC) and Mastosis (Ma) with patches from the 'normal' morphology for further be able to remove non-informative patches. VSI have been acquired at X20 (0. 5 µm per pixel) and stored in TIFF 6. 0 file format (compression 30%). The tools are developed in Python language with the help of specialized modules (PIL: Python Imaging Library, SciPy and mathplotlib).
Data reduction This study aims to develop a CADS whose one component is a visualization tool showing relations between breast cancer images, stored in a knowledge database, and new images presented to the system. Typically, these relations may be expressed as a connected graph in a 3D space where we hope to find 30 distinctive clusters corresponding to histological types or sub-types. It is therefore mandatory to reduce dimensionality from n (65 dimensions in our example) to 3. The signatures being non linear data, it is not appropriate to perform a principal component analysis (PCA). Belkin  [5]  and Coifman  [6]  have shown that methods based on Spectral Connectivity Analysis (SCA) such as diffusion maps, involving eigenvalues and eigenvectors from a normalized graph Laplacian, are well suited to non linear data.
Conclusion This work is the second part of a CADS we aim to develop based on an original strategy starting from VS and leading to an unbiased knowledge database containing reference patches of breast tumors. The first part has been presented in  [4]. We have shown that combining stereological sampling and data reduction based on diffusion maps offers an interesting general framework. The results illustrated here are a proof of concept of the second part that is to classify new unknown patches. About 400 high resolution VS are now available in our lab; the benign and malignant breast tumors are classified into 30 histological types and subtypes. We plan to project some reference patches extracted from these 30 classes in the same 3D space, in order to build clusters, and then to classify a new unknown VS previously split in patches.
Minimal promoter definition and validation of transcription factor binding sites meloe messenger comprises multiple short ORFs (Open Reading Frame). The most proximal ORF shown to be translated is MELOE-2  [2]. We first defined the transcription initiation site by 59 RACE PCR analysis starting from MELOE-2 proximal 59 region. The longest amplified sequence places the start codon 544 bp upstream of MELOE-2 ATG, and thus presented 259 additional bases compared to the public sequence of meloe transcript [NR_026664]. In silico analysis of meloe proximal promoter region (from 22000 to +1 nucleotide), located in the third intron of the HDAC-4 gene (NG_009235. 1) [1] , conducted with Jaspar database (Jaspar http://jaspar. cgb. ki. se/), highlighted the putative TATA element located 30bp upstream of the +1 transcription start and a number of motifs able to bind TFs, some of them illustrated on Figure  2A.
MIGS In 2009, the scientific committee of GENOSCOPE selected the IIV-22 genome for sequencing. The complete genome sequence and annotation are now available in GenBank (accession no. HF920633). A summary of the results of this project is shown in Table  1. The genome of IIV-22 was sequenced using the 454 FLX pyrosequencing platform (Roche). Library construction and sequencing were performed as described previously  (Henn et al. , 2010). Assembly metrics are described in Table  1. The assembled contig representing the entire IIV-22 genome sequence was confirmed by comparing five predicted restriction fragment profiles from the genome, for BamHI, EcoRI, HindIII, PstI and SalI, with the matching fragment profiles produced by actual restriction digestions of the IIV-22 genome  (Williams, 1994).
No nucleotide sequence identity was found between IIV-22 (the isolate from the blackfly larva) and either isolate IIV-3 (from a mosquito larva) or isolate IIV-6 (from the lepidopteran Chilo suppressalis). The annotations for the 167 genes are shown in Table  S1  (available at JGV Online). This revealed that 166 of the 167 protein-coding genes had a related gene in databases with e-values below 10 23. Only seven IIV-22 genes, 012R, 036L, 089R, 104R, 120R, 126R and 167R, had no orthologues in the IIV-9 genome. Sequence analysis of the IIV-22 genome revealed that it is closely related to IIV-9. Thus, the functions of the 67 IIV-22 protein-coding genes are presumably the same as those described previously  (Wong et al. , 2011). The cluster of five collinear genes in IIV-9 (097R-101R) and IIV-3 (028R-032R;  Wong et al.
Data collection Research personnel from each hospital collected data and implemented the interventions at their study site. These personnel were from departments that supervise infection control activities at the participating hospitals, including infection control, infectious diseases and hospital epidemiology departments. They were trained at the study coordinating centre with regard to the study protocol, the outcome definitions and the use of the data collection tools prior to the start of the study to ensure consistency of data collection across the hospitals. Local microbiology laboratory data were reviewed to  HH adherence was monitored by the research personnel who had been trained and validated in the WHO method of direct observation at the study coordinating centre. 15 A standardised observation form was used by all centres.
All hospitals collected data for 100 HH opportunities per ward during baseline and washout phases. 20 H observers were specifically instructed not to provide feedback to healthcare workers concerning their HH practices during these study phases and the observers were independent of surgical ward staff, reducing the likelihood of the Hawthorne effect, in which staff improve their practices when they are aware that they are being observed. 21  During the intervention phase, there was intensive monitoring of HH practices in wards using the enhanced HH and combined strategies. In these wards, 100 HH opportunities per ward per month were observed as part of the intervention. Implementation of contact precautions, decolonisation therapy and single room isolation for MRSA carriers was randomly audited each month.
Signage of MRSA status and availability of gowns, gloves and alcohol-based handrub for contact with MRSA carriers was also audited. Data regarding numbers of admissions, patient-days, surgical procedures and level of staffing were collected. Owing to variation in the availability and quality of electronic medical record and pharmacy data between the study sites, individual-level data (such as length of stay) and antibiotic utilisation data for the surgical wards was not collected as part of this study. Ward-level data were submitted monthly to a central data management centre through a password-protected secure online database which included range, consistency and missing data checks. Meetings, site visits and monthly teleconferences were held to review data, ensure adherence to study protocols and address queries.
MicroRNAs FROM THE MOLECULE TO THE NETWORK The discovery of the first microRNA, C. elegans  Lin-4, in 1993 (Lee et al. , 1993; Wightman et al. , 1993)  has profoundly revolutionized our perception and understanding of gene regulation. At that time, small antisense RNA were tedious to identify by standard genetic approaches, but, since then, the use of nextgeneration sequencing and its ongoing technological improvements has pervaded the benches, leading to the identification of 1872 mature microRNAs in human, 1186 in mouse and 449 in rat, according to the Mirbase database (www. mirbase. org, release 20, June 2013). MicroRNAs are endogenous 22-nucleotide long, non-coding RNAs that regulate gene expression post-transcriptionally, upon specific base-pairing of their 5 (the seed) generally to the 3 untranslated region (UTR) of a target mRNA.
However, this incremental composition requires some adaptation described in Section 3. 2. The aforementioned abstraction, minimization and composition operations are performed using the CADP toolbox  (Garavel et al. , 2011) , which provides several tools to produce, transform and analyse LTSs from process specifications. LRM behaviours are expressed using the LOTOS NT specification language  (Champelovier et al. , 2011) , in terms of a process with as many associated gates as components (input and proper components), and with state variables representing the values of the components. Each gate can issue actions with labels denoting updates of component values. The process evolution is driven by the logical rules that elicit updates of the state variables. When the conditions associated to a component update are met, an action associated to the gate of the component is issued, the corresponding state variable is updated accordingly and the main process loops back.