dataesr/scientific-paragraphs-categorization huggingface dataset:
One cannot assume that the criteria used by one methodology used for one center for a CDC would be applicable to another center performing a CDC. Using an acceptance criteria of ±3%/3 mm with a pixel pass rate of 90%, the gamma evaluation analysis results obtained with the RPC PH for the sagittal plane indicates a pass rate of 17% while the VPP institution dosimetry gamma analysis using the same criteria indicated a significantly different (75%) pass rate (Table  2 ). There was however no significant difference when analyzing at the ±5%/5 mm (90% of pixels passing; pass rate of 92% vs. 100% for the RPC and VPP, respectively) between the 12 RPC datasets and the 12 VPP datasets for this limited dosimetry analysis and sample size. It is well know that commercial QA systems have unequal sensitivities to detect dose errors.
Interestingly, the majority of our RPC PH -INST PH discrepant results stemmed from the Delta 4 system (data not shown), which is known to have suboptimal performances for CDC  [12]. It is crucial that more data be compiled before any true judgment is made regarding the VPP credentialing process. Credentialing using the RPC phantom includes a much larger dosimetry dataset than evaluated and used in this study and as such, no equivalence between the RPC and VPP CDC methods for the credentialing process can be made at this time. The question posed by this study is whether the dosimetry comparisons from various TPSs and phantoms used by each EORTC institution (Table  1 ) can be used to verify IMRT planning and dose delivery for multiinstitution clinical trials as compared to the established RPC phantom dosimetry measurement system.
This study has, however, some major limitations and before the VPP paradigm is deemed acceptable for CDC in prospective trials, a more extensive study and dataset are needed to validate the appropriateness of this proposed dosimetry evaluation and credentialing process. First, the limited number of centers in this study prevents any true direct correlation between the RPC PH and INST PH CDC results. More results are warranted and a further comparison of centralized phantom credentialing vs. virtual phantom credentialing is needed. Second, the data from 6 out of 18 institutions (33%) could not be analyzed because of data transfer incompatibilities that have to be resolved. In two cases, the data could not be read by the evaluation software. This problem might be solved in new versions of either the measurement software or evaluation software.
In 4 cases, the dataset was incomplete. The fact that, in our analysis, a third of the data stemming from EORTC centers could not be analyzed could be considered tantamount to disproving the ability of the VPP to perform IMRT credentialing within the context of prospective trials. Truth to be told, the retrospective design of this analysis put a lot of pressure on medical physicists from EORTC centers to obtain these data that were neither supportive of a compulsory credentialing requirement process nor did benefit patients treated in busy radiation oncology departments. This problem could be solved with the introduction of prospective credentialing as is performed for NCI funded clinical trials, such that without providing the full dataset, trial participation is not granted. The VPP methodology will be prospectively tested in the new EORTC lung cancer study (www.
e. , RPC credentialing) with a small measurement uncertainty. The need to include credentialing phantom failures and compare those results to the VPP CDCs is necessary to ascertain the validity of the VPP process. Credentialing through a VPP should thus never be performed as the only QART check within a trial. Beam Output Audits should always be required, as it is currently for most institutions participating in clinical trials. This study is essentially the first step in a proof of concept and feasibility study to perform virtual credentialing, however much work is still to be done. The use of standardized anthropomorphic phantoms has undeniably advantages, when compared to the use of institutional phantoms only. The uniformity and quality of data that are collected centrally is unquestionable.
These potential errors could potentially be identified also by institutional credentialing, however the data from > 1000 IMRT H&N phantom irradiations suggest that this is not the case since nearly 20% of the institutions failed the RPC phantom irradiation test using a generous criteria  [3]. Incorrect output factors and percent depth dose data, entered by the institution, cannot however be thoroughly verified by an institutional credentialing process without central supervision and absolute dosimetry measurements. Moreover, incorrect systematic application of QA calculations or measurements can be diligently identified by the RPC's independent audit, but not by the institution itself. These types of dosimetric violations should be routinely identified by the local QA process of participating institutions which have their own QA programs, but because of human error, workload, to name a few, many of these dosimetric discrepancies may go undetected.
Thus, the use of a VPP could potentially also allow a faster credentialing and introduction of these techniques in EORTC clinical trials if the VPP can be made to be uniform in its data collection and assessment. Moreover, these phantoms might also be used to test the 4D CT imaging capabilities of these institutions  [15]. In conclusion, IMRT dosimetry gamma evaluations for a single plane of data evaluation for a H&N prospective trial using the institutional phantom measurements showed agreement at the gamma index criteria of ±5%/5 mm (90% of pixels passing) in this analysis that involved a limited number of VPP measurements. Gamma indexes also showed agreement between RPC and VPP metrics at the ±7%/4 mm (85% of pixels passing) but more VPP data sets are needed before a definite conclusion can be reached since we cannot assume that the critieria levels being used by the two evaluations, RPC and VPP, would be the same as the means to compare the two techniques.
Mixed venous oxygen saturation (SvO 2 ) changes were modeled in 1,000 patients using JMP (SAS Institute, Cary, North Carolina, USA). In both panels, we used a random uniform distribution of age (range 20 to 90 years) and temperature (range 34 to 41°C) and the normative tables as modeled by the Hemodyn software®  [7, [26] [27] [28] [29]. This generates a wide range of total body oxygen consumption (VO 2 ) needs (70 to 290 ml/minute. m 2 ) and their corresponding cardiac output values (2. 28 to 4. 22 L/minute. m 2 ) from which individual values of needed SvO 2 can be inferred from Equation  2 according to different values of hemoglobin concentration and arterial oxygen hemoglobin saturation. In the left-hand panel, only old and hyperthermic patients have an expected physiological SvO 2 < 0. 65.
The last study showed that a loading dose of 1,800 mg/day for 4 days followed by a 1,200 mg/day regimen for 6 days with a normal meal provided the desired exposure of fexinidazole and its metabolites, particularly sulfone, with good tolerability. Based on preclinical evidence from a chronic infection mouse model, systemic drug concentrations obtained are expected to be clinically effective in stage 2 HAT. Conclusions These studies show that fexinidazole can be safely assessed in patients as a potential oral cure for both stages of HAT. The studies were registered with ClinicalTrials. gov.
Study Protocol 1 (NCT00982904) Study protocol 1 was a combined protocol that included three parts (study 1A-C). Study 1A was a double-blind, within-cohort placebocontrolled SAD study of ascending doses of fexinidazole ranging from 100 to 3,600 mg in the form of an oral suspension of the PIB formulation carried out in cohorts of eight subjects (six active and two placebo) to determine safety and tolerability of the drug  [16]. For each dose, adverse events (AEs), electrocardiogram (ECG) findings, vital signs and clinical laboratory tests (haematology, blood chemistry and urinalysis) were assessed for up to 48 h postdose in a blinded fashion by the SRC before administration of the next dose. Blood samples for drug assay and plasma pharmacokinetic analysis were drawn until discharge. Urine was also collected for pharmacokinetic analysis.
Study Protocol 2 (NCT01340157) Study protocol 2 was a food interaction study performed to evaluate, in addition to the previous high-fat rich meal (Study 1B), the effect on fexinidazole bioavailability of two different meals, more representative of the local diet in target regions: a field-adapted meal of rice and beans, an easily quantifiable and non-fat carbohydrate food; and a fieldavailable high-fat meal (Plumpy'Nut Ò ). The design was a randomised, open-label, three-way crossover study to assess the effect of the two different types of food versus fasted conditions on the bioavailability and safety of a single 1,200 mg dose (two tablets of 600 mg). At each treatment sequence (treatment x = fasted, treatment y = Plumpy'-Nut Ò , and treatment z = rice and beans) safety and tolerability follow-up were done up to 48 h post-dose, and an endof-study visit was performed on day 8 of the last period of treatment before final discharge.
Study Protocol 3 (NCT0148370) Study protocol 3 was carried out following a population pharmacokinetic evaluation using all available data from the preceding studies exploring the various dosing regimens with food. The aim was to ascertain the most efficient dosing schedule in terms of rapidity to peak steady state and extended duration of systemic bioavailability of the fexinidazole sulfone metabolite M2. Simulations of the cerebrospinal fluid concentration were included using the free fraction determined in plasma in Study 1B. The target was to obtain the same plasma concentration that provided a curative effect in the GVR35 chronic mouse model  [9, 10]. This murine model is, to date, the most widely studied laboratory model of the chronic disease, and fexinidazole cured the disease in this model, with oral dosing resulting in plasma drug concentrations of the sulfone metabolite of [10,000 ng/mL for a number of hours post-dosing.
Pharmacokinetic Analysis Pharmacokinetic parameters for fexinidazole and its sulfoxide (M1) and sulfone (M2) metabolites were estimated using non-compartmental methods (WinNonlin Ò software, version 5. 2, Pharsight Corporation, Mountain View, CA, USA). The pharmacokinetic parameters studied were maximum plasma concentration (C max ), time to reach C max (t max ), elimination rate constant (k e ), area under the plasma concentration-time curve (AUC) from time zero to the last measurable concentration (AUC last ) and, when applicable, AUC from time zero to 24 h post-dose and from time zero to infinity (AUC 24 and AUC ? ) determined by a linear trapezoidal method, terminal elimination half-life (t b ) determined by linear regression using at least three time points, apparent total clearance of the drug from plasma (CL) after oral administration (CL/F) estimated using the ratio of dose to AUC, apparent volume of distribution after oral administration (V d /F) determined as CL/k e.
1 ). Inter-individual variability of C max and AUCs were slightly higher for the parent compound than for the metabolites and ranged between 19 and 77 % for fexinidazole, 8 and 59 % for M1, and 14 and 55 % for M2. Summary data are shown in Tables 4, 5 and 6. Between 0. 75 and 3. 15 % of the fexinidazole dose was recovered in urine over the 168 h measured (sum of unchanged and transformed drug), after oral administration of ascending single doses, indicating that the elimination route of fexinidazole is almost entirely extra-renal. The corresponding renal clearance ranged from 1. 23 to 6. 01 mL/h. The rate and extent of absorption of fexinidazole was less than dose proportional over the studied dose range (100-3,600 mg). When the administered dose was increased twofold, C max and AUC ? increased 1.
Acknowledgments The clinical studies were carried out by the  SGS Aster Clinical Unit (Paris, France)  with  Phinc Development (Evry, France ) performing pharmacokinetic and statistical analysis, and  A. Tarral  et al. manuscript preparation; the work was funded by the  Drugs for Neglected Diseases initiative (DNDi)  through donor grant from the  Bill and Melinda Gates Foundation. The donor had no role in study design, data collection, data analysis, data interpretation or writing of the report. The authors wish to thank the  Sanofi Research & Development and Access to Medicines departments  for their review of the initial study. We would also like to thank  Hana Bilak  and  Susan Wells  ( DNDi ) for help writing and editing the manuscript.
, 2002; Frishman, 2006). The unique AII cell and its connectivity thus remains of central interest in the evolution of mammalian rod vision. Our approach to this problem is based on automated TEM (ATEM) connectomics. ATEM connectomics enables the acquisition of rich synaptic maps by characterizing all partners and structural weights for all synapses and synapse types using connectome volume RC1. Connectome volume RC1 is a synaptic resolution ATEM dataset from rabbit retina spanning the inner nuclear, inner plexiform and ganglion cell layers of a sample field 0. 243 mm in diameter. It currently contains ≈890,000 annotations; ≈600 identified neurons; 6500 conventional and 13,700 ribbon synapses; 26,000 identified postsynaptic sites, over 3800 gap junction pairs, and 2280 adherens junctions; all assembled into 8600 identified presynaptic/postsynaptic partnerships.
MATERIALS AND METHODS The methods for connectome RC1 have been extensively detailed by  Anderson et al. (2009 Anderson et al. ( , 2011a,b),b). The RC1 dataset is freely available at connectomics. utah. edu. The associated software is available as free (SerialEM) or open-source applications (Nornir build manager, nornir. github. io/nornir-buildmanager), or via a free license (Viking and Viz web-services tools) for educational use through the University of Utah. The raw RC1 dataset is available on user-provided storage media.
VOLUME ASSEMBLY RC1 was created as previously described  (Anderson et al. , 2009 (Anderson et al. , , 2011b;; Lauritzen et al. , 2012). Briefly, the desired field on each grid was captured by SerialEM  (Mastronarde, 2005; Anderson et al. , 2009)  using a Gatan US4000 phosphorimaging camera. Each capture field is an array of ≈1000 tiles captured at 2. 18 nm resolution. Mosaics and 3D volumes were originally generated using the NCR Toolset (http://www. sci. utah. edu/ download/ncrtoolset). This code has now been superseded by Nornir (nornir. github. io/nornir-buildmanager/). CMP-to-TEM registrations are operator-guided with ir-tweak software from the NCR toolset. Re-imaging for optimized resolution and section tilt is performed using using high resolution (20,000-60,000×) goniometric tilt series.
IMAGE VIEWING, ANNOTATION, AND ANALYSIS Volume RC1 was visualized and annotated with the Viking viewer  (Anderson et al. , 2011a). The annotations trace 3D cell architectures as well as locations and dimensions of presynaptic, postsynaptic, adherens, and gap junction motifs as well as non-junctional touches are logged in the Viking database and visualized using VikingPlot, a compiled Matlab application that queries structure information from the annotation database and renders surfaces for display. VikingPlot exports formats for rendering of 3D data in a variety of free (e. g. , Blender, blender. org) and commercial applications. The annotation database permits standard SQL queries.
DATASET ANALYSIS Every cell annotation in RC1 is a 2D disc that is the largest inscribed circle contained by the close shape of the cell's margins (typically a star domain) in a given slice. Internal structures such as gap junctions, presynaptic specializations, postsynaptic densities (PSDs), etc. , are linked to a cell via child annotations: 2D discs representing the child structure's Feret diameter. These annotations, summed over slices, enable quantitative assessments of features (e. g. , gap junction and PSD areas) and 3D representations of cells and child structures. These data are accessed via Viking Viz  (Anderson et al. , 2011a)  or Microsoft SQL queries. Large queries were exported as delimited text files. We used AnalystSoft, StatPlus:mac (www. analystsoft. com) for statistics and histogramming.
mix of OFF amacrine cell and TH1 presumed ON synapses. Thus, the TH1 ON excitation is ≈1/3 the ON coupling excitation area. We can also parse the strengths of individual gap junctions for homocellular AII::AII and heterocellular AII::CBb coupling. The RC1 database includes includes 525 AII::AII and 172 AII::CBb coupling instances (Figure  13 ), with a mean ± 1 SD gap junction diameter of 267 ± 95 nm for AII::AII and 238 ± 95 nm for AII::CBb pairings. The mean for heterocellular coupling is only about 11% smaller, but due to the large sample size is still highly significant (2 tailed homoscedastic t-test, p = 0. 00052). More to the point, however, the largest gap junctions made by AII::AII pairings (745 nm) are 20% larger than made in AII::CBb pairings (592 nm) and the cumulative frequency distributions are significantly different.
Mrk 509 is one of the brightest Seyfert galaxies in the sky (L (1-1000 Ryd) = 3. 2 × 10 38 W), and it is considered one of the closest (z = 0. 034397) Seyfert 1/QSO hybrids. Because of its brightness and confirmed presence of UV and X-ray absorbers, Mrk 509 was the subject of an extensive multiwavelength campaign in 2009 described in  Kaastra et al. (2011b). The present Chandra data were taken in 2012. This paper is organised as follows. In Sect. 2 we describe the data reduction. In Sect. 3 we analyse the spectral lines, while in Sect. 4 we study the O viii line profile. The overall modelling of the outflow and the Fe-K complex are described in Sect. 5 and Sect. 6, respectively. In Sect. 7 we search for ultra-fast outflows in our spectrum. In Sect. 8 we discuss our results and, finally, we report our conclusions in Sect.
Data reduction Mrk 509 was observed by Chandra with the HETGS between 4 and 9 September 2012. The Chandra observation was split into two parts, separated by 19 h: observation nrs. 13864 and 13865, with exposure times of 170 and 99 ks, respectively. The data were analysed using the standard CIAO version 4. 4 tools. The spectra and response matrices were converted into SPEX  (Kaastra et al. 1996)  format, and that package was used for all spectral analysis. The standard High Energy Grating (HEG) and Medium Energy Grating (MEG) spectra are slightlyoversampled hence we binned them by a factor of 2 to about 1/3-1/2 of the full width at half maximum (FWHM) of the respective gratings.
Apart from the flux constancy during the Chandra observations in 2012, also the fluxed continuum spectrum of the Chandra observation over the 5-25 Å range agrees within 5% with the fluxed RGS spectrum taken in 2009. The similarity is even stronger: we have measured the equivalent width of the seven strongest absorption lines in common between the RGS and HETGS spectra, and found that they are the same within the error bars. The HST/COS UV band also shows no strong differences in flux between the 2009 and 2012 observations (less than 5% at 1360 Å). Given this similarity of the spectra, we will adopt for our modelling the same ionising spectral energy distribution (SED) as used for the analysis of the XMM-Newton data of 2009  (Kaastra et al. 2011b; Detmers et al. 2011).
5-26. 5 Å, and again with zero flux at λ = 6. 5 Å. In our spectral fitting, the splines representing the soft excess are allowed to vary independently for both datasets to account for weak time variability and cross-calibration effects, both of which are most important at lower energies where both instruments overlap. We keep the photon index of the power-law frozen to a value determined from a preliminary fit to the HETGS data. Because the RGS does not cover the λ < 5 Å regime, the precise value of the photon index is not important for RGS; any differences with the "true" underlying power-law in the soft band are compensated for by the spline. Furthermore, we add three broad Gaussian emission lines for the 1s-2p transitions significantly detected in the RGS spectrum: O viii, O vii and Ne ix, with their width frozen to the value obtained by  Detmers et al.
Fe-K region Ultra-fast outflows (e. g. Tombesi et al. 2010 ) have been previously reported for Mrk 509. Dadina et al. (2005)  report a redshifted system (+0. 21c if interpreted as Fe xxvi) in two out of five time segments of one of the six BeppoSAX observations taken between 1998-2001, as well as a blue-shifted system at -0. 16c in one of these time segments. In addition, they report an outflow system at -0. 19c in the XMM-Newton observation taken in 2000. Cappi et al. (2009)  reported three systems in three different XMM-Newton observations taken in  2000, 2005 and 2006. They have equivalent widths of 32 ± 12, 20 ± 6 and 20±7 eV at outflow velocities of -0. 172c, -0. 141c and -0. 196c, respectively, when identified with Fe xxvi 1s-2p. In the stacked XMM-Newton data taken before 2009,  Ponti et al.
(2009)  found evidence of an additional system with an outflow velocity of -0. 048c and with equivalent width of 13(-4, +2) eV (the error bars from both papers have been converted to 68% here). The lack of a similar absorption line during the non-simultaneous Suzaku campaign was interpreted by  Ponti et al. (2009)  as being due to intrinsic variability in the absorber properties. Moreover, the stacked XMM-Newton pn spectrum from the deep exposure taken in 2009 does not show any significant (>3σ) evidence of ultra-fast outflows in the Fe-K band  (Ponti et al. 2013). We have searched for the presence of absorption lines from highly ionised iron ions in the present HETGS data by adding to our best-fit continuum model discussed before absorption from Fe xxv or Fe xxvi at various redshifts. Because with the standard extraction that we used for our HEG spectra the spectrum for energies above ∼7.
Is the high-velocity component found by  Detmers et al. (2011)  real? To address this question, we show the RGS and HETGS spectra near the strongest transitions from Mg xi and Fe xxi (Fig. 9 ). Compared to the best-fit HETGS model, the RGS spectrum shows two data bins, each about 1σ below the model, at the blue side from the centre of the line. In a fit to RGS data alone, these tend to make the outflow velocity higher indeed, but the excess is not very significant compared to the HETGS model. For Fe xxi a roughly similar situation occurs, and combining this weak blue excess absorption for both lines creates the -715 km s -1 component. Our work shows that such a component is not present in the 2012 HETGS spectrum, but of course we cannot exclude that such a somewhat weaker, but transient component was present in the 2009 RGS data.
Abundances Our best-fit abundances do not differ much from those reported by  Steenbrugge et al. (2011)  based on the RGS data. We refer to that paper for a full discussion on the abundances. In general, inclusion of the HETGS data improves the accuracy, in particular for Ne, Mg, Si and Fe. The Mg and S abundances are somewhat closer to the proto-solar values by including the HETGS data.
Acknowledgements. This work is based on observations obtained with  XMM-Newton , an  ESA  science mission with instruments and contributions directly funded by  ESA Member States  and the  USA (NASA). It is also based on observations with INTEGRAL, an  ESA  project with instrument and science data centre funded by  ESA  member states (especially the PI countries:  Denmark, France, Germany, Italy, Switzerland, Spain ),  Czech Republic , and Poland and with the participation of Russia and the  USA. This work made use of data supplied by the  UK Swift Science Data Centre at the University if Leicester. SRON is supported financially by  NWO , the  Netherlands Organization for Scientific Research. J. S. Kaastra thanks the  PI of Swift ,  Neil Gehrels , for approving the TOO observations. M. Mehdipour acknowledges the  support of Studentship Enhancemement Program (STEP)  awarded by the  UK Science & Technology Facilities Council (STFC).