dataesr/scientific-paragraphs-categorization huggingface dataset:
9, voxel size 1mm), and volumetric 3D fluid-attenuated inversion recovery (FLAIR) images reconstructed in the axial and coronal planes (TR/TE 6000/318, voxel size 1mm). In the five patients who underwent targeted spine MRI, sagittal T2-weighted TSE (TR/TE 3000/111, slice thickness 3mm) and T1-weighted TSE (TR/TE 465/11, slice thickness 3mm) images were obtained. Further data were obtained from medical records (including those of original presentation); all available electroencephalograms and MRI were reviewed. Mutation analysis and the electrophysiological consequences of identified mutations in these patients have been reported previously,  8, 9, 14, 15  as have the ophthalmological features. 16 lumetric analysis of MRI T1-weighted magnetic resonance brain images were available for volumetric analysis for four patients and eight ageand sex-matched comparison children imaged on the same scanner.
17, 20 An example of region identification by automatic segmentation is given in supplemental figure 1. Normative volumetric reference data were generated by segmenting images from healthy individuals aged between 20 years and 30 years (n=83) from the IXI database (www. brain-development. org). Labels of cortical regions containing grey matter or white matter portions (i. e. all regions except ventricles, central structures, cerebellum, and brainstem) were multiplied with a binary grey-matter mask. Regional volumes were normalized by intracranial volume. Images of patients and age-and sex-matched comparison children in this study were processed in an identical fashion. Individual region sizes are expressed as score values after z-transformation using the distribution of volumes in the reference database.
SIB-VMAT was associated with signifi cant reduction in the irradiated small bowel (SB) volume compared with 3D CRT CB for all dose range Ͼ 10 Gy (e. g. V 15 : 163. 5 cm 3 vs. 341. 3 cm 3 , p ϭ 0. 001 and V 40 : 43. 8 cm 3 vs. 85. 2 cm 3 , p ϭ 0. 008). With regard to bladder and rectum, SIB-VMAT showed a signifi cant sparing advantage at all dose levels with respect to 3D CRT CB retrieved plans. Moreover, overall OARs D mean were signifi cantly reduced by the SIB-VMAT (p ϭ 0. 001). According to CTCAE v. 4. 0, acute (within three months) GI toxicities were more frequent in 3D CRT CB versus SIB-VMAT (90. 0% vs. 66. 7%; p-value 0. 028). Conclusions. Compared to data from a historical database of patients administered 3D CRT CB, SIB-VMAT signifi cantly improves the dose conformity and sparing of OARs in HIR-EC patients undergoing postoperative radiotherapy.
Dosimetric verifi cation SIB-VMAT underwent to more accurate physical checks than 3D CRT CB plans. The delivered doses were measured using the Seven29 ion chamber array  [21]  and the Octavius phantom  [22]. SIB-VMAT plans were recalculated on phantoms representing the Octavius geometry and density and the doses were measured both on coronal and sagittal planes for every arc. A comparison of measured versus calculated dose distributions was carried out with PTW Verisoft software v4. 0, using the gamma index evaluation. Pretreatment dosimetry was considered optimal if the percentage of points fulfi lling gamma index criteria (P γ. 1 ) exceeded 95% using 3% for dose criterion and 3 mm for the distance to agreement criterion. Average and maximum values of the gamma index were also considered for comparison.
Our research group recently published an assessment of the impact of habitually different total fluid intake behaviors (low consumption: o1. 2 l per day versus high consumption: 42. 0 l per day) on multiple biomarkers of hydration status in freeliving conditions. 14 Hydration biomarkers in first-morning and 24h urine (24hU) collections, but not plasma osmolality, were significantly different between low and high volume drinkers, and urine osmolality, volume and specific gravity (USG) were distributed over a broad range of values. The study database also included individuals whose fluid consumption fell between the established low and high drinker thresholds, and who were therefore excluded from the previous low/high analysis. This presented us with an opportunity to further explore the associations between hydration biomarkers and total fluid intake over a wide range of fluid intake behaviors.
Experimental design This observational study evaluating fluid intake and urinary hydration biomarkers was carried out over the course of four consecutive evaluation visits scheduled Tuesday through Friday. Weekends were excluded to limit within-person day-to-day variation. Enrolled subjects were asked to eat and drink as usual, and record all food and fluid consumption using an online diary (e-diary; MXS Epidemio, France). Food and fluid item selections were made from a reference databank listing the nutritional composition of more than 1300 generic foods. 17 Beginning on Monday morning, subjects used the e-diary to record all food and fluid intake through Thursday night. Tuesday morning, subjects collected their first morning urine (FMU) at home and delivered it to the clinic. Study participants returned to the clinic over the next three consecutive mornings to deliver 24hU collections (24hU-1, 24hU-2, 24hU-3), which were checked for completeness.
The relative contribution of each predictor in fitting the model (variable importance for projection) was calculated for each (Table  1 ), using a cutoff of variable importance for projection40. 8 (SAS Institute Inc. ; SAS version 9. 2, Cary, NC, USA). Given that a model requiring a single measure would be most practical for widespread use, urinary hydration biomarkers with a variable importance for projection41. 20 were identified. These urinary biomarkers were used to generate simple linear regression models of fluid intake for comparison with the full PLS model. Using fluid intake and urinary data from a single collection day, the predicted versus observed values for total fluid intake generated from the simple linear and PLS models were assessed. Limits of agreement between observed and predicted total fluid intake were calculated using the Altman-Bland procedure.
Perhaps more important, the values for these biomarkers span a sufficiently broad range to show linear relations to fluid intake. This suggests that urinary biomarkers of hydration may have the potential to be used to approximate fluid intake volume in situations where collecting fluid intake data is difficult. Moreover, fluid intake records alone account for only one side of the water balance equation, and cannot account for daily water loss. Urinary biomarkers of hydration status, in contrast, provide information about the adequacy of fluid intake relative to body water losses, and are thus more indicative of adequate hydration status than relying on fluid intake alone. In the current study, data was collected during temperate environmental conditions, and the physical activity of participants was restricted to sedentary activities, thus limiting water losses.
Flow cytometry The HMCLs were stained with a conjugated-control monoclonal antibody (mAb), the anti-IGF1R-PE mAb, the anti-JAG2-APC mAb or the anti-C-KIT-PE mAb, and fluorescence was analyzed on a FACsCalibur flow cytometer with CellQuest software (Becton Dickinson, Paris, France).
Gene expression profiling and quantitative real-time PCR The gene expression profiling of HMCLs was previously reported. 16 We also used Affymetrix data (Santa Clara, CA, USA) from a cohort of 345 purified myeloma cells from previously untreated patients from the Arkansas Cancer Research Center (ACRC, Little Rock, AR, USA). The patients were treated with total therapy 2 that included high-dose melphalan and autologous stem cell transplantation (ASCT). These data are publicly available on the online Gene Expression Omnibus (Gene Expression Profile of Multiple Myeloma, accession number GSE2658. http://www. ncbi. nlm. nih. gov/geo/). Quantitative PCR was performed in duplicate using the TaqMan Universal PCR Master Mix (Applied Biosystems, Villebon sur Yvette, France) and the MX3005 instrument (Stratagene, Agilent Technologies, Massy, France).
Indeed, XG11 expressed a very high level of IGF1R (confirmed by flow cytometry). In contrast to IGF1, IL6 expression (Table  1 ) and the subsequent constitutive phosphorylation of STAT3 could not be detected in all but one HMCL, NCI-H929 (data not shown). Furthermore, clonogenic assays in the presence of 5 mg/ml of the blocking of anti-IL6R mAb Tocilizumab (Chugai), which prevents the binding of IL6 to the gp80 chain,  19, 20  did not significantly affect colony number (data not shown). Collectively, our data suggest that IGF1 but not IL6 is the main self-clonogenic growth factor for myeloma cell lines. SCF is a clonogenic self-growth factor for a subset of myeloma cell lines As the IGF1R-blocking mAb did not fully inhibit colony formation in several cell lines, we looked for other growth factor autocrine loops.
Reciprocally, P-AKT level was not decreased upon stable silencing of JAG2 (shJAG2), despite an inhibition of colony formation (Figure  3d  and Chiron et al. 15  ). Similarly, inhibition of C-KIT pathway by imatinib (20 mM) or of PI3K/AKT pathway by wortmannin (10 mM) did not induce a decrease in JAG2 expression in C-KIT þ XG11 or in C-KIT -KMM1 cells (Figure  3e ). These data showed that the inhibition of PI3K/AKT pathway by IGF1R silencing, imatinib or wortmannin did not induce a decrease in JAG2 expression, despite an inhibition of colony formation. ] [8] [9] [10] [11] [12]  To address the expression of C-KIT/SCF, IGF1R/IGF1 and IL6R/IL6 in primary myeloma cells, we used the Amazonia database, which contains public data from 345 patients at diagnosis and was published by Arkansas University.
d. of three independent experiments. (d) The blocking anti-SCF mAb inhibited colony formation. XG11, JJN3, KARPAS 620 and KMM1 cells were seeded for 21 days with or without 500 ng/ml blocking anti-SCF mAb. The colonies were counted, and the results are expressed as the percentage of inhibition of colony formation. The data represent the mean±s. d. of three independent experiments. (e) Western blot analysis of constitutive P-C-KIT in the presence of the anti-IGF1R and/or anti-SCF mAbs. Cellswere cultured for 24 h in serum-free RPMI 1640 supplemented with 0. 5% BSA, washed and further incubated for 30 h with or without anti-IGF1R (6 mg/ml) or anti-SCF (6 mg/ml), as indicated. The western blot analysis was performed as described in Figure1. (f) Western blot analysis of constitutive P-AKT and P-ERK in the presence of the anti-IGF1R and/or anti-SCF mAbs.
Cells were cultured for 24 h in serum-free RPMI 1640 supplemented with 0. 5% BSA, washed and further incubated for 30 h with or without anti-IGF1R (6 mg/ml) or anti-SCF (6 mg/ml), as indicated. The western blot analysis was performed as described in Figure1. (g) The PI3K/AKT inhibitor wortmannin inhibited colony formation. Cells were seeded 21 days before analysis, as described in Figure1a, with or without the addition of wortmannin (10 mM). The colonies were counted, and the results are expressed as the percentage of inhibition of colony formation. The data represent the mean ± s. d. of two independent experiments performed in duplicate wells. *Po0. 05, **Po0. 01.
ACKNOWLEDGEMENTS We thank  Dr T Guillot  ( Chugai, Neuilly, France ) and  Dr C Venot  ( Aventis, France ) for providing us with the blocking anti-IL6R mAb (gp80) Tocilizumab and the blocking anti-IGF1R AVE1642 mAb, respectively. This work was supported by grants from  Canceropole Grand-Ouest  ( AO2009   Cellules  souches).
, 2009; Wheeler et al. , 2010; Krenn et al. , 2011; Almanza et al. , 2012) , little is known about the feasibility of simultaneously using these monitors with preschool-aged children. Only two studies assessed the feasibility and validity of using GPS tracking devices to identify the location of preschool children  (Elgethun et al. , 2003 (Elgethun et al. , , 2007)). Neither study simultaneously assessed the child's physical activity. One study assessed indoor versus outdoor activity in preschool children using GPS and accelerometer, but did so only in the school context  (Tandon et al. , 2013). The aims of this study were to: (i) assess the feasibility to recruit participants and simultaneously obtain location and activity data on Hispanic preschool children in real life settings; (ii) assess the agreement of child location and travel mode as measured by GPS data loggers with parent-reported diaries as the validity criterion; and (iii) explore the utility of GPS monitors and accelerometers to identify locations where Hispanic-American preschool children were more physically active.
Participants Fifteen Hispanic-American children, 3-5 years old, residing in Houston, Texas were recruited via fliers posted in the community and at the Texas Medical Center, and from the Children's Nutrition Research Center's volunteer list. The average age of the children was 4. 7 (SD 0. 8) years; 60% were boys; and 53% of the families reported household incomes of less than US $50,000/year. Data were collected in January-February 2011.
Instruments and measures Monitors-Children simultaneously wore QStarz BT100X GPS data loggers (Qstarz International Co. , Ltd; Taiwan) and Actigraph GT3X accelerometers (Actigraph, Pensacola, USA) whose clocks were synchronised to the Universal Time Clock, for a 24-to 36-hour period on a day when the parent and the child were to spend the whole day together. Physical activity data were collected at 30 sec epochs and GPS data were collected at 5 or 30 sec epochs to test monitor memory abilities. The monitors were worn on two separate elastic belts each fitted on the child's hips, after noting that wearing both monitors on one belt (as recommended for adults; see  Kerr et al. , 2011)  became too heavy for younger children. Children were instructed to wear the monitors at all times during the study period except while swimming or bathing to prevent the monitors from getting wet.
Data processing and analyses There are no agreed upon data reduction algorithms for accelerometer data among preschool-aged children  (Cliff et al. , 2009). Previous studies in children and adults have elected to remove 10, 20, 30 or 60 min of consecutive zeros as non-wear time  (Masse et al. , 2005; Cliff et al. , 2009) , a decision which may influence the number of valid days included for each participant. In this study, accelerometer data were considered completed if there were 480 min of data after excluding consecutive zero activity for 30 min or more (i. e. nonwear time). Child physical activity was reported as activity counts per 30 sec epoch. GPS data were matched to valid accelerometer data. Data from both monitors were simultaneously processed using the Personal Activity Location Measurement System, version 1.
0. 6 (PALMS: https://palms. ucsd. edu:8443/ PALMS/)  (Patrick et al. , 2008; Kerr et al. , 2011; Demchak et al. , 2012). PALMS is an encrypted web application that simultaneously processes time-stamped accelerometer and GPS data to clean, filter and detect locations and travel mode based on study specified settings and established algorithms. Locations were identified in PALMS for GPS coordinates with a 100 m buffer where the participant spent at least 3 min. The locations identified in PALMS were viewed using Google Earth and coded as whether they matched the location recorded in the diary. The time spent in each location as assessed via PALMS output and the diary was compared. PALMS also identified the time a participant was taking a trip between locations. Trip parameters were set at travel of ≥30 m/min, with a minimum of 100 m for a duration ≥180 sec.
Results Parents were receptive to having their child wear monitors. All children were able to wear both monitors simultaneously and had valid GPS data. There were no apparent monitor removals by parents because they did not want their child's location tracked. Eighty percent (n=12) of children had valid physical activity data with a mean of 10. 0 hours (SD 2. 7), including one re-wear. These data were used in subsequent analyses. Invalid accelerometer data were primarily due to accelerometer initialization problems, rather than non-wear. The average correspondence between GPS-and diary-based estimates of location types ranged from 91. 9% to 99. 9% agreement with moderate to good Kappa statistics, ranging from 0. 49 to 0. 99 (moderate to almost perfect) (Table  1 ). The between-method (GPS monitors and parent reported diaries) correlation of estimated participant-aggregated min spent on vehicle-based trips was strong (Spearman correlation 0.
Acknowledgements We would like to thank  Drs. Kevin Patrick  and his research group at the  University of California, San Diego  for providing us access and very helpful assistance in using the PALMS software system to simultaneously process the GPS and accelerometer data. This work is a publication of the  US Department of Agriculture (USDA/ARS) Children's Nutrition Research Center, Department of Pediatrics, BCM  funded in part by the  USDA/ARS  (Cooperative Agreement  6250-51000-053-20S ). The content of this publication does not necessarily reflect the views or policies of the  USDA , nor does mention of organizations imply endorsement from the  US government. This project was funded by a R21 grant from  NIH-NICHD  ( 5R21HD060925 , PI: O'Connor). JAM was supported, in part, by a  career development award  ( K07CA131178 ).
The inserts show the precontraction applied before adding acetylcholine. Beta-actin and eNOS expression levels were measured using Western-Blot analysis (typical blots on the right side). Data is given as a ratio eNOS/beta-actin (C). Mean 6 SEM is presented (n = 10 per group). #P,0. 01, ZDF versus LZ. *P,0. 01, old versus young rats. doi:10. 1371/journal. pone. 0068217. g001 biochemical and immuno-histological analyses. Before sacrifice, a sample of blood was collected for analyses  [21]. In a separated series of experiments 12-month-old male LZ and ZDF rats were treated with the COX2 inhibitor Celecoxib (25 mg/kg/day, forced feeding once a day, 21 days) or with the combination of Celecoxib (25 mg/kg/day) plus 1-Oxyl-2, 2, 6, 6tetramethyl-4-hydroxypiperidine (tempol, forced feeding once a day, 10 mg/kg per day, 21 days)  [12, 22].
Detection of Reactive Oxygen Species (ROS) Using Confocal Microscopy Other arterial segments were embedded vertically in Tissue-tek (Sakura) and frozen in isopentane. ROS detection was performed on transverse cross sections 7 mm thick incubated with dihydroethydine (DHE) as previously described  [29]. DHE, in the presence of superoxide, is briefly oxidized to fluorescent ethidium bromide. Ethidium bromide is trapped by intercalation with DNA, and the number of fluorescent nuclei indicates the relative level of superoxide production. In negative control experiments arterial sections were incubated with Tempol before DHE staining  [29]. Positive control experiments were performed using MRA isolated from a rat treated for 6 hours with bacterial lipopolysacharide. Positive staining was visualized using confocal microscopy and QED-image software (Solamere Technology, Salt Lake City, UT)  [30].
Minimal set of clinical information accompanying the samples For the correct interpretation of genetic tests, a minimal set of clinical information from the referring physician or genetic counsellor must accompany the sample. The purpose of testing must be clearly identified (for example, confirmation of diagnosis that was established by other means, differential diagnosis of several disorders, carrier testing, presymptomatic testing and prenatal testing). The ethnicity of the individual tested should be included if relevant for the purpose of testing; this information may be useful to select the most appropriate test methods and to calculate residual risks. The referring physician should provide all clinical, laboratory, imaging, genealogic, genetic or other data that are relevant to the purpose of testing, and that may guide the laboratory in selecting the optimal laboratory procedures.
With the use of new high-resolutionand high-throughput sequencing techniques, the number of (copy number) variants of unknown clinical significance will increase, and the prediction programs and the databases with normal variants have their limitations. Thus, laboratory and clinical specialists should discuss and document what the minimal interpretation in the laboratory report should be. In the opinion of the Quality Committee, the report should at least contain all relevant information that enables the clinician to perform the clinical interpretation using literature resources (for example, which RefSeq genes are located in a copy number loss, or what the results are of prediction programs on a missense variant and whether or not it has been reported in databases like dbSNP). It may be worthwhile to inform the clinician that genealogical/family studies may be helpful in the interpretation, and that the interpretation is based on current knowledge and thus may change in the future due to additional evidence.
Although this is beyond the scope of these recommendations for reporting results of diagnostic testing, the committee acknowledges the need for curated locus/disease-specific databases and databases of copy number variants. Such databases should be supported by an (international) expert committee (as proposed by Tavtigian et al. 14  ). The report must provide a full and clear interpretation of results, depending both on the clinical context and the reason for referral (for example, diagnostic test, carrier test, prenatal test). Reports on patients may be read by a variety of professionals involved in their care, many of whom will be unable to interpret fully the results of genetic testing. In order to provide a full interpretation, the results must be viewed in the context of the relevant clinical and family information available (for example, relationship between the patient and the index case where relevant, family pedigree, ethnic background, clinical and laboratory data provided by the referring physician and others).
In some cases there may be more than one conclusion: Normal finding(s) (physiological finding, normal variation): findings within the range of physiological variation for the given individual considering ethnicity, age, sex, maturity and other relevant factors; for example, nucleic acid sequence corresponding to the reference sequence or containing genetic variants considered usual variations at the time of reporting, chromosomal heteromorphisms as defined in the ISCN, frequently occurring CNVs reported in the database for genomic variants (DGV), concentrations of analytes in quantitative biochemical genetic tests within reference ranges, results of qualitative biochemical genetic tests showing a usual result/profile. If relevant, it may be important to provide an estimate of the diagnostic sensitivity (that is, the proportion of affected individuals likely to be detected; or one minus the false-negative rate).
Attempts should be made to explain significance of the finding using literature, additional databases, prediction programs and other means, and to report the likelihood of the finding in respect to the clinical question asked. In some cases, it may be important to provide an estimate of diagnostic specificity, which indicates the risk of a false-positive result (for example, in late-onset diseases or in cases of reduced penetrance). When practical and appropriate, the advice of a Clinical Geneticist should be sought. Pathognomonic (disease-specific, pathological) finding(s): findings that are outside of the physiological variation and that are, at the present state of knowledge, unequivocally associated with a clinically relevant disease or group of diseases. Pathogenicity of findings should be clearly supported from data in literature/ databases, segregation analysis, functional tests of genetic variants and other relevant means.