dataesr/scientific-paragraphs-categorization huggingface dataset:
No difference in the frequency and severity of OHSS was detected between the two treatment groups. Follow-up data showed no difference in term of frequency and type of adverse event occurred during pregnancy between the two treatments. Information was collected for 57 newborns (born to 44 mothers) in the Meriofert group and 60 newborns (born to 49 mothers) in the Menopur group. Abnormalities at birth were reported for two babies in the Meriofert group (congenital hand malformation with syndactyly for one baby and single umbilical artery for the other) and one in the Menopur group (reporting patent ductus arteriosus, intraventricular haemorrhage neonatal and neonatal respiratory distress syndrome). Tolerability at the injection site was found to be very good in both treatment groups, with few patients reporting pain (14 patients in the Meriofert group versus 18 in the Menopur group), redness (seven patients versus 14 patients), tenderness (three patients versus eight patients) or itching (one patient versus two patients).
Morphology digitization The skin morphology is explicitly digitized from reflective light microscopy observations. The skin tissue layers are modeled as separate continua of specific thermal properties taken initially from the literature in the preliminary model. The contours of the four layers constituting the epidermis and the two upper layer of the dermis are surveyed using Plotdigitizer  [51]  as shown in Figure  10 (c). A script in Python is then written using the digitized contours to feed the finite element code ABAQUS  [52]. A skin layer of thickness 200 µm is constructed for a 3D thermal FEM analysis. The sample average height and width are noted H and L and are about 1 mm. For each surgery, the morphometric measurements of the successive layers are taken and given in Table   Figure  10  shows the successive steps necessary to enhance the microscopic observation before the morphology digitization.
ACKNOWLEDGMENT I would like to express my gratitude to my gradute advisor  Dr. Elisa Budyn  for giving me this oppurtunity. Her guidance and motivation was a major element in the success of this thesis. It would not have been possible without her unconditional support. I would like to thank  U. S AirForce  for their research support. Also I would like to thank  Dr James Radosevich ,  Dr. Lacey  and their lab members for being helpful and assisting in providing us with the useful data. I would also take this oppurtunity to thank my lab-mates  Dr. Julian Jonvaux  and  Gayatri Mistri  for assisting me with the simulation work. I would like to thank my thesis defense commitee for being supportive and giving me their valuable feedback.
Other macroautophagy-enhancing agents A chemical screening approach has resulted in the discovery of three small-molecule enhancers of rapamycin (SMERs): SMER10, SMER18 and SMER28  [112]. These agents induced autophagy and enhanced clearance of mutant A53T α-syn in a PC12 cell line in an mTORindependent way  [112]. Although SMERs have shown promising potential in PD model systems, their specific biochemical pathways remain to be characterized  [113]. The antihistamine latrepirdine was shown to enhance autophagy  [114]  and to attenuate accumulation of α-syn both in vivo and in vitro systems  [115]. Latrepirdine entered phase 3 clinical trials for both Alzheimer's disease (AD) and HD  [116] , in which was well-tolerated without significant toxicity it failed to show efficacy compared to placebo (more information available on: https://clinicaltrials.
Data Analysis Data were analyzed using custom-written procedures in Matlab (MathWorks, Inc. , Natick, MA, USA). OIS was calculated using the first-frame subtraction approach: OIS(t) = (I(t) -I 0 )/I 0 , where I(t) -pixel intensity at the moment t, I 0 -time-averaged pixel intensity in the preconditioned baseline period (100 s). Resulting frames were filtered with a 10 × 10 median filter. Regions of interest (ROI) were selected as square areas near recording sites. OIS traces were calculated as the average OIS signal within selected ROIs. LFP signals were downsampled to 1 kHz. Continuous running line fit was removed using local linear regression in 300 s windows with a 10 s overlap [locdetrend function from the Chronux toolbox (http://chronux. org/)]. Amplitude of the LFP evoked response was calculated as a negative peak value of the LFP in the 100 ms after the stimulus relative to baseline level (average of the LFP in the 10 ms before the stimulus).
Data were smoothed by the 1,000-point moving average filter and the first derivatives were calculated. Local negative peak time of the first LFP derivative was calculated within the 20 s window preceding the negative AD peak. The value of the LFP at this time was taken as 100% and the previous time corresponding to 30% considered as AD onset. Velocity of vertical AD propagation was calculated from onset values as a distance between neighboring recording sites (100 µm) divided by AD onset delays between corresponding channels. The baseline level was calculated for each recording site as the mean value of the LFP in the -20 to -10 s time window preceding AD onset. AD amplitude was calculated as the maximal negative LFP peak from the baseline. Data from different slices were aligned by L4 and average amplitude and onset depth profiles were calculated.
Intracellular ROS Determination. Intracellular ROS generated in cells upon treatment by oxLDL or 4-HNE were evaluated by measuring the oxidation of H 2 DCFDA-AM  [35, 36]. 30 min before the end of the experiment, the probe was added to the culture medium (5 M final concentration) of HMEC-1 previously seeded on 96-well microplates. Cells were carefully washed twice with phosphate buffered saline (PBS), then 0. 2 ml fresh PBS was added to each well, and the fluorescence of CFDA was measured using a fluorescence microplate reader TECAN5 (excitation/emission 495/525 nm). The data are expressed as ratio of fluorescence/fluorescence of the unstimulated control.
Statistical Analyses. The results are presented as mean ± SEM of  experiments. Statistical significance was estimated by analysis of variance (SigmaStat 3. 5, Systat Software). When test for normality and equal variance (Kolmogorov-Smirnov) was passed, differences between means values were evaluated byone-way ANOVA (several experimental groups) followed by multiple comparisons by the Holm-Sidak test. Values of  < 0. 05 were considered significant.
Visual Field The subject's visual field consisted of a frontal white board, located 72 cm apart from the stool. A panel representing the tension generated on each load cell was screened on the board at the subject's eye level, using a video-projector (Figure  1 ). The signals were displayed as a percentage of the maximum voluntary contraction (MVC) in a left and a right numerical indicator representing each sensor, and designed in Labview software (National Instruments). The background color of these indicators changed from red to green when the subject reached the required value, enriching the basic feedback by a specific indication of the task to perform. The experimentation room was lit with artificial lighting to obtain constant brightness.
, Natick, MA, USA). Statistical analysis was performed using the Statistical Package for Social Sciences (SPSS) software V22 (Chicago, IL, USA). Normality of data distribution and the condition of sphericity were first checked using Shapiro-Wilk and Mauchly tests, respectively. A two-way repeated measures analysis of variance (ANOVA) was conducted for each dependent variable, with compressive load intensity (0%, 20% and 40% of the MVC) and laterality (bilateral, unilateral) as within-subjects factors. Values for the unilateral level were calculated from the mean between the left and right trials, as this study did not focus on specific left and right variations that might have resulted from handedness. When statistical significance was reached for compressive load intensity, the ANOVA was followed by a within-subjects analysis of contrasts (difference contrast) to compare the three levels of the independent variable.
For these reasons, hematologic data after three cycles are available for all patients, while blood counts after the complete sixcycle therapy are present for 32 patients (75%) only. After three cycles, four patients had grade III Hb toxicity; two of them discontinued the treatment while the other two continued under supportive therapy: after six cycles, one of them recovered while the other still met the grade III Hb toxicity criteria. Three further patients had developed grade III Hb toxicity after six cycles. Grade III leukopenia/neutropenia was observed after three cycles in one patient, who subsequently recovered. One more patient developed a grade IV leukopenia/neutropenia after three Ra-223 injections. In total, nine patients (19%) showed an at least grade III bone marrow dysfunction at some point in the course of Ra-223 treatment.
Statistical analysis All comparisons on data (ACF, inflammatory cells, Lymphocytes, Mucin cells, RNA levels as assessed by qPCR, etc) have been established using X 2 , Mann and Whitney no parametric tests and Anova tests when appropriate with significance set to P value < 0. 05. For Microbiota analysis, all statistical analyses were computed with the GNU R software (www. r-project. org). Jensen-Shanon matrix distance (JSD) between samples was computed from the abundance of each genus as described previously  [16]. The obtained distance matrix was then subjected to a principal coordinate analysis (PCoA) in order to estimate the major variation between samples. Temporal stability of the microbiota in GF mice, was assessed by comparing microbial Human Colon Cancer Microbiome Induced Changes in the Intestinal mucosa composition in mice fecal sample to the baseline (day 0) by using JSD distance metrics.
Co-inertia analysis revealed that clinical data(ACFs, mRNA levels and blood assays) was associated with fecal microbiota data (figure3, left panel). The first component of the analysis allowed a significant separation between HN-GF and HK-GF mice driven by both microbiota and clinical factor. This first component was significantly associated linearly with Bacteroides and Coprococcus genera (figure3, top right panel). Associations with clinical data was mainly driven by gene expression associated with cell renewal (Math1, HES1, ELF3 and KLF4) which co-varied together from ileum and colon tissues allowing a significant separation between HK-GF and HN-GF mice (figure 3, bottom right panel).
Conclusion In conclusion, we showed for the first time that transplantation of human intestinal microbiota from CRC patients to GF mice induced early carcinogenic changes through activation of Notch, Wnt/ β-catenin pathways. Further works are warranted to clarify putative mechanisms (host gene alteration, inflammation, metabolic, etc) involved in this fecal microbiota-related CRC predisposition to the recipient mice. ucts were determined with Picogreen method and to obtain equal amounts of each of the samples. 16S rRNA gene amplicons were sequenced on a Roche GS FLX 454 sequencer (Genoscreen, Lille, France) and processed with standard protocol from manufacturer. Data availble on line : http://github. com/tapj/comica. Text S2: Supporting text for ACFs assessment. In the present study, at day 42, large intestines were taken, opened, and left segments were carefully pinned flat, without folds, to examine the totality of colonic mucosa.
Acknowledgments: We thank  Denis Mariat  and  Nathalie Gallegos  ( INRA ) for their contribution in fecal DNA extractions and analysis,  Julie Villemot  and  Amine Amoura  for their technical contribution in gene sequencing and protein quantifications. Grant Support:  National institute of Research in Cancer INCA, Ile de France Branch (Cancéropôle, Ile de France  ;  Project Libre) ;  ACD (association Charles Debray)  ; Award Grant of UEGW 2011 of top basic research study (Stockholm October 24 2011) used for 16S rRNA genes pyrosequencing. 16S rRNA sequences files in fasta format are available here: http://www. bork. embl. de/~tap/tap_et_al_2014/.
Prediction of thermal conditions inside a vehicle cabin is still a challenge due to the fast transient behaviour of physical factors influencing the boundaries of the vehicular space. In order to gain knowledge and to propose new models of air flow and thermal characteristics on one hand and new adapted thermal comfort indices on the other hand, researchers need to perform parametric studies. In this case a CFD model can be a very powerful tool which let us simulate the environmental conditions in the vehicle cabin and test different strategies of ventilation and their impact on human thermal comfort. The challenge when using a CFD software is to produce results that can be trusted. This study tries to evaluate a simple approach of calibrating and validating a CFD model that reproduces the thermal environment and the flow dynamics inside a vehicular cabin.
PMV and PPD indices were also evaluated in both cases, using constant metabolic rate M= 1Met, constant cloth insulation resistance Icl = 0. 7 and constant relative humidity RH=60%. For the experimental case we considered a constant turbulence intensity Tu= 40% as prescribed by ISO 7730  [13]  in the case were the turbulence intensity cannot be measured. Comparing values thermal comfort PMV and PPD indices, some differences can be observed. The values recorded for the PMV display a "discomfort" indication in the direction of "cold sensation". The differences between the numerical and experimental data are amplified at some points (see Table  4 ) especially where slightly higher values of the air speed and smaller values of the temperature were found in the numerical model. We have to note that the range of the air speeds and of the temperatures are all corresponding to moderate environments as prescribed by EN ISO 14505/1 in order to apply the model from EN ISO 7730.
Acknowledgements This work was supported by a grant of the  Romanian National Authority for Scientific Research ,  CNCS ,  UEFISCDI , project number  PN-II-PT-PCCA-2011-3. 2-0512 (92/2012 ) The authors would also like to acknowledge the financial support for this work through the grant -Bourse Doctorale Régionale-from the  Région Bretagne, France.
Data sources and collection Surveillance data described in this article were reported within the framework of collaborative surveillance of TB in Europe through a network of national TB surveillance authorities  [11, 12]. Reporting has followed a standardised methodology allowing comparison between countries and over time  [13] [14] [15] [16]. Anonymous case-based data are now consolidated as part of the European Surveillance System (or TESSy) by the European Centre for Disease Prevention and Control (ECDC; www. ecdc. europa. eu), based in Sweden. Until 2007, the European TB data collection was coordinated by a dedicated network (EuroTB) coordinated by the French Institut de Veille Sanitaire, a WHO Collaborating Centre. review group and access will be granted if the request fulfils the requirements described in the Policy on data submission, access, and use of data within TESSy.
The characteristics of the cases for the variables of interest had a similar distribution to the one of the whole population before restriction; the only exception is in the proportion of cases from the Central European EU countries (53% in complete dataset and 74% after exclusions; Tables  1  and 2 ). Seventeen percent of cases had exclusively EPTB. Children and the elderly accounted for smaller proportions of the PTB (2% and 17% respectively) when compared with EPTB cases (10% and 20%). The ratio of males to females was 1. 2 among the EPTB cases, but was 2. 2 among the PTB cases. More of the EPTB cases were previously untreated for TB (95%) than PTB cases (81%). Only 6% of PTB cases originated from outside Europe, with most reported from Romania and other Central European EU countries. In contrast, a larger proportion of EPTB cases originated from Western Europe (23%), Africa (9%) and Asia (8%).
Discussion We explored pooled, case-based surveillance data reported by EU/EEA countries for twelve recent years to identify factors associated with the site of disease in notified TB patients. We observed that EPTB was more likely at the extremes of age, in females, in individuals not previously treated for TB, and in patients who were of African or Asian ethnicity. These findings are important for the post-2015 End TB Strategy with its stress on treating all forms of TB  [18]. Our analysis quantifies the differential effects of demographic and clinical factors on the disease manifestation using a multinational routine surveillance database which has been consolidated over several years through the long-standing commitment of a vigorous network of public health workers  [11]. Two important strengths of the dataset we used were its diversity in terms of patient mix in the countries of the EU/EEA and its size-more than half a million cases from 27 countries could be included in the main analysis.
tuberculosis genotypes which are more prevalent in some geographical areas  [29, 30]. Host predisposition for TB and for certain forms of disease based on human genetic characteristics have been described, although the relationship with any particular site was not strong  [31, 32]. In the dataset we analysed, severe forms of EPTB (CNS and disseminated) were relatively uncommon. Challenges remain to diagnose EPTB, including in well-resourced, high-income settings where TB is becoming increasingly rare  [33]. Delays in diagnosis and treatment of EPTB predispose to progression of disease. Maintaining a higher index of suspicion thus remains important, bearing in mind the protean manifestations of TB. The use of appropriate diagnostics is key, particularly molecular techniques which are now validated for the diagnosis of several forms of EPTB and for use in children  [34, 35].
Some limitations in our analysis should be highlighted. The authors had no means of independently verifying the quality of reporting and any differences in the validity of report between countries and over time. However, most institutions belonging to this European network have been using a fairly standardised reporting methodology for many years. The variables in the ECDC TESSy database do not include those for certain risk factors that are known to influence progression and form of TB disease, such as the delay to start of treatment, adequacy of treatment administered, diabetes, cancer, and other comorbidities, immunosuppressive therapy tobacco smoking, and alcohol consumption  [20, 21, [36] [37] [38] [39]. Moreover, incomplete information on susceptibility to isoniazid and rifampicin and on HIV status precluded the investigation of their potential associations with TB localisation within the scope of our analysis.
While it may be difficult to harmonise variables about modifiable risk factors like alcohol use and smoking within multinational databases such as TESSy, it may be feasible to do so at national level and explore associations which could guide frontline staff in their work. Finally, it may be useful in the future to use TB strain genotype data being collated within TESSy to explore transmission at the molecular level. In conclusion, it is likely that the predilection for TB site of disease is determined by a complex interplay between the microbiology and host genetic, behavioural, clinical and demographic features  [40, 41]. Our analysis sheds light on the determinants of TB localisation from a surveillance dataset spanning a broad swathe of the global population, extending well beyond the high-and middle-income settings of the western and central European countries reporting the data.
Acknowledgments The authors acknowledge the contribution of the nominated TB surveillance experts of the  EU/EEA   Member States  for providing the data to TESSy:  Daniela Schmid ,  Maryse Wanlin ,  Tonka Varleva ,  Maria Koliou ,  Jiri Wallenfels ,  Peter Henrik Andersen ,  Piret Viiklepp ,  Hanna Soini ,  Delphine Antoine ,  Bonita Brodhun ,  Georgia Spala ,  Gabor Kova  ´cs,  Thorsteinn Blondal ,  Joan O'Donnell ,  Maria Grazia Pompa ,  Irina Lucenko ,  Edita Davidaviciene ,  Irene Demuth ,  Analita Pace-Asciak ,  Erika Slump ,  Trude Margrete Arnesen ,  Maria Korzeniewska-Koseła ,  Antonio Diniz ,  Domnica Chiotan ,  Ivan Solovic ,  Petra Svetina ,  Elena Rodrı ´guez-Valı ´n ,  Jerker Jonsson ,  Maeve Lalor.
Data collection and outcomes Data were collected using questionnaires at the household, maternal and child level, during the baseline and endline surveys. Maternal questionnaires were administered on all mothers in a household with children 24-59 months of age at baseline, which given polygamy and household structure meant that some households had more than one mother questionnaire administered. At baseline, heads of household were asked about household characteristics such as household composition, cattle ownership, ownership of assets, and household food security using the HFIAS score  [12]. Maternal knowledge was assessed through a questionnaire asking mothers to list iron-rich foods and the consequences of anemia in children. Mothers were considered knowledgeable if they were able to mention at least 2 out of 3 iron-rich foods (i.
e. liver, fish or meat) and at least 2 out of 5 anemia consequences (i. e. difficulty at school, impaired development, slow growth, weak immunity defenses, and fatigue). Data on availability of the MNFY at the household level were collected at the two follow up surveys (F1 at 3 months; and F2 at 8 months following baseline) and at endline, through maternal recall. Mothers were asked if children from their household had consumed the MNFY, in the 3 days prior to the interview. Data on anemia (the study's primary outcome) was collected at all four data points (baseline, F1, F2 and endline) using Hb concentration measured through a finger prick using a Hemocue device 201+ (HemoCue Ltd, United Kingdom). HemoCue devices 201+ were cleaned and checked for accuracy prior each data collection, using blood samples to check for repeatability.
Anemia was defined as Hb below 11 g/dL and severe anemia, as Hb below 7g/dL. Children who were severely anemic and/or detected as suffering from severe acute malnutrition at any of the data collection points were referred to the closest health center. Mothers were given a small fee for transportation. Basic treatment (iron treatment, antiparasitic treatment, antibiotics) was provided at no cost to the mother or the health center, conditional on presenting the project's referral letter. Health centers were reimbursed by the survey managers for treatment costs after the surveys and data on children were collected. Data were collected using computer assisted personal interviews (CAPI) using CSPro software.
We compared main outcomes by intervention group using linear mixed-effects regression models with random effects at two levels-random intercept and random slope at the child level and random intercept at the cluster level of concession. We found that adding time as a random slope improved the fit of the model significantly using a likelihood ratio test. We used an unstructured covariance for the random effects at the child level, after comparing models with different covariance structures using the Akaike information criterion. Models for Hb and anemia included all children having a baseline measure of Hb, regardless of whether or not they were present at any follow up measurement, mixed models allowing missing data for repeated measures. Interactions terms (Intervention X Time) were used to assess the effect of the intervention on Hb and anemia (Intervention vs control) between baseline and different time points (F1, F2, endline) (baseline serving as reference).