dataesr/scientific-paragraphs-categorization huggingface dataset:
The only result close to significance was found for TLR2 C137A SNP in the Golden Retrievers. Due to the unpredictable direction of allelic frequency shift between dogs with SNA and controls, PLINK software was used to compute two-tailed Fisher's exact test p-values. Using a one tailed test would have led to half the actual p-value (0. 04; data not shown), thereby reaching significance. Although this might be interpreted as a significant result, the rationale for choosing a one-tailed test might be debated. Furthermore, multiple testing issues (i. e. performing several tests) could be considered, leading to a lowered significance threshold that would make this result not significant. On the other hand, the power of the test might be low due to the relatively small size of the sample. Therefore, this SNP could merit further investigation with a larger number of dogs.
fumigatus and culture of A. fumigatus from suspected lesions. A computed tomography examination of the head was performed in some dogs. Blood stored in ethylenediaminetetraacetic acid (EDTA) or biopsy tissue from the nasal mucosa were available for each dog and were used for extraction of genomic DNA using the Macherey-Nagel NucleoSpin blood (or tissue) isolation kit (ABgene, Epsom, UK). All biological material used to perform the present study was collected for diagnostic purpose in all dogs. Nevertheless, the right to use it was obtained by owner consent. Canine TLR2, 4 and 9 forward and reverse primers were designed via Primer 3 (http://bioinfo. ut. ee/primer3-0. 4. 0/primer3/), using the canine specific GenBank sequences, as described previously  [27]  (Table  7 ). Only primers for exon 3 were design for TLR9, because only two bases of exon 2 are coding.
Each assay was optimized with the synthetic positive controls and residual DNA from the samples used in the sequencing study. The qPCR was performed using GoTaq Colourless Master Mix (Promega Corporation, Southampton, UK) in a Stratagene Mx3005P (Agilent Technologies, Stockport, Cheshire, UK) with results analysed using the MxPro Software (ver. 4. 1; Agilent Technologies). Optimisation involved varying the annealing temperature (55, 60 and 65°C), with and without additional MgCl 2 (final concentration: 3. 0 and 4. 5 mM), to balance the Ct value difference between the target and non-target allele-specific PCR, and non-specific product formation with genomic DNA and water negative controls. The final reactions consisted of 200nM of each primer, 4. 5 mM Mg and 1:50,000 SYBR Green I (Invitrogen) with a thermocycling protocol of incubation at 95°C for 2 min for Taq activation and then 35 cycles of 95°C for 15 sec and annealing (55 or 60°C: Table  9 ) for 30 sec during which the fluorescence data were collected.
Samples were analysed using the QIAxcel ScreenGel Software (ver. 1. 2. 0; QIAGEN) and the standard DM500 analysis protocol. For one TLR4 SNP (T23C) we could not successfully optimise a set of allele-specific primers which could successfully genotype this base position. Both primers with and without the additional artificial base pair mismatch were tried, but an assay which would amplify DNA from dogs with the C allele could not optimised. Therefore, a Taq-man (hydrolysis) probe-based qPCR assay was designed using Primer3 and optimised against the synthetic positive control and residual DNA from the sequencing experiments. The assay comprised a forward primer (GCACAGAAAATGCCAGAATG), reverse primer (AGGTGAACCGACATGAAACC) and two allele-specific probes (FAM-CTACCCGCCTGGCTGGGATAT-BHQ-1) and (HEX-CTACCCGCCTGGTTGGGATAT-BHQ-1).
The genotyping was performed using GoTaq Colourless Master Mix (Promega) with 200nM primers, 100nM of each probe and 4. 5 mM Mg in a final volume of 25 μl. The same thermocycling protocol was used as for the allele-specific genotyping with a 60°C annealing temperature, but 40 cycles were performed with genotyping based on end-point fluorescence using the MxPro software. Reactions were run in duplicate for each sample with homozygous, heterozygous and no-template controls were run with each reaction.
Culturing in vitro has also shown the mycobacterium to have a preference for a low oxygen environment  [9]. These traits therefore indicate that the mycobacteria have a preference for environments with low light and oxygen levels. Identification of the bacteria in the environment has been generally isolated to parts of Australia where there have been significant outbreaks  [10] [11] [12]  or tropical regions of Africa where the disease is persistent  [4, 5, [13] [14] [15] [16] [17]. Whilst in South America human cases of the disease have been definitively present since 1969 as for example in French Guiana  [1, 18, 19]  and previous data suggests the presence of M. ulcerans DNA in environmental water sources  [18] , the DNA has never before been definitively identified in the environment. To ascertain whether environmental conditions where the pathogen is present in French Guiana are comparable to other continents and laboratory analysis extractions of DNA were taken from numerous freshwater bodies in addition to abiotic readings of the water.
This work is supported by  Laboratoire d'Excellence Centre d'Etude de la BiodiversiteAmazonienne  (grant  ANR-10-LABX-25-01 ) through its integrative research programme BIOHOPSYS on Biodiversity and infectious diseases, and a 3-years Bournemouth University PhD fellowship grant to AM. This research partially received technical support and funding from  ANR SEST Programme EREMIBA (Ref. 0010005 ) for JFG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Statistical analyses All data were expressed as mean (standard deviation) and categorical variables by percentage (frequency). Overall survival (OS) and disease-free survival (DFS) were calculated by the Kaplan-Meier method, and the differences between groups were compared using the log-rank test. To identify predictive variables of OS or DFS, continuous variables were analyzed by a Cox proportional hazards model and qualitative variables using the logrank test. Expectation maximization algorithm was used to determine the cut-off values of Yes and Yap. All analyses were performed using SAS software version 9. 2 (SAS Institute Inc. , Cary, NC 25513). A p-value <0. 05 was considered significant.
Acknowledgments Financial support:  ARC, Ligue contre le Cancer (Comité du Nord) ,  INSERM, SIRIC ONCOLille Grant INCa-DGOS -inserm 6041 and IRCL We are grateful to  Nathalie Jouy  ( Flow Cytometry Core Facility IFR114/IMPRT ),  M. H. Gevaert  and  R. Siminsky  ( Department of Histology, Faculty of Medicine, University of Lille 2 ),  M. Samyn  and  V. Dumetz  ( Laboratory of Immunohistochemistry, Centre de Biologie-Pathologie, CHRU-Lille ), and  Laurence Wicquart  ( "Tumorothèque du Centre Regional de Référence en Cancérologie" ).
Microscopy and data analysis Four-dimensional imaging experiments were conducted using an Axiovert 100M (Carl Zeiss Microimaging, Thornwood, NY) with the Lasersharp Confocal package (model 1024; Bio-Rad) with a 63× objective. Cells were wounded with a Micropoint pulse nitrogen-pumped dye laser (Laser Science, Mumbai, India). Brightestpoint projections, kymographs line scans, and intensity data were obtained using ImageJ software (National Institutes of Health, Bethesda, MD). Brightest-point projections are z-projections of maximum-intensity points from three-dimensional movies. Line scans are derived from a 10-pixel-wide line drawn through the wound edge, and lines were smoothened by averaging four neighbor data points and using a second-order polynomial with Prism 5. Distinct lipid domains were characterized by signal peaks ≥1 μm apart.
Methods This was a retrospective case study. Bronchoalveolar lavage fluid (BALF) samples from the ICL patient were used for in vitro culture for Candida, microscopy for Pneumocystis identification and quantitative PCR (q-PCR) for the detection of P. jirovecii DNA; candidaemia was also evaluated in this patient  (Chumpitazi et al. , 2011 (Chumpitazi et al. , , 2014;; Fricker-Hidalgo et al. , 2004). The q-PCR target used was the gene encoding the Pneumocystis major surface glycoprotein (MSG)  (Chumpitazi et al. , 2011; Cushion & Stringer, 2010; Khot & Fredricks, 2009). The amplicon DNA was sequenced, and pair-wise sequence alignments of the amplicons were performed using CEQ2000 DNA analysis software (Beckman). Further identification of MSG genes of P. jirovecii was performed by BLAST from both nucleotide and amino acid sequences of the three successive isolates from BALF samples with at least 99 % nucleotide identity for each.
Background Individuals with heterozygous familial hypercholesterolemia (heFH) have higher levels of low-density lipoprotein cholesterol (LDL-C) and are predisposed to premature cardiovascular disease. Alirocumab is a fully-human, monoclonal antibody targeted to proprotein convertase subtilisin/kexin type 9 currently in Phase 3 development for the treatment of hypercholesterolemia. Described here are three ODYSSEY Phase 3 trials, FH I (NCT01623115), FH II (NCT01709500) and HIGH FH (patients with heFH and LDL-C levels ≥160 mg/dL) (NCT01617655), in which alirocumab is further evaluated in the heFH population. Methods Multicenter, multinational, randomized, doubleblind, placebo-controlled studies have been designed to evaluate efficacy and safety of alirocumab in more than 800 patients with heFH who are not adequately controlled with a maximally-tolerated stable daily dose of statin for ≥4 weeks prior to the screening visit, with or without other lipidlowering therapy.
Alirocumab (formerly REGN727/SAR236553; Sanofi-Regeneron) is a fully human, monoclonal antibody targeted to PCSK9 currently in Phase 3 development for the treatment of hypercholesterolemia. In Phase 2 trials, alirocumab demonstrated significant reductions in LDL-C levels in patients receiving concomitant statin or statin plus ezetimibe therapy [NCT01288443; NCT01266876; NCT01288469;  21, 23, 24]. In these studies, alirocumab significantly (p<0. 001) reduced mean LDL-C levels by up to 72. 4 %, and also showed favorable trends in other atherogenic lipid parameters including lipoprotein(a) [Lp(a)], triglycerides, non-high-density lipoprotein cholesterol (non-HDL-C), apolipoprotein (Apo) B, and anti-atherogenic fractions HDL-C and Apo A1. Specifically, a Phase 2 trial demonstrated that alirocumab reduced mean LDL-C levels by up to 67.
Study Design All three Phase 3 ODYSSEY FH clinical trials are multicenter, multinational, randomized, double-blind, placebo-controlled studies designed to evaluate the efficacy and safety of alirocumab in patients with heFH not adequately controlled with their current LLT. The FH I study (ClinicalTrials. gov identifier: NCT01623115) is being conducted at 89 sites across North America, Europe and South Africa, and has a planned population of 471 patients. The FH II study (ClinicalTrials. gov identifier: NCT01709500) is being conducted at 26 sites across Europe and has a planned population of 250 patients. Both the FH I and II studies enroll heFH patients who have LDL-C levels ≥70 mg/dL at the screening visit and have a history of documented CVD, or heFH patients with LDL-C ≥100 mg/dL at the screening visit without a history of documented CVD (Fig.
1 ). The HIGH FH study (ClinicalTrials. gov identifier: NCT01617655) is being conducted at 33 sites across North America, Europe and South Africa, and has a planned population of 105 patients with heFH and LDL-C levels ≥160 mg/dL at the screening visit (Fig. 1 ). The studies are being performed in accordance with the ethical principles that have their origin in the Declaration of Helsinki and all applicable amendments laid down by the World Medical Assemblies and the International Conference Harmonisation guidelines for Good Clinical Practice. Institutional review board or independent ethics committee approval of the protocols and informed consent forms were obtained from each study site, and written informed consent was obtained from all patients.
Analysis Populations for Each of the FH Studies Primary Analysis The primary analysis population will be the ITT population and will comprise all randomized patients with at least one baseline calculated LDL-C value available and at least one calculated LDL-C value available between Weeks 4 and 24 (regardless of treatment adherence). The percent change from baseline in LDL-C at Week 24 will be analyzed using a mixed effect model with repeated measures (MMRM) approach. All available post-baseline data from Week 4 to 24 (on-and off-treatment) will be used, in which missing data will be accounted for by the MMRM  [28, 29]. The model includes fixed categorical effects of treatment group, randomization strata time point, treatment-by-time point and strata-by-time point interaction as well as the continuous fixed covariates of baseline LDL-C value and baseline value-by-time point interaction.
Safety Analysis AEs (including adjudicated CV events), laboratory parameters, and vital signs will be reported descriptively, based on the safety population (all randomized patients who received at least one dose or partial dose of study treatment). The safety analysis will focus on the TEAE period defined as the time from the first double-blind dose to the last double-blind dose of the investigational product +70 days (10 weeks). Patients entering the open-label extension study will be followed up to their last visit in the double-blind treatment period and their TEAE period will be truncated at this visit. Timing of Analysis The analysis will be conducted in two steps. The first step will be the main efficacy and safety analyses conducted as soon as all patients have been randomized and have at least all their data up to Week 52 collected and validated.
In this step, the final analysis of the primary and secondary efficacy endpoints up to Week 52 will be performed as well as the safety analysis on all safety data collected and validated at the time of the first analysis. Of note, the results of the first analysis will not be used to change the conduct of the ongoing study in any aspect. Since the analyses of primary and key secondary efficacy endpoints will be final at the time of first step analysis, the significance level for the study remains at 0. 05. The second step in the analysis will be conducted at the end of the study and will consist of the final analysis of Week 78 efficacy endpoints and the final safety analysis.
Acknowledgments The ODYSSEY FH studies were supported by  Sanofi  and  Regeneron Pharmaceuticals, Inc. The design and conduct of the study, as well as analysis of the study data and opinions, conclusions, and interpretation of the data, are the responsibility of the authors. Medical writing support in the preparation of this publication was provided by  Betty Thompson, Ph. D. , of  Prime Medica  and sponsored by  Sanofi  and  Regeneron Pharmaceuticals, Inc. The authors were responsible for all content and editorial decisions and received no honoraria related to the development/presentation of this publication.
Changes in CI before (T0) and after fluid challenge (T1) or ΔCI, were expressed as percentages. We calculated CI by using only TEE data  [13]. Additionally, LV systolic function was measured before and after fluid challenge by calculating the LV fractional area change (LVFAC) as previously described  [14]. A LVFAC <40% was considered as a LV dysfunction. Measurements of Dmax SVC , Dmax IVC and AoVTI were performed in triplicate over three consecutive respiratory cycles. The results are expressed as the mean of these three measurements. The mean interobserver and intraobserver variabilities in the measurement of Dmax SVC , Dmax IVC and AoVTI were 8 ± 7% and 5 ± 6%, 9 ± 9% and 6 ± 8%, and 8 ± 6% and 5 ± 4%, respectively.
After addition of 30 μL of an MTT solution (5 mg/mL) to each well, the well-plates were incubated for an additional 4 h at 37 °C. The medium was removed and the dark-violet formazan crystals were dissolved in dimethyl sulfoxide. The absorbance was determined at 560 nm using a microplate reader (Victor X3, Perkin Elmer, Waltham, MA, USA). To quantify cell viability, the ratio of the absorbance of the test samples to the absorbance of control cell samples (=100% viability) was calculated using Microsoft Excel software (Microsoft Corp. Redmond, WA, USA).
Dosimetric Calculations To estimate the equivalent absorbed dose for 149 Tb-cm09 in KB tumor xenografts, biodistribution data obtained with 161 Tb-cm09 were used. Based on these data the following calculations were made: (i) the cumulative radioactivity was calculated from integrated AUCs (MBq• s) of biodistribution data expressed in non decay-corrected percent injected activity [%IA] per tumor mass (assumed as 100 mg, due to an approximate calculated volume of the tumors at the start of the therapy); (ii) the adsorbed radiation dose in tumor xenografts was assessed for a sphere of 100 mg using the Unit Density Sphere Model from RADAR  [23] ; (iii) The absorbed dose (mGy/MBq) was calculated by multiplying the AUC (s; normalized to 1 MBq ID) with the S-value (mGy/MBq• s); and (iv) the dose (mGy) was calculated by multiplying the absorbed dose (mGy/MBq) with the amount of injected radioactivity.
Background: Therapeutic inertia has been defined as the failure of health-care provider to initiate or intensify therapy when therapeutic goals are not reached. It is regarded as a major cause of uncontrolled hypertension. The exploration of its causes and the interventions to reduce it are plagued by unclear conceptualizations and hypothesized mechanisms. We therefore systematically searched the literature for definitions and discussions on the concept of therapeutic inertia in hypertension in primary care, to try and form an operational definition. Methods: A systematic review of all types of publications related to clinical inertia in hypertension was performed. Medline, EMbase, PsycInfo, the Cochrane library and databases, BDSP, CRD and NGC were searched from the start of their databases to June 2013.
Search results The initial search of the databases resulted in a total of 2 946 abstracts: 1061 from Medline, 1732 from EMbase, 74 from PsycInfo, 77 from the Cochrane Library and database, and 2 from the other databases. After reviewing the abstracts, removing duplicates, and discussion to resolve differences in opinions, 145 abstracts were included: 84 from Medline, 51 from EMbase, 8 from PsycInfo, 2 from the Cochrane Library and database, and none from the other databases (duplicates were removed in this order). Systematic check of all the results retrieved in Google with the terms "clinical inertia" (162 results) and "therapeutic inertia" (142 results) did not lead to any new inclusion, and neither did the personal databases of the authors and the experts we contacted. A systematic search of the reference lists of selected articles retrieved 21 more abstracts.
Trials -Surveys and epidemiological studies -Qualitative research -Reviews -Opinion papers and editorials about the concept of inertia or about guideline-implementation issues Search strategy for identification of studies Databases The following databases were searched from their beginnings until June 2013: Medline, EMbase, PsycInfo, the Cochrane library and databases, BDSP (French Public Health Database), CRD (Center for Reviews and Dissemination), and NGC (National Guideline Clearinghouse). The search algorithm for Medline (via PubMed) was: ("guideline adherence"[MeSH Terms] OR ("practice guidelines as topic"[MeSH Terms] AND ("clinical audit"[MeSH Terms] OR "clinical competence"[MeSH Terms] OR "attitude of health personnel"[MeSH Terms] OR "delivery of health care"[MeSH Terms] OR "physician's practice patterns"[MeSH Terms] OR "nurse's practice patterns"[MeSH Terms])) AND ("hypertension"[MeSH Terms] OR "antihypertensive agents"[MeSH Terms])) OR "clinical inertia"[All Fields] OR "therapeutic inertia"[All Fields].
Introduction: Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Manual segmentation is time consuming and operator dependent. Automated segmentation as usually performed via single atlas registration fails to account for anatomo-physiological variability. We present, evaluate, and make available a multi-atlas approach for automatically segmenting rat brain MRI and extracting PET activies. Methods: High-resolution 7T 2DT2 MR images of 12 Sprague-Dawley rat brains were manually segmented into 27-VOI label volumes using detailed protocols. Automated methods were developed with 7/12 atlas datasets, i. e. the MRIs and their associated label volumes. MRIs were registered to a common space, where an MRI template and a maximum probability atlas were created.
Three automated methods were tested: 1/registering individual MRIs to the template, and using a single atlas (SA), 2/using the maximum probability atlas (MP), and 3/registering the MRIs from the multi-atlas dataset to an individual MRI, propagating the label volumes and fusing them in individual MRI space (propagation & fusion, PF). Evaluation was performed on the five remaining rats which additionally underwent [ 18 F]FDG PET. Automated and manual segmentations were compared for morphometric performance (assessed by comparing volume bias and Dice overlap index) and functional performance (evaluated by comparing extracted PET measures). Results: Only the SA method showed volume bias. Dice indices were significantly different between methods (PF. MP. SA). PET regional measures were more accurate with multi-atlas methods than with SA method.
Manual delineation of the hippocampus on an individual MRI image was used as an atlas for VOI extraction after affine registration  [13]. All these approaches have limitations. They may contain anatomical distortions due to the deformability of frozen brain sections or to ex vivo brain perfusion  [14]. They cannot allow for interindividual anatomical variability, being based on a single image, even if this is obtained by averaging several individual images; moreover, this in itself may lead to inaccurate delineation, as demonstrated in humans. A multi-atlas strategy for segmenting brain structures by propagation and fusion of several brains from a database has been described for the human brain  [15, 16] , and for Macaca fascicularis  [17]. This strategy has proved to be effective, and may be suitable for the rat brain.
Multi-Atlas Dataset Creation of the individual atlases. The 7 T2 MRIs in the Atlas group were manually labeled into 27 VOIs, each in native space. The MRI image and its corresponding label volume constitute one atlas of the multi-atlas dataset. The list of these 27 structures, following the nomenclature of the Paxinos atlas, is shown in Table  1. The delineation protocols created for this study can be found in the Appendix S1. VOIs were drawn using Display software (MINC, Montreal Neurological Institute, Montreal, Canada). Each volume was primarily drawn on coronal slices, with sagittal and horizontal slices used to confirm regional boundaries. All structures were delineated by one investigator (A. S. ) and checked by two others (S. L. , N. C. ), to ensure protocol conformity. Creation of the maximum probability atlas.