dataesr/scientific-paragraphs-categorization huggingface dataset:
MATERIALS AND METHODS Data were pooled from the Ponatinib Ph1 ALL and CML Evaluation (PACE) trial  6  and the EBMT registry  10  to conduct an indirect comparison of ponatinib versus allo-SCT. PACE is a multicenter, international, openlabel, single-arm, phase 2 trial among patients with CML and Ph1 ALL who are resistant to or intolerant of dasati-nib or nilotinib or who have the T315I mutation. Of 449 patients enrolled in PACE from September 2010 to October 2011, 128 harbored the T315I mutation at enrollment. The EBMT registry collects data on demographics, treatments, mutations, and clinical outcomes in patients who undergo SCT. EBMT data were available from 2000 through 2010. Sixty-nine patients from the EBMT registry who underwent allo-SCT for CML and Ph1 ALL and were identified with the T315I mutation and TKI resistance at baseline were eligible for the study.
All 128 T315I-positive patients from PACE comprised the ponatinib group of this study. Fifty-six patients from the EBMT database comprised the allo-SCT group, because they had complete data for all variables used in the analysis. Both the ponatinib and allo-SCT cohorts consisted of T315I-positive patients aged 18 years in any phase of CML or with Ph1 ALL. Patients who underwent allo-SCT in their second CP were excluded, and no patients who received ponatinib were in their second CP. In addition, no patients in the EBMT database had received ponatinib before undergoing allo-SCT. The index date was defined as the date of intervention (the date of treatment initiation with ponatinib among patients from the PACE trial and the date of allo-SCT among patients from the EBMT registry). Patients were followed from the index date until the end of observation (the earliest of death, loss to follow-up, or the end of data availability).
DISCUSSION This study is the first demonstrating that patients with T315I-positive CP-CML who received ponatinib alone had significantly longer OS than patients who underwent allo-SCT. Conversely, patients with BC-CML or Ph1 ALL who underwent allo-SCT had better survival than those who received ponatinib alone, which was expected because multiple genes are activated and contribute to progression in these settings. Thus, although allo-SCT remains standard therapy for patients who have BC-CML at diagnosis or after TKI treatment, our results suggest that ponatinib alone is a valuable alternative to transplantation for prolonging survival in patients with T315I-positive CP-CML. Our study has several limitations, including very small sample sizes in each stratum (especially for the allo-SCT group in AP-CML and Ph1 ALL, rendering results for these phases inconclusive); residual confounding, because only variables that were common between PACE and the EBMT database could be adjusted for (eg, we lacked data on previous therapies and residual disease before intervention); selection bias; and missing data in the EBMT database to implement inclusion/exclusion criteria common to PACE or to examine cause of death and adverse events.
Eduardo Olavarria: Coordinated the research within the EBMT Chronic Malignancies Working Party, member of the EBMT, cared for patients and collected clinical data, assisted with data interpretation and data review/approval, and writing-critical review. Javier Pinilla-Ibarz: Investigator in the PACE clinical trial, cared for patients and collected clinical data, assisted with data interpretation and data review/approval, and writing-critical review. Jane F. Apperley: Investigator in the PACE clinical trial, member of the EBMT, supervised and contributed to data collection for the EBMT registry (UK), cared for patients and collected clinical data, assisted with data interpretation and data review/approval, and writing-critical review. Timothy Hughes: Investigator in the PACE clinical trial, member of the PACE Steering Committee, cared for patients and collected clinical data, assisted with data interpretation and data review/approval, and writing-critical review.
Dietger Niederwieser: Member of the EBMT, supervised and contributed to data collection for the EBMT registry (Germany), cared for patients and collected clinical data, assisted with data interpretation and data review/approval, and writing-critical review. Michael J. Mauro: Investigator in the PACE clinical trial, assisted with data interpretation and data review/approval, and writing-critical review. Charles Chuah: Investigator in the PACE clinical trial, supervised and contributed to data collection for the EBMT registry (Singapore), cared for patients and collected clinical data, assisted with data interpretation and data review/approval, and writing-critical review. Andreas Hochhaus: Investigator in the PACE clinical trial, member of the PACE Steering Committee, supervised and contributed to data collection for the EBMT registry (Germany), cared for patients and collected clinical data, assisted with data interpretation and data review/approval, and writing-critical review.
Giovanni Martinelli: Supervised and contributed to data collection for the EBMT registry (Italy), cared for patients and collected clinical data, assisted with data interpretation and data review/ approval, and writing-critical review. Maral DerSarkissian: Designed the research, assisted with data interpretation and data re-view/approval, contributed to writing-initial draft, and writing-critical review. Mei Sheng Duh: Designed the research, assisted with data interpretation and data review/approval, contributed to writinginitial draft, and writing-critical review. Lisa J. McGarry: Designed the research, assisted with data interpretation and data review/approval, contributed to writing-initial draft, and writing-critical review. Hagop M. Kantarjian: Investigator in the PACE clinical trial, cared for patients and collected clinical data, assisted with data interpretation and data review/approval, and writing-critical review.
values were computed using Wilcoxon rank-sum tests for continuous variables and chi-square tests or Fisher's exact tests for categorical variables, as appropriate. c Patients with missing values were excluded (N 5 1). d P <. 05. e Follow-up started at the index date and continued until the first of death, loss to follow-up, or the study end date. A few patients who underwent allo-SCT were missing a date of last follow-up (n 5 1), and the latest date in the database (January 27, 2015) was used. f A few patients who underwent allo-SCT were missing the date of T315I detection (n 5 2); the mean was computed among patients who had both dates known. g A few patients who underwent allo-SCT were missing the date of diagnosis or had a date of diagnosis that was the same as the index date (n 5 15), and the date they started imatinib use was used.
P values were computed using Wilcoxon rank-sum tests for continuous variables and chi-square tests or Fisher's exact tests for categorical variables, as appropriate. c Patients with missing values were excluded (N 5 1). d P <. 05. e Follow-up started at the index date and continued until the first of death, loss to follow-up, or the study end date. A few patients who underwent allo-SCT were missing a date of last follow-up (n 5 1), and the latest date in the database (January 27, 2015) was used. f A few patients who underwent allo-SCT were missing the date of T315I detection (n 5 2); the mean was computed among patients who had both dates known. g A few patients who underwent allo-SCT were missing the date of diagnosis or had a date of diagnosis that was the same as the index date (n 5 15), and the date they started imatinib use was used.
The real part µ gives an image of how much energy can be stored whereas µ gives an image of the losses. Two frequency-dependent complex permeability models able to be included into SPICE models for AC analysis are presented here. The first model is inspired by the magnetic field diffusion equation solution  3  and the second one is inspired by Nomura's work. 4 Both models are implemented thanks to behavioural sources and Laplace equations available in Spice softwares. Their implementation in LTSpice®will be explained in the second section. Results concerning the ability of each model to predict complex permeability versus frequency on two different materials will be shown on the third section as well as all simulation cases of the EMI filter to see the benefits of the approach on a realistic device.
V. CONCLUSION Two analytical models able to predict the frequency dependent magnetic complex permeability behaviour were analysed in this paper. After identification, Nomura's model showed that it has a better accuracy in the whole frequency range and is more robust (better adaptability to various materials). Nomura's model seems to be a better choice even if it is a bit more difficult to identify. This tendency has to be confirmed by further work on other materials like ferrite or iron powders. Both models have been successfully implemented in LTSpice® thanks to the tools available in the software, to simulate a realistic EMI filter. Both models showed that it is crucial to take into account the frequency dependant properties of magnetic material in the medium frequency range especially in common mode.
The European project HeritageCare aims to develop a methodology to help managers preserve historic monuments. The methodology developed integrates the advantages of HBIM (Historical Building Information Modelling): 3D visualization, grouping of information (history, diagnostics, videos, etc. ) under the same object, help with monitoring of maintenance, help with the planning of works, etc. This article develops in a first part the state of the art on the HBIM and its use: realization of numerical model (tools and database of objects), help with the maintenance, identification of the risks associated with the realization of the works. The second part is devoted to the presentation of the HeritageCare project: context and challenges, content and development of the HBIM part.
). The storage of information consists in grouping together in the form of a database attached to the digital model, all the useful information relating to a monument diagnosed: -Historical documents on construction, -Regulatory texts for monitoring and carrying out maintenance work, -Manual of maintenance and monitoring of the monument, -Methods of carrying out maintenance work, -Diagnostic reports made, -Etc. The graphic 3D modeling of a historic monument allows to increase the promotion of this monument and to make accessible to the general public inaccessible places since too vulnerable (crypt for example).
Conclusion This paper develops in a first part the state of the art on the preservation of historical monuments based on the HBIM. HBIM is seen as a way of bringing together all the necessary information (3D representation, history, diagnostics, videos, etc. ) to the maintenance. The research developed concerns the techniques used to obtain the 3D models (laser, photogrammetry, scan, radar, object libraries), the diagnostic assistance for the maintenance and the verification of the security for the realization of the works. In a second part, the context, the different levels and the development of HBIM as part of the HeritageCare project are developed. This project is moving towards the creation of a parametric object library, the creation of an information database (diagnosis, history, costs, etc.
The human microbiome is a diverse and complex ecosystem integral for healthy human development. Recent advances in next-generation sequencing technology have paved the way for a 'multi-omics' era of microbiome research, uncovering associations between microbial dysbiosis and disease. Our ability to harness the full potential of these 'multiomics' datasets are currently constrained by several technical, analytical, computational and bioinformatics factors. However, it may be possible to overcome such limitations through the use of novel systems biology thinking and approaches, to integrate and analyse these large 'multi-omics' datasets. Thus, the question arises -can systems biology approaches pave the way to a new era in microbiome research; determining underlying mechanisms in health and disease, and identifying key microbial interactions and causalities?.
In these models, whole-genome assembly data is used in an attempt to link annotated genes to functional categories, functional gene networks, host-microbial interactions, and microbial-microbial interactions  [3]. Such approaches, however, rely heavily on the quality of genome sequences and the availability of curated genome databases, as well as the quality of gene and genome annotation data. Here we discuss the current state of metagenomics research in the context of 'multi-omics' analysis and systems biology.
Main Text: Advances in next-generation sequencing technology, have led to the establishment of the field of metagenomics. In its simplest form, metagenomics refers to the study of the genetic material recovered directly from the totality of organisms present in an environmental sample or microbial community  [9]. In metagenomic studies, genomic DNA is isolated from the sample of interest, and randomly sheared before being shot-gun sequenced. The resulting output is a mass of short sequencing reads that need to be "trimmed" for quality, assembled, and mapped to gene databases allowing identification of the microbial population structure (taxonomy) and function (gene annotations). Despite there being numerous platforms available for metagenomic sequencing (Ie. Illumina, Ion Proton), there is a bottle neck for metagenomic studies in the lack of downstream resources for read mapping and subsequent bioinformatics analysis of the generated datasets.
< Developing Reference Gene Catalogues > In the early 2000's, international initiatives; the Human Microbiome Project (HMP)  [10, 11] , and the International Human Microbiome Consortium (IHMC)  [2] , were established to generate sequencing resources that would aid in the characterisation of the human M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT microbiome. One of the main aims of these initiatives was to generate and curate genome databases for taxonomic discrimination of microbial communities, as well as mapping and annotating a large number of entire microbial genomes (Figure  1A )  [10]. To date, even though there are several well established 16S rRNA gene databases (ie. SILVA  [12] , Greengenes  [13] ) which can be used for taxonomic binning of sequencing reads, the curated resources available for analysing metagenomics and 'multi-omics' datasets have been limited.
Unfortunately, GEMs are currently only applied to microbial networks, although a limited number of studies are beginning to integrate microbial datasets with both host and fungal networks. Figure  1C  shows a GEM as a well-connected network that can be applied for network dependent analysis using 'multi-omics' data or through constraints implementation assisting in determining the phenotypic potential of a target organism. GEMs are widely applied in constraint-based modelling, referred to as Flux Balance Analysis (FBA), to predict and interpret physiological data and moreover, used in design and discovery  [29, 30]. Like host modelling, the application of microbiome GEMs has been mainly evolved in two paths. Using the network properties and contextualizing of high-throughput data through mapping 'multi-omics' data to GEM to identify reporter metabolites and/or sub-networks  [31, 32].
Conclusions: The current focus of human host-microbiome studies is centred around generating 'multiomics' datasets to investigate the role of the microbiome in human health and diseases. Generating such data is remarkably important in the microbiome field for understanding the interactions between microbes and their host. A recent study has used GEMs of host and microbiome data on conventionally raised and germ-free mice and showed that global metabolic differences in mice tissues was influenced by the gut microbiome  [44]. This therefore highlights the benefits of systems biology approaches and its capabilities for describing mechanistic relationships in the microbiome and host-microbial interactions. This is a necessary step-forward in microbiome research allowing for a better explanation of the role of the microbiome in associated diseases.
86 mSv after the injection of 200 MBq 68 Ga-NODAGA-RGDyK. It needs to be acknowledged that the ED estimations for the reference person are potentially biased by the absence of experimental data on female, that is an undesired effect of our dosimetry study protocol, which was approved for the first five patients only. Therefore, it was necessary to derive the female ED from the residence times obtained in the male subjects who exclusively composed our cohort. Because of the limited number of patients, we did not calculate doses for patient-specific organ volumes. Rather, we computed doses according to OLINDA/EXM 1. 0 reference phantoms. This approach is reasonable in the low dose range typical of radiopharmaceutical used for diagnostic purposes in which the patient radioprotection is the main concern, considering the fact that ED is a metric used for assessing stochastic risk in a population.
The OIE has put in place the concept of wildlife focal points that could be from other national ministries to improve coordination and information sharing on wild animal diseases. The paucity of validated diagnostic tests available for use in most wild animals presents an additional challenge to determining current or previous pathogen infection and significance  (Cousins & Florisson 2005). While the OIE has traditionally focused on livestock, the growing intersection of wild and domestic animals and increasing global wildlife trade warrant a closer look at wildlife pathogen susceptibility. The OIE has demonstrated a strong commitment to integrating wildlife considerations into its disease reporting, prevention and animal health capacity-building efforts. To further this goal, EcoHealth Alliance collaborated with the OIE World Animal Health Information and Analysis Department to generate a database of host wildlife families with species proven to be susceptible to OIE-Listed diseases.
Materials and Methods An analysis of the peer-reviewed literature published in the English language between 1900 and 2014 was carried out for each of the 73 OIE-Listed terrestrial animal diseases (categorized according to Table  1 ) between May 2012 and July 2014. The disease name, causal pathogen(s) and any historical scientific names for the pathogens were used as keywords in separate searches in Web of Knowledge. Reported confirmed animal host-pathogen relationships (with the exception of avian hosts) for OIE-Listed diseases were recorded in our database. For this study, a 'host' was defined as a species for which at least one individual was documented to have been infected with a particular pathogen through diagnostic testing. Host-pathogen relationships reported in review articles were verified via the original peer-reviewed source.
Host-pathogen relationships established through experimental research (i. e. laboratory-induced infections) and originating from laboratory or zoological collections were excluded. For each host-pathogen relationship identified from the literature, the host species, pathogen, pathogen detection method (i. e. laboratory test applied) and scientific reference were recorded. The pathogen detection method was compared against the 'Prescribed and alternative diagnostic test for OIE-Listed diseases' in the OIE Manual of Laboratory Diagnostic Tests and Vaccines (OIE Manual). If the detection method fell within the OIE criteria or was considered more reliable (i. e. possessed a higher sensitivity and specificity), the host-pathogen relationship was considered confirmed and included in our database.
Host-pathogen associations were excluded if the detection method was deemed inadequate according to the OIE Manual or not stated. Hosts for which evidence was based on serology alone were only included if the serological test used was the OIE prescribed test, and it was noted whether the serological test included antibody identification vs. antigen detection. Once one valid reference was found for each host-pathogen relationship, further references for that relationship were not pursued. Although wild animals were the focus of the database, domestic animal host and human host records from the literature were separately noted to highlight the potential for cross-exposure.
Thus, while captive wild animals such as those held in zoos and rehabilitation centres are likely to be involved in international trade, it is noteworthy to mention that these host-pathogen relationship records were not included in our database. Finally, our records of host-pathogen relationship did not reflect prevalence, and any given documented relationship may represent findings of the disease in one animal, or thousands. Thus, these confirmed relationships should not be directly translated into quantitative risk of any given population of that species as carrying the disease, rather only potential host susceptibility at a species level. Our findings for several OIE-Listed diseases are notable in showing the range of wild animal species affected, and align with other recent findings. Miller et al.
the agricultural trade sector, countries can take note of the potential risks of wild animal importation and translocation in specific cases. Synergistic efforts across animal health, ecology and risk assessment may provide a more comprehensive understanding of the relative risks of various types of wild animal trade and relevant transmission pathways from this trade, and in identifying risk mitigation strategies where actual surveillance is not practical or feasible. While we believe this study and associated database are a critical first step, not all host-pathogen relationships indicate that a particular species serves an epidemiologically significant role in the transmission of disease. Reporting of infections in wild animals along with clinical and pathological findings will contribute to improved risk assessments for both wild and domestic animal species in the future.
The empirical TB parameters are usually generated by fitting TB calculations with DFT  12, 13  or experimental data. 14 Although the first study of graphene band structure was done by Wallace in 1947,  15  the concept of parameterized TB model was just introduced in 1954 by Slater and Koster. To calculate the energy bands of 2D graphene in low energy regions, it has been shown that the 2p z orbital TB model is relevant and allows us to describe important electronic properties of this material. 16 In the case when only the nearest neighbor interaction (1NN) is taken into account, the nearest neighbor hoping energy to be considered is t 1 = 2. 7 eV. 17  This simple 1NN TB model is basically accurate around the Dirac points (K points), but it poorly mimics DFT or experimental data in high-energy regions.
12 n 2002, Reich et al  12  have shown that the match between TB and DFT results for 2D graphene can be substantially improved by introducing a third nearest neighbor (3NN) TB model, including overlap terms. Indeed, the set of TB parameters proposed by Reich fits DFT predictions over a large range of energies. However, the solution for such a set is certainly not unique and in this model the values of the parameters seem to be fitted mathematically rather than physically as hoping and overlap terms of the third nearest neighbors are even larger than that of the second ones. Realizing this problem, Kundu  16  proposed to re-fit DFT data of Reich's paper and introduced a new set in which hoping and overlap terms decay as the neighbor distance increases. 9] [20]  However, DFT calculations  20  and experiments  21  have shown that all AGNRs are semiconducting.