dataesr/scientific-paragraphs-categorization huggingface dataset:
Such data demonstrate that CTC may be characterized by a very high level of plasticity, which is presumably related to their capacity of creating distant metastases. These and further studies focused on heterogeneity of tumor cells in blood should be able to shed more light on the markers associated with their increased metastatic potential. From a technical point of view, CTC heterogeneity represents a challenge for CTC tests developers. In fact, the optimal CTC test would require the isolation of all types of CTC without any loss allowing to further perform their immune-molecular characterization. CTC heterogeneity also highlights the difficulty of using antibodies or cocktails of antibodies to isolate/identify all types of CTC as we lack specific markers expressed in all types of CTC and not expressed in non-tumor cells  [16].
Furthermore, ISET® has been shown to be more sensitive than CellSearch  [57, 59, 86, 88, 89, 94]  (Fig. 3 ). As a consequence, we will refer to these cells isolated by ISET and identified by cytopathology as Circulating Cancer Cells (CCC) in order to distinguish them from CTC. CCC are cancer cells extracted from blood potentially without loss and/or bias and diagnostically identified by cytopathology. It is very difficult to assess the sensitivity of different methods to isolate CTC based on published noncomparative data as they rely on variable non-standardized models and protocols. As an example, Vona et al. published in 2000 the results of a very accurate spiking test showing that the sensitivity threshold of ISET® is one tumor cells per ml of blood  [32]. This threshold level has been subsequently confirmed by several ISET® users  [86, 90, 92].
Taxon Sampling and Data Selection A total of 19 nuclear gene fragments were amplified for 39 taxa. Additional data for A. ornatus were downloaded from GenBank to bring the total number of taxa analyzed to 40. These nuclear gene fragments consisted of 12 exon fragments (ADORA3, APP, ATP7A, BDNF, BUF134, PNOC, PLCB4, TTN3, TTN5, TTN6, TTN7, and RGF2) and seven intron fragment (ABHD11, ROGDI, ACOX2, BGN, THY, STAT5A, and PRKC1) (supplementary table S1, Supplementary Material online). Of the 40 taxa analyzed, ten were outgroup taxa represented by two Yangochiroptera  (Teeling et al. 2005) , represented by the families Vespertilionidae (n = 1) and Mormoopidae (n = 1),  Foley et al. doi:10. 1093/molbev/msu329 MBE and four Yinpterochiroptera  (Teeling et al. 2005) , including the families Megadermatidae (n = 2), Rhinopomatidae (n = 2), Craseonycteridae (n = 1), and Pteropodidae (n = 3) (table  1 ).
The remaining 30 taxa are split between the rhinolophid (n =10) and hipposiderid bats (n = 20). The ten Rhinolophus spp. were chosen with regard to previously published phylogenies to differentiate between competing topologies. Our 20 hipposiderid taxa represent all nine of the currently recognized genera and include Anthops (n = 1), Asellia (n = 1), Aselliscus (n = 1), Cloeotis (n = 1), Coelops (n = 1), Hipposideros (n = 10), Paratriaenops (n = 2), Rhinonicteris (n = 1), and Triaenops (n = 2). As it was not possible to obtain DNA for Anthops, only three nuclear introns (STA5A, THY, and PRKC1) could be downloaded from GenBank. Hence, a phylogenetic reconstruction was specifically carried out only with these three introns to position Anthops. To overcome potential systematic bias resulting from cryptic diversity, where possible, all sequences were generated from the same sample for each species or from the same species collected in the same geographical area as the original sample.
All sequences newly generated are deposited into GenBank accession numbers (KP175738-KP176371) and concatenated with previously published data where possible (supplementary table  S3 , Supplementary Material online). In total, 40 species, including A. ornatus, from eight families were analyzed. Tissue Sampling, DNA Extraction, and Amplification Tissues used for genetic analyses were either from wing punches from released individuals or tissue samples from voucher specimens housed in different museum collections (see Genbank Accessions for further details). DNA was isolated using a modified salt/chloroform extraction protocol  (Miller et al. 1988) , which included an additional chloroform/isoamyl alcohol (24/1) step after the addition of the saturated NaCl solution  (Puechmaille et al. 2011).
Alignment Optimization Alignment optimization involves the removal of ambiguously aligned positions, which can occur partly as a result of insertion/deletion events, which have previously been shown to be phylogenetically informative  (Murphy et al. 2007 ). To compromise between alignment optimization and the loss of potentially phylogenetically informative sites through the removal of indels, single gene fragments were preliminarily aligned with MAFFT  (Katoh and Standley 2013). The program Repeatmasker Open-3. 0  (Smit et al. 1996)  was used to screen for and annotate indels prior to their downstream removal as part of alignment optimization. Single gene fragments were then re-aligned using the T-Coffee web server. The analysis was carried out with default parameters using a comparison of the Mafft, Clustal W, Muscle, and T-Coffee algorithms  (Notredame et al.
The resulting trees were collated using TreeAnnotator v1. 7. 2  (Drummond and Rambaut 2007)  with 10% discarded as burn-in to obtain a single representative tree for each gene fragment. To examine differences between different data types (exons vs. introns), single gene fragments were divided by data type and concatenated to form distinct data sets for further analysis: 1) exons, 2) introns, and 3) exons+introns. Preliminary analysis revealed topological incongruence between intron and exon data. Topological incongruence may be due to factors such as incomplete lineage sorting, hybridization, or different mutation rates, therefore it is optimal to identify these data and remove them to assess the true evolutionary signal in a data set  (de Vienne et al. 2012). Given that our data comprised of introns and exons, which evolve at differential rates, we performed a search for outlying data using Phylo-MCOA (de Vienne et al.
2012) with default settings on individual gene trees to resolve the topological incongruence observed between exon and intron data in preliminary analysis. A small number of sequences (0. 67%; 5/741; supplementary table S1, Supplementary Material online) were identified as outlying data and were removed Overcoming Impediments to Resolution. doi:10. 1093/molbev/msu329 MBE to optimize each single gene alignment. jModeltest was rerun as before for each gene fragment to obtain an appropriate model of sequence evolution, there was no change from previous results (supplementary table S1, Supplementary Material online). To evaluate the effect of removing outliers, we analyzed our data with and without outliers resulting in a total of six data sets for further phylogenetic analysis: 1) exons, 2) introns, 3) exons+introns, 4) exons-outliers removed, 5) introns-outliers removed, and 6) exons+introns-outliers removed.
Phylogenetic Analysis Phylogenetic analysis was carried out using RAxML  (Stamatakis 2006)  for ML and BEAST v1. 7. 2. (Drummond and Rambaut 2007) , MrBayes 3. 2. 1  (Ronquist et al. 2012) , and PhyloBayes 3. 3 incorporating the CAT model  (Lartillot et al. 2009 ) for Bayesian analysis. The RAxML analysis was carried out via the RAxMLgui v. 0. 95  (Silvestro and Michalak 2012)  using the thorough bootstrap method with 500 replicates. The analysis used a partitioned model in which each gene was allowed its own model of sequence evolution. The analysis carried out in BEAST was as described above for single gene trees. BEAST employs a single Markov chain in its analysis, whereas MrBayes uses two independent chains. To highlight any differences or avoid shortcomings in either program arising from these different tree-searching methods, we used both programs as representatives of Bayesian Analysis.
1. To assess further the convergence of the run, the analysis was repeated three times, resulting in the same topology. To determine the position of Anthops within our tree, a separate analysis was carried out on a subset of data, comprising only Hipposideridae species. The analysis was constrained to include only Hipposideridae species as it was important to maximize the number of confidently aligned positions available in this reduced intron data set to accurately determine the position of Anthops. Each gene fragment was aligned separately using T-Coffee  (Notredame et al. 2000) , all resulting ambiguous alignment regions were removed, and fragments were subsequently concatenated. jModeltest was used to determine the most appropriate model of sequence evolution for the concatenated alignment and AIC indicated a TVM + G model was best.
Molecular Dating Analysis The molecular dating analysis was carried out using the MCMCTREE program in PAML  (Yang 2007 ). The analysis was carried out on the exon+intron data set-outliers removed, as this represents the most optimized combined data set. MCMCTREE requires a completely bifurcating tree, and as such, the analysis was carried out on the tree resulting from the Bayesian analysis with BEAST from the same data set, because other trees contained polytomies. The analysis was run under the HKY85 model of sequence evolution, the most comprehensive model available in PAML. Analysis was carried out using both the independent and correlated rates models. The root age was set to 64-65 Ma following consistent recovery in the following articles:  Eick et al. (2005) ,  Teeling et al. (2005) , and  Miller-Butterworth et al.
Biogeographic Analysis Biogeographic analysis was carried out using the ML method implemented in Lagrange  (Ree and Smith 2008). Distribution data were gathered for all taxa in the tree from Bat Distribution Viewer (Polish Academy of Sciences, http://gis. miiz. waw. pl/webapps/thebats/iucn/, last accessed October 1, 2014) except for Rhinopoma hardwickii, which has recently been split into two species (table 1), whose distribution was recalculated in line with this revision  (Hulva et al. 2007). Distributions for all species were coded as eight possible areas: A) Europe, B) South America, C) South East Asia, D) India, E) Middle East, F) East Asia, G) Australia, and H) Africa (including Madagascar). Reconstructions were performed on the tree arising from the BEAST analysis of the exon+intronoutliers removed data set.
Diversification Analysis To facilitate our comparative analysis of diversification rate in the speciose genera Rhinolophus and Hipposideros, faster evolving mitochondrial markers Cyt b and Cox1 were used to enable species level differentiation. To explore if the rate of diversification was marker specific, all available Cyt b and Cox1 sequences for Rhinolophus and Hipposideros species were downloaded from GenBank  (April 30, 2013). Four data sets were prepared for the diversification analysis: 1) Rhinolophus-Cyt b; 2) Rhinolophus-Cox1; 3) Hipposideros-Cyt b, and 4) Hipposideros-Cox1. The four data sets were subsequently aligned using MAFFT v. 7 alignment server  (Katoh and Standley 2013). Sequences with 100% identity were identified and removed using CD-HIT server  (Huang et al. 2010). Because of the high number of sequences of conspecifics, sequences with 97.
5% sequence identity or above were removed from the data set in an attempt to keep only one sequence per species; 97. 5% was used as the cutoff as low intraspecific mtDNA sequence divergence between 1% and 2. 5% have been generally reported for bats  (Ditchfield 2000). Data sets were then visually inspected to remove misidentified sequences and trimmed leaving one outgroup species. jModeltest was used to identify the most appropriate model of sequence evolution for each data set using the AIC, which resulted in the selection of GTR+I+G variants for all data sets. Gene trees for each gene fragment were generated in BEAST  (Drummond and Rambaut 2007 ) using the same parameters as previously described. LTT plots were constructed for each data set and compared in R 2. 5. 2 (R Development Core Team 2012) using the APE package  (Paradis et al.
Performance Evaluation: Differential Resolving Power of Intron and Exon Data? To test whether exons perform better at resolving older nodes and if introns are better at resolving younger nodes, we compared the relationship between PP and height (referred to as node height and is defined as the average height of the clade across all trees in a BEAST run that support that clade) for intron and exon data sets. This analysis was carried out using the results of BEAST runs from analysis of the "exons -outliers removed" and "introns -outliers removed" data sets. It was not possible to compare directly the two outliers removed data sets (exons vs. introns) as the exon alignment was approximately three times larger (~7,000 bp vs. ~2,500 bp), hence it would be expected that the exons should have more resolving power simply because there are more data.
To overcome this, positions from the concatenated exon alignment were jack-knifed to form ten random exon data sets of 2,532 bp using custom R scripts. For each of the ten random exon data sets, jmodeltest indicated GTR+I+G was the best model. Each data set was analyzed in BEAST as described previously. We tested whether node height was related to PP using a general linear model with a binomial function and logit link. Differences in posterior probabilities for the same node between exons (average over the ten subsets) and introns were then tested in R using an asymptotic Wilcoxon Mann-Whitney rank sum test  (Hollander and Wolfe 1999). To investigate whether the results were specific to the jack-knifed data sets, we repeated the analysis using gene fragments. To obtain size comparable data sets, ten random combinations of four exon gene fragments.
Acknowledgments This project was supported in part by a  Science Foundation Ireland   PIYRA 06/YI3/B932  to E. C. T. and an  IRCSET-Marie Curie International Mobility Fellowships  in Science, Engineering and Technology to S. J. P. E. C. T. and N. M. F. are supported by the  European Research Council   ERC-2012-StG: 311000. For access to museum specimens, we acknowledge  Paula Jenkins  (BMNH). S. M. G. 's examination of specimens in the BMNH received support from the  SYNTHESYS  project http://www. synthesys. info/(last accessed December 6, 2014), which is financed by  European Community Research Infrastructure Action  under the  FP7 Integrating Activities Program. Fieldwork conducted by V. D. T was supported by  Vietnam National Foundation for Science and Technology Development (NAFOSTED)  under grant number  106.
244). With the advent of high and low fidelity patient simulators and multi-media clinical case simulation software (MMCCSS), the PBL concept has been updated from what was once a text-based only platform  (Maldonado, 2011). The DxR Clinician™ is software developed for PBL in the 1990s by the DxR Development Group. This web-based software provides multi-media clinical case simulations for problem-based learning for medical, physician assistant, and nurse practitioner students. The software provides patient cases with various multi-media modalities including audio, photos, and videos. One of the most useful features of the DxR Clinician™ software is its ability to calculate a clinical reasoning score based on the student's choice of history questions, physical exam, test/lab selections and a differential diagnosis.
Although the validity of these automated clinical reasoning scores is still under review  (Jerant & Azari, 2004) , educators can use the scores to track student progress in developing this competency. The DxR Development Group has also created similar programs for physical therapy and nursing students as well. Recently, a self-efficacy survey was administered to two cohorts of GW physical therapy students prior to their first clinical training internship. One cohort of students engaged in a series of four hospital-based computer assisted learning (CAL) modules similar to the DxR Clinician™ while the other cohort did not. Preliminary results reveal a significant difference in student self-efficacy regarding preparation for practice in a hospital based environment. Students who used the CAL modules were more confident in their abilities to practice safely and effectively in the hospital based environment as compared to students who did not (p <.
Standardized Patients Communication skills in healthcare should be treated and taught as any other psychomotor or procedural skill and competency should be demonstrated during the student's training  (Henry, Holmboe, & Frankel, 2013). At the GW Physician Assistant Program, technology has been used to assess a student's oral communication skills when interacting with standardized patients giving the student the opportunity to develop communication skills in a safe environment. A student will engage in a standardized patient encounter for a focused and common medical complaint, which is video and audio recorded. The student then delivers an oral case presentation (OCP)-also videotaped-which details the historical and physical exam data obtained as well as a formulated differential diagnosis and plan.
More recently, GW's Doctor of Physical Therapy program introduced informatics into the curriculum framed within the context of the Triple Aim of Healthcare Reform. The students were asked to consider the concept of systems skills including how data could be used to affect clinical practice and subsequent identification of problems and systems solutions. Ultimately, students appreciated that data input into an electronic medical record (EMR) and billing system could ultimately be used to improve quality and efficiencies in care. Clearly this content area requires further exploration within the context of a health education program in order to align with IOM competencies and meet societal needs. To prepare students to be leaders, innovators, and competent clinicians, they need to have an understanding of the increasingly salient role of informatics and specifically the use of graphical display such as dashboards.
One such tool is LibGuides -a content management platform created by SpringShare. LibGuides allow librarians to create a repository of curated links developed specifically to guide a certain sub-population of patrons (defined by department, major, topic of interest, etc. ). LibGuides supports patrons' needs at times when librarians are not available to give direct assistance. One example of the use of LibGuides in a health sciences environment is the creation of LibGuides that match the architecture of a medical curriculum to supply students with targeted resources based on their current topic of study  (Neves & Dooley, 2011). Another recent innovation in health sciences librarianship facilitated by the use of technology is the online 'embedded librarian' model. This refers to the placement of a health sciences librarian within an online component of a course via course management software.
Activities of the embedded librarian may include training students to evaluate and retrieve appropriate information resources, providing guidance with bibliographic management products, giving a point of contact within the library, and more. There is some evidence this service is useful for helping students feel more confident in their abilities to appraise research articles, search common article databases, conduct literature reviews, as well as other related information literacy and evidence-based practice skills  (Kumar, Wu, & Reynolds, 2014). At the GW Himmelfarb Health Sciences Library, the practice of embedding librarians in courses has grown rapidly in recent years. During the 2013-2014 school year alone librarians were embedded in over 60 different sections of online classes. Additionally, and often in connection with the 'embedded librarian' model, is the provision of live, online instructional sessions  (Konieczny, 2010).
Online Learning and Management Systems Online content delivery is no longer a foreign concept in healthcare professional education and the use of a learning management system (LMS) has become a mainstay of course organization programs. Commonly used platforms include commercial-based products (e. g. , Blackboard, Desire2Learn) as well as open source versions (e. g. , Moodle, Sakai, OLAT) most of which operate via a cloud-based system known as Software as a Service (SaaS). Content delivery can be easily accomplished with these systems in a traditional format (e. g. , syllabus and handout repository, online narrated lectures, and discussion boards) though most are beginning to incorporate Web 2. 0 strategies such as wikis, video conferencing, and other online collaborative technologies  (Ellaway & Masters, 2008; Hanss, Jemison, & Alrbight, 2008).
Background: The homeless population of France has increased by 50% over the last 10 years. Studies have shown that homelessness is associated with a high risk of premature death. The aim of this study was to estimate the number of homeless deaths in France between 2008 and 2010, using a reproducible method. Methods: We used the capture-recapture method to estimate the number of homeless deaths in France using two independent sources. An associative register of homeless deaths was matched with the national exhaustive database of the medical causes of death, using several matching approaches based on various combinations of the following variables: gender, age, place of death, date of death.
Results: The estimated number of homeless deaths between 2008 and 2010 was 6730 (95% CI: [4381-9079]), a number greatly underestimated by the two sources considered separately (less than 20%). Conclusions: In the absence of a register of the homeless deaths, the capture-recapture method provides an order of magnitude for evaluation of the resources that may be allocated by policy makers to manage the issue. Based on common and routinely produced databases, this estimate may therefore be used to monitor the mortality of the homeless population. Further studies about homeless mortality, particularly on the lead causes of deaths, are needed to manage this issue and to implement strategy to decrease the number of homeless deaths.
An exploratory study  [23]  identified two sources of mortality data on the homeless population at national level in France: the CépiDc (The French Epidemiological Center for the Medical Causes of Death, which reports to the French National Institute for Health and Medical Research) and 'Le Collectif les Morts de la rue' ('deaths in the street' association, subsequently referred to as the CMDR) databases. CépiDc collects all the death certificates for deaths in France and is responsible for the analysis of the causes of death. Some of the certificates indicate 'homelessness' but this status is not systematically reported. The CMDR specifically collects data on the deaths of homeless people in order to alert politicians on their health conditions. It is considered to be the most exhaustive source of homeless death records, although the completeness of the data is not known.
The CMDR database The CMDR collects data on homeless deaths in order to implement the means and actions needed for research and the reporting of violent causes of homeless deaths (street homeless and those living in homeless shelters), to ensure dignified burial, and accompany the people in mourning. The information collected is mainly based on various informal reporting circuits: associations addressing to homeless, institutions, relatives and media. Associations, institutions or relatives could report information about the death by filling a form, available on the CMDR website, or by informal means. In cases where the CMDR learn the death by media or when data are missing, the CMDR contacts services addressing to homeless people in order to learn more about the death. From January 2008 to December 2010, 1145 homeless deaths were registered in this database (source A).