dataesr/scientific-paragraphs-categorization huggingface dataset:
Phantom data The preparatory measurements collected EMT sensor data recorded in a phantom fixed to a male chest. The sensors measured a superposition of the movement along the catheter and the breathing motion. EMT measurements were performed according to the following protocol: • In the first measurement, the male proband tried to achieve a high breathing amplitude by chest breathing. • In the second experiment, he was breathing calmly via its belly. • In the last experiment, he was breathing while constantly speaking. The sensor was moved according to the treatment plan, which was defined based on the Xray CT images acquired while the phantom was fixed on the CT-table. Between the CT and the EMT-measurements, the phantom was removed from the CT-table and placed on the chest of the male proband.
Therefore, some displacements could have been arisen, because of the soft material of the prosthesis. However, we carefully avoided exerting any strain onto the phantom, hence we never could observe any displacement during the EMT measurements. The aim of this work was to remove patient movement-related signal components while precisely reconstructing the shape of the catheters from EMT measurements alone. The sensor signal as well as the breathing signal, which was measured separately on the patient's chest via fiducials, could be decomposed by applying an EEMD. Afterwards the signal has been reconstructed by adding up all IMFs which did not contain information about the breathing signal. The human eye is very well suited to grasp similarities between two signals. Therefore, as an additional cross-check, the algorithm was applied again to all datasets, but, instead of taking a similarity measure, an experienced data analyst decided which IMF should be deleted.
The goal of this visual inspection was to not delete any 'stop'-information and to get rid of breathing artifacts. This kind of analysis created kind of a ground truth against which the performance of the fully automatic algorithm has to be tested. To have an accuracy measure, the resulting dwell positions were compared with the dwell positions defined in the treatment plan. This could be achieved by an algorithm based on MDS (see Appendix 5 and references given therein). Thus, for each pair of dwell positions, a deviation value could be assigned, and, subsequently, an average over all deviations has been calculated. In Fig  8 , a reconstructed dataset is illustrated including the corresponding colorbar. The breathing mode was removed automatically from all three data sets obtained with the phantom.
Patient data Apart from the preparatory measurements with the breast phantom, EMT sensor data was recorded inside the catheters which were implanted into the breast of female patients. To remove the breathing motion artifacts contaminating the patient data, much as before the phantom data, the previously described algorithm was applied to the data, and different similarity measures were applied to identify breathing mode contaminations in the IMFs deduced from the EMT sensor signal. First, the well-known Pearson correlation coefficient (PCC) was used to point-wise measure linear similarities. Next, the density-based Kullback-Leibler divergence (KLD) was employed to account for higher order correlations as well. As a third similarity measure representing a real metric, the Jensen-Shannon divergence (JSD) as a smoothed and symmetrized version of the KLD was used.
Finally, the data was reconstructed also while data similarity was judged by a human expert. After reconstructing the data in four different ways, the dwell positions were compared with the dataset provided by the treatment plan. Applying the MDS-method, any mismatch of each pair of dwell positions could be quantified resulting in a sensitive detection of catheter displacements and bendings. A nice example is given in Fig 10  which  shows EMT measurements of a female patient immediately after the X-ray CT has been taken (left graph) and hours later when the patient has been moved meanwhile to the EMT treatment room (right graph). The EMT measurements performed on the CT bench still do nor show any catheter deviations from the treatment plan. However, the second EMT measurement shows a clear catheter shift of one of the catheters, while for all others CT and EMT data coincide almost perfectly.
Only small differences between the three different ways of reconstruction are observed. The mean deviations for the three phantom measurements are almost identical. Consequently, between the acquisition of the CT and the measurement on the male chest, a displacement of roughly 2. 3 mm occurred. It is especially noteworthy that values resulting from the Jensen-Shannon divergence do not deviate much from the results of other similarity measures, although by employing the JSD, the breathing mode artifact could not always be removed completely. This is because the breathing mode amplitude is in most patients small compared to the dissimilarities which happen. Only for patient 1, the mean deviation is larger when determined using the JSD than when using other similarity measures. In summary, the automatic removal of breathing artifacts worked very well for each dataset.
Conclusion High Dose Rate Brachytherapy (HDR-BT) encompasses the irradiation of a tumor bed after the cancerous tissue has been removed during a surgical intervention (Lumpectomy). During radiation therapy, a high activity radiation source will be positioned in close proximity to the tumor bed via catheters which are implanted surgically into the female breast. The spatial position of the radiation source inside these catheters is determined via an electromagnetic tracking (EMT) device. During radiation treatment, the source is moved inside the catheters while stopping at pre-defined dwell positions for predefined dwell times. The latter data are established, relative to the patient's anatomy, from an initial X-ray computed tomography (CT) image by a medical physicist. During the radiation treatment sessions, which are scheduled during one week roughly, catheter displacements occur due to patient movements, breathing etc.
Assuring a proper radiation treatment in accord with the treatment plan, source positions need to be ascertained each time, a treatment session is scheduled. Such control measurements are performed using solenoid sensors instead of radiation sources whose movement inside the catheters follows the treatment plan and whose spatial positions are determined with an electromagnetic tracking (EMT) device. The current study develops an automatic analysis tool of such EMT data sets, recorded with a solenoid sensor, to either corroborate concordance of the source movement with the treatment plan or precisely quantify a spatial displacement that might have been occurred. The tool combines machine learning techniques such as • a particle filter (PF) to precisely track the dwell positions of the solenoid sensor inside the catheters implanted in a female breast, • a singular spectrum analysis (SSA) as a proper technique for removing high amplitude artifacts such as resulting from measurement device malfunctioning, • an ensemble empirical mode decomposition (EEMD) to identify EMT signal components related to movement artifacts, especially breathing mode contributions even if the latter are highly erratic due to constantly speaking probands, • linear and non-linear similarity measures such as Pearson correlation, Kullback-Leibler divergence and Jensen Shannon divergence to identify breathing mode contributions within EMT sensor signal recordings, • a multi-dimensional scaling (MDS) to match positional measurements referring to different coordinate systems.
Approximately 90% reduction in expression of cytochromes P450 was observed in HepG2 and Upcyte cells, and approximately 60% in HepaRG cells relative to cPHH. Drug transporter expression was also lower compared with cPHH with the exception of MRP3 and P-gp (MDR1) which appeared to be significantly expressed in HepaRG cells. In contrast, a high proportion of Nrf2-regulated proteins were more highly expressed in the cell lines compared with cPHH. The proteomic database derived here will provide a rational basis for the context-specific selection of the most appropriate.
The scope of this work was to assess the relative differences in protein expression between the cell lines and not to provide absolute quantification as has been done for a selection of DMETS in liver tissues and primary hepatocytes in recent years  (Ji et al. , 2012; Ohtsuki et al. , 2012; Schaefer et al. , 2012; Xu et al. , 2014). Furthermore, the aim of this study was not to rank the cell lines, but rather to deliver a comprehensive protein expression dataset that provides a useful resource for the contextspecific selection of the most appropriate cell type for a given purpose. It is hoped that this information will provide the basis and direction for a rational approach to the development of new, more sophisticated in vitro models for the prediction of DILI and facilitate the interpretation of the derived data.
0 (AB Sciex) using the Paragon algorithm and the UniProt/SwissProt database. The data were normalized for errors due to differential sample loading and co-eluting peptide signal by bias and background correction, respectively. A fixed modification of MMTS-labeled cysteine was used, biological modifications were allowed, and the detected protein threshold was set to 10 %. Ratios for each iTRAQ label were obtained using a pooled sample as a reference which consisted of combined aliquots of each individual sample tested.
Data analysis Pilot Descriptive Statistics version 2. 6. 2 (Sciex) was used to assess the quantification false discovery rate (FDR) for each of the iTRAQ datasets. An internal replicate of a pooled sample containing all the different cell types was included in 2 of the iTRAQ lanes (119 and 121) to enable the intra-assay consistency of the quantification to be evaluated. The analysis revealed that in all 3 datasets, a 1% FDR was equivalent to a p-value threshold for protein quantification of. 01. Using this value, only 6 (0. 16%), 4 (0. 09%), and 3 (0. 07%) proteins exhibited significant fold changes in the 119:121 ratio across the 3 runs, respectively. Raw p-values were generated using a 2-tailed t test, and were subsequently adjusted for multiple hypothesis testing using the Benjamini and Hochberg correction.
Heat maps were generated using the programming language R-3. 0. 1 (http:// www. r-project. org/). Hierarchical clustering was calculated using Euclidean distance metric with complete linkage. Canonical pathways were generated using Ingenuity Pathway Analysis (IPA) software (Ingenuity, Mountain View, California; www. ingenuity. com) using inputs of gene identifiers, log 2 fold changes, and p-values between 2-group comparisons.
RESULTS Global Protein Expression in HepG2, Upcyte, and HepaRG Cells Relative to cPHHs iTRAQ-based relative quantification of protein profiles were determined for HepG2, Upcyte, and HepaRG cells, and compared with the protein profile of cPHH. A total of 4696 unique proteins were identified across the 4 cell models, of which 2722 were common and quantified in each of the 3 independent biological replicates of every cell type at an FDR of < 1% (Table  1 ). A list of the Phase I, II, III, and Nrf2-regulated proteins for which relative quantification was obtained in every cell type is presented in Supplementary Tables  S2-S5. Representative examples of mass spectra obtained for 4 DMET proteins of relevance to DILI (Cyp2E1, GSTmu, BSEP, and MDR1) along with the iTRAQ reagent motif used for relative quantification across the cell lines, are included in the Supplementary Data (Supplementary Fig.
In order to identify common pathways that were most significantly represented in all 3 cell lines, canonical pathway analysis was undertaken using IPA. IPA calculates the significance value of a given canonical pathway as the probability that the pathway or function is associated with the data set by random chance. This is done by considering the number of proteins in the experimental data set that participate in that process, compared with the total number of proteins that are known to be associated with that process in the selected reference set (Ingenuity Knowledge Base). Table  2  shows the 20 top canonical pathways based on the total protein group analyzed and includes the log 2 fold change in expression for each of these pathways in each of the 3 different cell lines relative to the cPHH.
Epidemiological investigation and case definition Clinicians in Dublin provided information on mode of transmission for new HIV diagnoses, and notified new HIV diagnoses in PWID immediately to the DPH. Clinicians asked all cases about the use of snow blow and at-risk sexual practices and whether they were homeless. Cases were crosschecked with data from the Dublin Region Homeless Executive's accommodation client database for periods of accommodation between 1 October 2013 and 30 June 2015. We defined a case as HIV diagnosis in PWID in Dublin reporting first HIV-positive test since January 2014: a confirmed case had recent HIV infection (one or more of: p24 antigen-positive, history of negative HIV test within 12 months of first positive test, acute seroconversion illness, or recency assay test); a probable case had an HIV diagnosis of unknown duration but with an epidemiological link (sexual intercourse or drug sharing) to a confirmed case.
The NDTC has a remit for providing OST for homeless PWID, but responding adequately to the needs of these PWID remains challenging. In  There were limitations in undertaking this investigation. Cases and controls were difficult to recruit among homeless chaotic PWID, exposed to illegal and at-risk behaviours. In the descriptive epidemiology, homeless status was determined by asking the patients if they were homeless, and by crosschecking with the homeless service database. If however homeless persons didn't register for any homeless service they might not have been identified as homeless. For the analytical study, controls identified by NDTC as homeless and chaotic were included in the study. However, we found that the homeless status of some participants had changed with a minority no longer reporting homelessness, although all remained chaotic.
Acknowledgements The authors wish to thank all those who participated to the study, all interviewers from NDTC, GUIDE clinic,  St James 's hospital, Curlew road clinic, Dochas centre, the  Mater hospital, Thompson Centre, Ms Áine Shine from NDTC Laboratory  for testing and analysis of urine samples,  Bernie O'Donoghue Hynes Head  of  Research and Pathie Maphosa Research Assistant  from the  Dublin Region Homeless Executive. Authors would also like to thank  Patricia Garvey EPIET  fellow at HPSC for her advice during the analysis, Public Health England for sharing their examples of questionnaire related to drug consumption, and for their Virus Reference Department's (Colindale) assistance with HIV recency testing, and  Alicia Barrasa Blanco , EPIET coordinator, for her advice in designing the questionnaire.
Western-blot analysis Protein extracts were prepared from HepaRG cells as previously described  [25]. Proteins were then separated on polyacrylamide gels and electrophoretically transferred to nitrocellulose membranes. After blocking in Tris-buffered saline containing 4% bovine serum albumin, membranes were incubated overnight at 4°C with primary antibodies directed against target proteins. Peroxidase-conjugated monoclonal antibodies were thereafter used as secondary antibodies. After washing, immunolabeled proteins were finally visualized by chemiluminescence. Gel loading and transfer were checked by staining membranes with Ponceau red. Intensities of antibody-stained bands and Ponceau red-stained lanes were measured by densitometry using ImageJ 1. 40g software (National Institute of Health, Besthesda, MA), allowing to normalize antibody-related staining to Ponceau red-labeling  [29].
For example, the effects of other herbal extracts such as sage are greater in older than younger individuals  (Scholey et al. , 2008) , as are the effects of glucose administration (e. g. Macpherson et al. , 2014). Therefore, a logical step in evaluating the cognition-enhancing potential of pharmaceuticals, or nutraceuticals such as P. quinquefolius is to evaluate their effects in a slightly older cohort including, initially, with acute dosing. The Cognitive Drug Research (CDR) battery has a large database of normative data, which indicates that working memory begins to decline in middle age  (Wesnes, personal communication). The battery has been used in approximately 1400 clinical trials-many involving the cognitive and mood effects of nutraceutical interventions, including P. ginseng  (Kennedy et al.
Procedure Each participant was required to attend the testing labs on a total of three occasions (one practice and two study days), conducted seven days apart to ensure sufficient wash-out between conditions. Testing took place in a suite of dedicated laboratories at the Centre for Human Psychopharmacology with participants visually isolated from each other. Participants were also wearing headphones to minimise distraction. At the beginning of the first visit, participants signed their informed consent and then underwent a health screen procedure during which their morphometric and demographic data were collected and blood pressure, heart rate and fasting blood glucose were measured. Based on the outcomes of the health screen, participants eligible for the trial were familiarised with the protocol and procedures of the study.
Additionally, participants filled in pen-and-paper questionnaires and completed three cycles of the computerised tasks of CDR and COMPASS batteries to minimise practice effects. Performance data from the practice session were not included in the analysis. On arrival at their first testing visit, participants were randomly allocated to the treatment order determined by a computer-generated matrix. Fasting blood glucose was measured at the beginning of each visit to ensure that participants fasted before attending sessions, which was followed by standard light breakfast (piece of toast or cereal) consumed at least 1 h before the onset of the experiment. During this hour, prior to baseline assessment, they completed selected pen-andpaper questionnaires (STAI-S, POMS and Leeds SEQ) and later underwent four identical cognitive and mood assessments using parallel versions of the CDR battery as well as parallel versions of selected tasks from the COMPASS battery.
Statistical analysis Only the data of the participants who attended all three visits and had a suitable level of compliance with all study protocol requirements were selected for analyses. Across the participants, screening for univariate outliers revealed that scores for each outcome measure were normally distributed. Data points with mean scores more than three standard deviations outside the sample means were excluded from the analyses; therefore, a number of participants vary between the tasks depending on their performance on particular measure. This especially evident in relation to Factor scores, which are composite scores based on performance not one but a number of tasks; however, it was confirmed that the treatment order was not affected by the removal of the outliers. Scores from the pencil-and-paper questionnaires (POMS, Leeds SEQ and STAI-S) administered prior to the baseline assessment to ensure there were no in participant emotional and physical state between 2 days prior to treatment administration, which could affect cognitive performance and mood, were analysed with a one-way repeated-measures analysis of variance (ANOVA).
Scores on all CDR individual task outcomes, the five primary factors, the selected tasks from COMPASS battery outcomes and blood glucose levels were analysed as 'change from baseline' using the SPSS 17. 0 statistical package for Windows. Prior to analysis of 'change from baseline' data, raw baseline scores for all these measures were subjected to a one-way repeated-measures ANOVA comparing placebo with P. quinquefolius to ensure there were no differences of performance between the study days. Next step, change-from-baseline scores were subjected to a twoway repeated-measures ANOVA with factors being 'treatment' (Cereboost TM , placebo) and 'time' (1, 3 and 6 h) with repeated measures on both factors. For this initial ANOVA, all testing was two-tailed at 5% significance level. Bonferroni corrected paired contrasts were made comparing P.
Acknowledgements The  Agence Nationale de la Recherche (ANR) ,t hrough the  ANR JCJC "ProOFE"g rant  ( ANR-13-JS08-0007-01 ), and the  Engineering and Physical Sciences ResearchC ouncil (EPSRC) , grant  EP/ K016938/1 , are gratefully acknowledged for their financial sup-port. The current work was granted access to the HPCr esources of [CCRT/CINES/IDRIS] under the allocation c2014085117 made by  GENCI  ( Grand Equipement National de Calcul Intensif ) and  HPC resources from ArronaxPlus  (grant  ANR-11-EQPX-0004  funded by the  ANR). We thank the  CCIPL (Centre de Calcul Intensif des Pays de la Loire)  for grants of computer time. We are grateful for  EPSRC Core Capability Funding  ( EP/K039466/1 ). L. M. thanks  Mkwawa University College of Education  for financial support. Keywords: hydrogen-bond acidity • benzylic alcohols • conformation analysis • fluorine.
Often optimization of several independent factors may lead to improved simulated PK profiles. In such circumstances it is of value to test such hypotheses with experimental data. In system model verification, use of prospective simulations and large compound datasets could help identify model limitations. Drug-independent system components and virtual populations may be verified with compounds representing a range of physicochemical properties. Hannah Jones (Pfizer) provided two examples of PBPK modeling. Differences in UGT-mediated clearance and UGT2B15 polymorphism between Japanese and Caucasian subjects were considered in the model of an investigational drug. Resulting simulations supported regulatory interactions aimed at avoiding additional clinical studies in Japanese subjects. In the second example, a PBPK model included drug disposition mediated by organic anion-transporting polypeptides (OATP).
Literature data of seven compounds and a scaling method were employed to develop the model. The model was valuable in prospectively predicting human PK for four investigational drugs tested to be OATP substrates in vitro. Patrice Larger (Novartis) described PK predictions using two PBPK pregnancy models: one with limited pregnancy factors and a second expanded with additional factors such as fetal-placental volume and blood flow. Model performance was assessed using four reference compounds. Specific pregnancy factors in the second model did not significantly impact the predicted pharmacokinetic profiles for the reference compounds. A PBPK model was also described to facilitate dose selection in a pediatric trial. The model incorporated PK data from adults, child physiology, and UGT ontogeny.
An important point identified for follow up was development of a statement, with a supporting rationale, which explains the expectation of intravenous data (unless its absence can be justified) as a key element in the quantitative mechanistic understanding of drug disposition. The system qualification discussion (Session 3) focused on commercially available software, which is utilized in the vast majority of regulatory applications of PBPK. The three main companies providing PBPK software used in clinical pharmacology regulatory submissions to date (GastroPlus, PK-Sim, and SimCYP) provided short summaries of their approach to system model qualification. The companies generally appeared to take similar approaches. The system validation consists of tracking and documenting (user manual, publication) changes and (re)validating the software using former or new test cases.