dataesr/scientific-paragraphs-categorization huggingface dataset:
In vivo cases Following the pipeline introduced in Sect. 2. 2, a unique parametrisation of the model was obtained for each of the volunteer and patient datasets acquired non-invasively. The myocardial geometry was obtained from the short-axis cine stacks registered to 3D tagged images and tracked through the cycle using IRTK, providing observations for estimation as well as boundary condition data, namely LV cavity volumes (see Fig. 8 ) and basal deformations. Following the parameterisation process, Figs. 9 and 10 show that unique identification of the passive ratio γ and active tension scaling α(t) was achieved. Moreover, Fig 11 illustrates that qualitative model motion corresponded well with extracted motion from the patient data. The active estimation results indicated that residual active tension was present in the myocardium in 4-5 frames past end systole.
This additional information could be used to correct passive parameter estimates. For example, the objective function J p was recomputed for case V based on the frames with no residual tension (15-24), and the minimum was achieved at γ = 0. 08. After scaling by end-diastolic pressure ratio, the corrected parameter values were a = 0. 304 kPa and a f = 3. 8 kPa, including relative corrections of 10 % for a and 3 % for a f. Unlike the in silico tests, where good model fidelity allows estimation of the active tension based solely on the tracked myocardial data, the in vivo cases relied on the pressure curve in producing a viable active tension estimate. The minimum of the displacement component of the active functional was achieved at near-zero active tension throughout the cardiac cycle for all datasets due to model errors dominating the term.
The use of non-invasive pressure estimates ensured that a reasonable pressure-volume loop was produced by the model (see Fig. 8 ). While we cannot make reliable interpretation of parameters based on three datasets only, some differences can already be observed in the results in Sect. 3. 2. The estimated passive parameter ratio is significantly lower for the healthy volunteer, and consequently we estimate that the tissue is much stiffer in the fibre direction than isotropically, which does not appear to be the case for the DCM patients. Case P1 activates significantly slower, and case P2 significantly higher than V. The higher total relative error J p and the shallower curves near the minimum in the passive estimation for 123 P1 and as compared to V could be caused by reduced motion of the diseased myocardium and therefore a stronger effect of model errors.
Limitations and future work In the current study, a range of in silico datasets and three in vivo cases are considered in order to validate unique identifiability of parameters in the personalisation process. A comparative analysis of parameter estimates for a wider range of cases including healthy volunteers and patients is necessary for determining the diagnostic and prognostic potential of the model parameters as biomarkers. While personalisation of multiple in vivo datasets using parameter sweeps would incur a high computational cost, the results of this work suggest that commonly used optimisation methods, such as the reduced-order unscented Kalman filter  (Chabiniok et al. 2012; Moireau et al. 2008; Xi et al. 2011) , would perform well in the same parameter spaces using the outlined objective functions.
As may be expected, the absolute values of the parameters obtained using these modified models change to some extent. More importantly, the identifiability of parameters is not dramatically affected by either modification. The viability of using regional passive parameters and non-homogeneous activation should be investigated. The choice of the end-systolic geometry as the reference state is likely to have an effect on displacement errors, as well as passive estimation results. An investigation of the bias introduced by this choice would be of interest. While the present set-up did not permit simultaneous estimation of the passive parameters and the reference state, it is possible that refinement of the model and/or enrichment of the dataset would make it a viable option. The need for an estimate of the LV pressure curve for active estimation means that further validation of the employed scaled normalised data would be beneficial.
Conclusions The proposed pipeline for personalisation of the full-cycle LV mechanics model produced unique passive and active parameters for all of the in silico and in vivo datasets tested. Moreover, the behaviour of the objective functions over the respective parameter spaces implied that an efficient optimisation or filtering method can be successfully used to find the patient-specific parameters of the model. The choice of the L 2 (Ω 0 )-norm of displacement error as the objective function in parameter estimation allowed us to take advantage of the rich dataset extracted from 3D tagged MR images. In the in silico tests, the presence of random noise of up to 20 % and typical processing errors caused a moderate deterioration of model fidelity, without a significant effect on the objective function behaviour.
The reduced ratio γ balanced this by reducing dilation and increasing elongation. Identifiability of The estimated active tension profile and identifiability in J a were not significantly affected by the chosen fibre distribution. The characteristics of the optimal α(t) and the associated J a are shown in Table  6 , and we also note that the highest difference between the estimated tensions for the different fibre orientations was 3 kPa at peak activation. A personalised fibre distribution, such as that based on diffusion tensor MRI, might have a stronger effect on model fidelity; however, this type of data was not available in the current datasets.
Acknowledgments The authors would like to acknowledge funding from the  BHF   New Horizons Program  ( NH/11/5/29058 ) and the  Engineering and Physical Sciences Research Council  ( EP/H046410/1  and  EP/K030310/1 ), and support from the  Wellcome Trust EPSRC Centre of Excellence in Medical Engineering  ( WT 088641/Z/09/Z ) and the  NIHR Biomedical Research Centre at Guy's and St. Thomas' NHS Foundation Trust  and  KCL. The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR  or the DoH. The Image Registration Toolkit (IRTK) was used under Licence from Ixico Ltd. The authors would like to thank  Dr E. Kerfoot  for providing medical image and model visualisation software.
The idea of that article, which specifically considered the particular univariate nonparametric finite mixture model described in Section 4. 1, is that each observation in the dataset may be randomly assigned, at every iteration of the algorithm, to one of the mixture components. This assignment is based on the probabilities of component membership for each observation, which are a natural byproduct of a finite-mixture EM algorithm, resulting in a stochastic algorithm. Benaglia et al. (2009a)  extends this algorithm to the case of Equation (1. 2) in its full generality and refines it by replacing the random assignment of observations to components by a deterministic algorithm that assigns observations fractionally to the various components according to their component membership probabilities.
The Water-level dataset To illustrate the adaptive block-and component-wise bandwidth approach, we consider a benchmark dataset previously analyzed by  Hettmansperger and Thomas (2000)  and  Elmore et al. (2004)  with a conditionally i. i. d. (independent and identically distributed) assumption, and more recently by  Benaglia et al. (2009a)  and  Levine et al. (2011)  under the same assumptions we make here. This experiment involves n = 405 children aged 11 to 16 years subjected to a written test as initially described by  Thomas et al. (1993). In this test, each child is presented with eight rectangular drawings of a vessel on a sheet of paper, each tilted to one of r = 8 clock-hour orientations: 11, 4, 2, 7, 10, 5, 1, and 8 o'clock, in order of presentation to the subjects. The children's task was to draw a line representing the surface of still liquid in the closed, tilted vessel in each picture.
The acute angle, in degrees, formed between the horizontal and this line was measured for each response, the associated sign being the sign of the slope of the line. The water-level dataset is available in the mixtools package  (Young et al. , 2009; Benaglia et al. , 2009b). As in  Benaglia et al. (2009a)  and  Levine et al. (2011) , it seems reasonable to weaken the conditionally i. i. d. assumption, assuming instead that only opposite clock-face orientations lead to conditionally independent and identically distributed responses. Thus the eight coordinates may be organized into four blocks of two each, which is model (3. 7) with B=4. According to the ordering of the clock-hour orientations, we thus define b = (4, 3, 2, 1, 3, 4, 1, 2). For instance, we see that b 4 = b 7 = 1, which means block 1 relates to coordinates 4 and 7, corresponding to clock orientations 1:00 and 7:00.
An alternative to this particular pairing of clock-hour orientiations is to consider each of the eight orientations as distinct. As we show in Appendix B, treating the dataset in this way provides evidence that the pairing approach is reasonable; thus, we proceed with the four-block assumption. We first consider here the 3-component model as studied in  Levine et al. (2011)  to compare the MSL with fixed bandwidth against the adaptive strategy. Figure  2  gives the solution returned by the MSL algorithm with a fixed bandwidth preset to h = 4, as in  Benaglia et al. (2009a)  and  Levine et al. (2011). This value has been chosen by trial an error by these authors, instead of allowing the algorithm compute a fixed bandwidth value using Silverman's rule as in (3. 13). However, using that rule would result in a fixed bandwidth value of h = 1.
A psychometric data The data in this section come from a large-scale psychometrics study exploring cognitive task performances for children with specific language impairments, presented in  Miller et al. (2001). Response (or reaction) times (RT) with which the children respond to a range of tasks are recorded in milliseconds. We focus in particular on one experiment that  Miller et al. (2001)  call a "simple RT task": The child is instructed to strike a key as quickly as possible in response to a visual signal, which itself is emitted after a delay following the word "ready" spoken by the child. There are 8 trials for each of three time delays of 1, 2 and 5 seconds. Tasks are mixed into a much longer sequence of trials so that the child does not know exactly what the next task will be and independence of the repeated measures for each child may reasonably be assumed.
This dataset with n = 82 subjects and r = 24 coordinates is available in the mixtools package  (Young et al. , 2009; Benaglia et al. , 2009b)  for the R statistical software environment (R Core Team, 2013), and is loaded by the data(RTdata2) command. This experiment supports a model with B = 3 blocks of 8 coordinates each, each block  corresponding to a delay between the "ready" sign and the stimulus. This data set is interesting because it illustrates the potential interest of the conditional independence model for multivariate data with a large number of coordinates and block structure suggested by scientific considerations. We ran the MSL algorithm with fixed and adaptive bandwidth strategies. Results in Fig. 5  show that there is almost no difference between the two, which is not surprising because the component densities have similar scaling properties.
Measurements on Abalone Nash  et al. (1994)  report measurements on a large dataset of n = 4177 abalone, which are a type of marine life akin to large snails that are often harvested for food. The dataset is publicly available at the Machine Learning Repository at the University of California, Irvine  (Bache and Lichman, 2013). There are m = 7 continuous measurements made on each animal in the dataset, many of which (e. g. , whole weight, shucked weight, and viscera weight) are highly correlated with one another. Since this correlation is likely to persist even within each mixture component, one might reasonably suspect that the conditional independence assumption is violated in this dataset. Thus, we use principal components (PC) analysis to find a linear transformation of the original dataset, then apply the modified MSL algorithm of  Levine et al.
Our choice here of m = 3 is arbitrary and more detailed analyses might explore different numbers of components. We have successfully run our algorithm on this dataset with up to m = 20 components. One interesting facet of our analysis of these data is that it is the first dataset for which we have noticed a qualitative difference between the results of the EM-like algorithm of  Benaglia et al. (2009a)  and the smoothed version of the algorithm presented by  Levine et al. (2011). For now, we cannot explain this difference, but its presence suggests a topic for future study.
B Justification of four-block assumption in water-level dataset In the water-level example in Section 5. 2, we pair the eight repeated measurements into four pairs of two clock angles each. As shown in Figure  8  for the three-component solution, this choice is justified by the fact that when we assume that each of the eight orientations follows a distinct set of component densities, the similarities between the pairs of orientations are clear. The results are similar when we consider the four-component solution, so we omit the corresponding plots for the four-component solution here.
, 2010) , and mature females can be observed with obvious mating scars  (Porcher, 2005; Chin et al. , 2013b). Blacktip reef sharks may also incur injuries from aggressive behaviour and predation by larger reef sharks, such as the grey reef shark (Carcharhinus amblyrhynchos) and sicklefin lemon shark (Negaprion acutidens), and potentially, by very large sharks, such as the great hammerhead shark (Sphyrna mokarran), which has been observed to prey upon reef sharks  (Mourier et al. , 2013b). Here, we provide photographic records and quantitative data documenting the recovery of blacktip reef sharks from natal umbilical scars and from injuries observed in free-swimming animals and from acoustic tagging data. These data will help to provide a greater understanding of the potential for recovery and survival from natural and anthropogenic injuries in this species and for elasmobranchs in general.
Photographs were imported into the open-source image processing software FIJI by ImageJ (version 2. 0. 0-rc-23/1. 49m; Schindelin et al. , 2012) , scales were set to 1 cm using the ruler that was photographed with each shark, and then scars were traced using the freehand selection tool. Area (in square centimetres), perimeter (in centimetres) and shape descriptor measurement tools, including circularity [4π*area*(perimeter 2 ) -1 ], aspect ratio (majoraxis*minoraxis -1 ) and roundness [4*area*(π*majoraxis 2 )], were used for each traced 'umbilical scar'. Data were then compiled and compared over time using Student's paired t-tests for body measurements (comparing day 0 and day 24 only) and repeated-measures ANOVAs for scar dimensions (Systat Software, Inc. , Chicago, IL, USA). All sharks were released back into their natal lagoons after 30 days in captivity.
1 ). All tagging procedures were carried out by trained personnel following strict protocols developed by veterinarians and approved by animal research ethics committees. The entire procedure for each shark was typically completed in <10 min. Sharks were then returned to their normal swimming position, closely monitored to ensure recovery, and then released. Long-term recovery and survival of these animals was directly recorded from field notes and photographs of individuals recaptured during subsequent capture-mark-recapture sampling, and inferred from long-term telemetry data  (Chin et al. , 2013c).
laboratory studies in Moorea were funded in part by the Institut des Récifs Coralliens du Pacifique (IRCP, Fellowship to J. L. R. ) and the Australian Research Council Centre of Excellence for Coral Reef Studies, and the field studies were financially supported by the Direction à l′Environnement (DIREN) of French Polynesia, Coordination Unit of the Coral Reef Initiatives for the Pacific (CRISP Program) and Proscience. The Australian study was funded by the Australian Government MTSRF Program 4. 8. 4 (A. C. ).
Identification and quantitation of heavy isotopetagged (BMP9-treated) versus light-isotope-tagged (control vehicle-treated) peptides were accomplished using ProteinPilot software (v4. 5, ABSciex), with a mass tolerance of 0. 6 Da and a detected protein threshold confidence of greater than 90%, and the UniProt Knowledgebase version 2007-0123 (or later), and set to detect contaminants (e. g. serum albumin) as described  [29].
Scratch Assay HUVECs were plated in 6-well dishes in EBM-2 media and allowed to reach confluence (approximately 24 hr). A 200 μl pipette tip was used to scrape a "wound" in a vertical line. Cells were washed with PBS twice to remove unattached cells and incubated for 24 hr in EBM-2 (0. 5% FCS and ½ the growth factor bullet kit) and BMP9. Pictures were taken 0 hr, 24 hr, and 48 hr (data not shown) after wounding using an Axiovert 40C microscope (Zeiss). Endothelial cell migration was quantitated by measuring the width of the cell-free zone (distance between the edges of the injured monolayer) as described previously  [59].
Finally, BMP9 showed diminished capacity to rescue the nuclear localization of YAP1 as seen for SMAD1/5 (the relative pixel densities for nuclear versus cytoplasmic staining are indicated below Fig 2A  and 2B ). This result supports the hypothesis that endoglin promotes BMP9-dependent translocation of YAP1 to the nucleus. Next, we performed cell fractionation followed by Western blot analysis of HUVECs after BMP9 stimulation and/or shENG transduction. These observations confirmed the immunofluorescence results indicating that BMP9 increased the nuclear localization of YAP1, and that this effect was impaired by shENG knockdown (Fig  2C ). YAP1 expression was also examined at the mRNA level by qRT-PCR. There were no significant changes elicited by BMP9 treatment or knockdown of endoglin expression (data not shown), suggesting YAP1 localization, but not expression, are BMP9 and endoglin responsive.
Acknowledgments This Research is supported in part through an  American Heart Association  grant  14GRNT20460045  (CV),  National Institutes of Health  grants  HL083151  (CV),  HL070865  and  HL109652  (LL), and  CA91645  (PB). Additional support was provided by the institute core facilities in Flow Cytometry and Progenitor Cell Analysis (as supported by  NIH / NIGMS   1P30 GM106391 , DM Wojchowski, PI) and Protein, Nucleic  Acid and Cell Imaging  (as supported by  NIH / NIGMS   P30GM103392 ,  R Friesel , PI). The authors thank  Ms. Sarah Coreau  for help in the preparation of this manuscript.
Results Baka populations. We collected life history data from Baka individuals to determine the pattern and tempo of acquisition of their short adult stature. During the 8-year period of our study in Moange-le-Bosquet, the Baka diet did not vary: their consisted of plants and animals from the forest even in the Catholic mission, where they had to employ a Baka man to cook every day. We observed a very marked change with the arrival of cheap alcohol in 2010. From this date, drunkenness became more common in the older men and now extends to adult women. Although birth records exist, they do not give a clear idea of fertility or mortality rates, and even less of infant mortality. The contacts between nuns and pregnant women made it possible to record some births over the years, but the total number of births in groups around Le Bosquet remains unknown.
Models of growth. The data were used to build separate growth models for each period. For a given period, we fitted one model for weight and one model for height (period B only): boys and girls were included in the same model, with fixed genderspecific coefficients to have gender-specific mean growth trajectories. We used structural non-linear models to fit the growth data because the model parameters can be meaningfully interpreted and because milestones and BMI can be easily calculated  52. When using growth models, it is assumed that all the children in a population share a similar pattern of growth in weight and height, respectively, which is reasonable in practice. Furthermore, these models allow some variations at the individual level in terms of the degree and timing of growth. Linear models usually cannot accurately describe growth over a long period; structural mathematical equations have therefore been proposed to describe the shape of the average growth curve.
The modelling was performed using the 'nlme' R software package  52. The R function fits the nonlinear mixed-effects model with the formulation described in Lindstrom and Bates  57  , which uses a Newton-Raphson algorithm to estimate the parameters. To fit a non-linear model, initial values are required. First, we fitted a fixed-effect model with the R 'nls' function. To choose the initial parameters, we used published data and other initial values by trial and error. We used different starting values and introduced random effects one at a time to limit the risk of obtaining local maxima for the likelihood function. We checked the validity of the model by analysing the trend in the residuals against time. We also checked that the weight or height velocity peak at puberty was not bound by the selected model, by again fitting the data using the SITAR method based on a random-effect cubic spline model and developed by Cole et al.
Three main indicators are considered: total upper arm area (TUA), muscle area (UMA) and fat area (UFA). The calculations are made according to Frisancho's formulae  58  TUA ¼ MUAC 2 /4p UMA ¼ (MUAC/p À triceps) 2 Â p/4 UFA ¼ TUA À UMA The reference data for MUAC, triceps skinfold measurement and UMA are from white Americans who participated in the Ten-State Nutrition Survey in 1968-1970 (ref. 58). The birth records allowed us to obtain several life history variables, which, in conjunction with other traits from the model developed for the Baka (for example, age and amplitude of the growth spurt) are of great importance to current discussions on the evolution of growth patterns in modern humans. Individuals whose birth was recorded many years ago have recently appeared in the records as parents, enabling us to obtain the age of first reproduction for 28 girls and in 10 boys.
The interbirth interval was calculated in 443 cases (886 births); twins were recorded in only three cases. During our time in Le Bosquet, a Baka girl carried out interviews to determine the presence or absence of menstruation, obtaining 199 answers from 128 girls of 10 to 17 years of age, which were analysed with probit analysis to estimate the median age at menarche. Comparison with African groups. Since it is more appropriate to compare the Baka with other traditional societies, growth in the Baka was also compared with patterns in other African populations. With the exception of Gambian individuals, with whom a long project on growth started in the late 1940s (ref. 59), growth in sub-Saharan African populations is known from cross-sectional studies on individuals of unknown age. The irregular, infrequent or time-limited studies, and especially the lack of known individual chronologies, have led authors to hypothesize about growth from sporadic data and forced them to develop analyses to deduce life history variables such as age at first pregnancy or age at menarche, among others.