dataesr/scientific-paragraphs-categorization huggingface dataset:
Consequently, the recorded firing rate in these areas was related to the activation of metabosensitive muscle afferents (Figure  1B ). The recording protocol and the MATLAB treatment were the same as for metabosensitive afferents recording described above. The firing rate variation after injection was first compared with the corresponding baseline discharge. Then, these values were expressed in percentage to the variation of the firing rate following saline injection in each hemisphere (IP-PRE and CT-PRE). Ratios of IP-POST/IP-PRE and CT-POST/CT-PRE were calculated.
TTC Staining Anesthetized animals were decapitated. Fresh brain tissues were quickly removed and placed at -80 • C during 4 min before performing a 2-mm-thick coronal sections using a brain slicer device. Coronal sections were then dropped in 5 ml of 1% TTC solution (Sigma-Aldrich R ) during 30 min at 37 • C and washed 3 times with PBS-1X and fixed with Antigenfix during 24 h. Infarcted tissues (white-colored area) were delimited with ImageJ software and infarct size was expressed in percentage of both right hemisphere and total brain volume.
Statistical Analysis Statistical analysis was performed using SigmaStat R software program (San Jose, CA, United States). All data are presented as Mean ± SD. Raw values of behavioral tests from D1 to D7 were averaged and compared to PRE value between groups with a twoway ANOVA (time × groups). For electrophysiological analysis, ratios of AFF-POST/AFF-PRE, Reflex-POST/Reflex-PRE, the IP-POST/IP-PRE, and CT-POST/CT-PRE were averaged in each group and compared between groups with a one-way ANOVA. Post hoc comparisons were performed with Student-Newman-Keuls multiple post-test comparisons. To compute the magnitude of difference between groups, the effect size was calculated for main results using Cohen's d for significant differences. Large effect size was considered for Cohen's d ≥ 0. 9, medium for Cohen's d between 0.
Results An automated quantification method was set up using the R software and was validated by quantification of β-cell mass, a well characterized parameter. β-cell mass was 29. 6±112 mg in patients and 628 ±717 mg in controls (p<0. 0001). α-cell mass was 181±176 mg in patients and 349 ±241mg in controls (p<0. 0001). Non-exocrine-non-endocrine area to total tissue area ratio was 39±9% in patients and 29± 10% in controls (p<0. 0001) and increased with age in both groups, with no correlation with diabetes duration in patients.
The Network for Pancreatic Organ Donors (nPOD) has been created to provide access to high-quality pancreatic specimens for research purposes  [16]. To date, the nPOD repository includes more than 400 pancreata. High-magnification digital images (virtual slides) stained for different markers are available on the nPOD website (www. jdrfnpod. org). Taking advantage of our previous experience with β-cell quantification  [17] , we developed an automated quantification method, using R software, to analyze the nPOD glucagon-stained virtual slides. We were thus able to characterize the α-cell mass in patients with T1D compared to non-diabetic control subjects and its relationship to disease duration.
Pancreas sampling and processing nPOD employs a standardized protocol for pancreas specimen preparation. Each pancreas is divided into three regions: the head, from the near-duodenal region up to the proximal isthmic region, located in front of the superior mesenteric vessels; the body and tail portions are then defined by dividing the remaining pancreatic tissue into two equal parts. Each region is processed into formalin-fixed paraffin blocks. Serial sections are stained by hematoxylin and dualcolor immunohistochemistry, either Ki67 and insulin or CD3 and glucagon, visualized with peroxidase-diaminobenzidine and alkaline phosphatase-Fast Red polymer systems, respectively. Stained sections are scanned at 320x magnification using an Aperio CS scanner (Leica/ Aperio) and pictures are finally stored in an online pathology database (eSLIDE; Leica/ Aperio).
Development of an automated morphometric analysis process α-cell area was quantified on glucagon-stained slides (4 slides by patient, 2 from the head and 2 from the tail), β-cell area on insulin-stained slides (2 slides by patient, 1 from the head and from the tail), and non-exocrine-non-endocrine (non-acinar, non-insular) pancreatic tissue area on both insulin-and glucagon-stained slides (i. e. 6 slides by patient, 3 from the head and 3 from the tail). On each slide, three different areas were evaluated: the total pancreatic tissue area, the nonexocrine-non-endocrine pancreatic tissue area and the Fast Red-stained area, the latter corresponding either to alpha-cells or to beta-cells area, depending on the immunostaining. In R software, a picture is converted into 3 matrices, corresponding respectively to the blue, green and red color levels, each matrix element accounting for one pixel.
e. any pixel whose blue, green and red color levels were superior to the respective color levels of background pixels in the resized picture (Fig  1 ). The main difficulty in automating this process was to be able to take into account the differences in contrast and staining intensity between slides. The choice was therefore made to build an R script aimed at generating several propositions for total pancreatic tissue selection, by varying the color level threshold used by the different filters applied in the script (S1 File). An additional step of visual selection of the most accurate proposition by one investigator (FBS), in comparison to the original picture, was further carried out, allowing a critical step of visual control in the automated process (Fig  1). For exocrine+endocrine pancreatic tissue area delineation, the first step also consisted in resizing the original picture, like in the total area delineation, but to a bigger size than for total pancreatic tissue, i.
e. to one twentieth of its initial size. The next step consisted in the selection of either hematoxylin-stained blue pixels, i. e. colored pixels, whose blue level was superior to green and red levels, or to diaminobenzidine, brown, pixels or Fast Red, red, pixels, defined as colored pixels, whose red level was superior to green level. The R script intentionally generated automatically several propositions for exocrine+endocrine pancreatic tissue selection (S2 File). The choice of the most adequate selection by the investigator added a second step of visual control in the automated process (Fig  2). Given the small size of Fast Red-stained regions, the use of the full resolution picture was necessary for appropriate selection of the corresponding area. However, given the large size of the original picture, this required its split into several smaller pictures to allow easy manipulations within R software.
The final Fast Red-stained selected area was then calculated by summing the results obtained on each small picture. The selection principle of Fast Red-stained pixels relied on the selection of colored pixels whose red level was higher than blue and green levels. However, the main difficulty encountered in this process was the presence of light pink artifact pixels on some slides, most often organized into large very slightly colored sheets, but sometimes with a color intensity similar to that of some weakly Fast Red-stained alpha-or beta islet cells. Thus, to gain detection sensitivity without losing specificity, a multi-step selection R-script was built (Fig  3 ) (S3 File). Briefly, assumption was made that every truly positive Fast Red-stained pixel should either display a high red level itself or a high red level pixel in its vicinity; artifact pixels being low in intensity and located in expanded regions only containing low red level pixels.
Hence, the first step of the script selected high red level pixels (Fig 3B ); the second step drew circles with a 50-pixel radius around each selected pixel (Fig 3C ), thus pinpointing the regions where true Fast Red-stained pixels were located; and the final step identified the red pixels with a lower threshold within these circles as Fast Red-stained pixels (Fig  3D ). The main drawback of this approach should be the inappropriate selection of islet neighboring tissue submitted to bleeding of the overstaining existing on some slides. However, the validation of the present methodology on some well-established parameters, as beta-cell mass, suggested that this effect was negligible (Fig  4 ).
Another advantage that may encourage further studies on this and other repositories is the use of the free software R as a single platform for both image analysis and surface ratio calculations. The quantification method was first validated for β-cell mass assessment, a parameter that has been extensively studied, including in the nPOD repository  [10, 17]. In non-diabetic control subjects, β-cell mass was close to that calculated in nPOD subjects, by us  [17]  and by others  [10]. Elsewhere, the β-cell mass was 800-900 mg in adults  [19, 20]. The present method thus gave slightly lower estimates than the literature, at least in part because, unlike methods based on point counting, we did not take into account the nuclear area, since only the cytoplasm was Fast-red-stained. In patients with T1D, the β-cell mass was close to zero, confirming our previous study  [17]  and another nPOD-based study  [10].
Acknowledgments The authors thank  Jerome Serrano  for his help in building the automated method for image analysis and  Didier Bonnet  for reviewing the manuscript. This research was performed with the support of the  Network for Pancreatic Organ Donors with Diabetes (nPOD) , a collaborative type 1 diabetes research project sponsored by  JDRF. Organ Procurement Organizations partnering with nPOD to provide research resources are listed at www. jdrfnpod. org/for-partners/npod-partners.
We developed new methods for parameter estimation-in-context and, with the help of 125 authors, built the AmP (Add-my-Pet) database of Dynamic Energy Budget (DEB) models, parameters and referenced underlying data for animals, where each species constitutes one database entry. The combination of DEB parameters covers all aspects of energetics throughout the full organism's life cycle, from the start of embryo development to death by aging. The species-specific parameter values capture biodiversity and can now, for the first time, be compared between animals species. An important insight brought by the AmP project is the classification of animal energetics according to a family of related DEB models that is structured on the basis of the mode of metabolic acceleration, which links up with the development of larval stages.
We discuss the evolution of metabolism in this context, among animals in general, and ray-finned fish, mollusks and crustaceans in particular. New DEBtool code for estimating DEB parameters from data has been written. AmPtool code for analyzing patterns in parameter values has also been created. A new web-interface supports multiple ways to visualize data, parameters, and implied properties from the entire collection as well as on an entry by entry basis. The DEB models proved to fit data well, the median relative error is only 0. 07, for the 1035 animal species at 2018/03/ 12, including some extinct ones, from all large phyla and all chordate orders, spanning a range of body masses of 16 orders of magnitude. This study is a first step to include evolutionary aspects into parameter estimation, allowing to infer properties of species for which very little is known.
Introduction The role of biodiversity in ecosystem structure and functioning is central for conservation and environmental quality management, as well as biospherics and earth system studies. Biodiversity is not only about the number of species present, but also the number and nature of the different characteristics and functions which make up a community or an ecosystem, often referred to as traits. Scientists and managers are turning towards such trait-based approaches to measure the health and vitality of ecosystems. In this context of apprehending biodiversity on the basis of diversity of characteristics and functionalities we have been developing the AmP (Add-my-Pet) project. AmP is a database of referenced data on animal energetics, parameter values of models based on Dynamic Energy Budget (DEB) theory  [1] [2] [3] [4] , and properties derived from these parameters.
Some 125 authors contributed to the database at 2018/03/12. The AmP project aims: (i) to find the simplest organization principles for metabolism upon which all life is based and (ii) to understand taxon-specific patterns as variations on this common organization. The development of DEB theory started in 1979 and meanwhile over 700 papers have been published on DEB theory, see www. zotero. org/groups/500643/deb_library/. Partly based on the fact that a large number of popular empirical models turned out to be special cases of DEB models  [4] , we claim that DEB theory is presently the best tested quantitative theory in biology. The comparison of species on the basis of parameter values is an important aspect of the AmP project. Species-comparisons based on measured quantities suffer from the problem that these quantities typically have contributions from many underlying interacting processes, and were not measured for all species of interest.
Parameters of mechanistic models, however, have much simpler links with such processes, which makes it easier to find explanations for differences between species. Moreover, the complete parameter set is available for each entry, allowing to predict, e. g. respiration, without any measured data on respiration. Comparison of species is, however, not the only important application of the AmP website. Prediction of effects of global change  [5] , understanding the geographic distribution of species  [6] [7] [8] , the effects of (toxic) chemical compounds  [9] [10] [11] [12] , the optimization of bio-production (e. g. aquaculture and agriculture  [13] [14] [15] ), stock management, the best re-introduction of endangered species or the control of invading species  [16] , are just examples of applications where detailed knowledge of energetics of species in a DEB context is very useful.
Like many ecologists, we see energetics as the key to understand the ecological behavior of species  [17] , and as the root of population  [18]  and ecosystem dynamics  [19, 20] , with consequences at the planetary level  [21]. This is the context that motivated the development of DEB theory, of which the AmP project is an application. In view of the rapid build-up of ecological stress all over the world, we think that the field is in urgent need of an online database like AmP. AmP started in 2009 as an educational initiative, to teach researchers how to estimate DEB parameters from their data and animals have the simplest metabolisms (if we compare with say plants, bacteria or microalgae). The database grew and in 2013, (at about 300 entries) we teamed up, formed the first AmP curator board, and together developed the code and webplatform underlying AmP.
We wanted to get an overview on: (i) how well the standard DEB model worked for describing animal metabolism, (ii) standardize and improve the parameter estimation procedure and (iii) improve our capacity to judge the realism of parameter values. We start with a brief introduction of DEB theory. All applications of models, including testing of the model against data, start with knowledge of parameter values. The parameters of a DEB model must be estimated from a collection of data sets on the various aspects of energy budgets and life history, using all this information in combination. This involves new features in the methodology of parameter estimation. The next section of this paper describes improvements we implemented, based on the co-variation method  [22, 23] , which was used in an early phase of the database project.
Moreover we needed more administrative rigor and improved methods for detecting patterns in parameter values. Development of new routines and re-organization of the previous estimation procedure allowed us to include these new extensions. So the following section describes the new web-interface and structure of the database. Finally we present and discuss the results obtained after implementing the new method and reaching 1035 species in the collection at 2018/03/12. Given that these entries employ together 270 different types of data, in 585 combinations, the estimation of 14 parameters of each species not only illustrates the scope of the data reduction, but also the step-up in comparison potential.
An improved parameter estimation method Prior to this work, DEB parameters for the AmP entries were obtained with the covariationmethod for parameter estimation  [22, 23]. All the code for parameter estimation is developed in the DEBtool_M package, which is frequently updated and freely available at https://github. com/add-my-pet/DEBtool_M/. In this section we describe improvements to the method which are implemented in DEBtool. The improvements comprise: • organization of data, parameters and model predictions into three user-defined functions which are controlled by a single 'run-file' where all estimation options are set and the estimation is performed. • Automatized saving of parameter estimates and possibility to use the saved values as a new seed parameter set. • A new and much better loss function that is minimized to estimate parameters which is symmetric in the role of data and predictions, and which addresses differences in dimensions.
The user can overwrite default weight values (for either the whole data set or else for particular values). This is done in the mydata-file (see  Fig 2). The overwriting of the weight coefficient is done by multiplying the default value by a dimensionless factor. This allows including user-based knowledge about accuracy of data. The AmP procedure distinguishes between how real and pseudo data are weighted such that if data determine parameters well, pseudo-data hardly contribute. The AmP estimation procedure includes several loss functions. The user defines which loss function to use in estimation options-the default weight coefficients for pseudo-data depends on which loss function is being used (these defaults are presented in the Results). Filters. The improved parameter estimation contains filters.
AmPtool AmPtool is a software package that is designed to analyse patterns in (functions of) parameter values in selected entries. It is available via github. com/add-my-pet/AmPtool/, and changes frequently since the AmP collection is rapidly expanding. Meta-data, parameters and implied properties (biologically relevant quantities which are functions of parameters as well as food and temperature) are collected in a single Matlab structure (allStat. mat). Advanced plot-routines were developed to plot (functions of) parameters against other (functions of) parameters, for selected species. These selections make use of the taxonomic tree. The tree actually consists of lists-of-lists, based on the newest insights in taxonomy, as presented in the Catalog of Life, the Encyclopedia of Life and Wikipedia.
AmP web-interface The AmP web-interface was developed as a result of this work. The interface allows examining each entry as well as obtaining on overview on how entries relate to each other. A web interface for each entry. Each entry has its own web-interface that can be accessed via several routes, each organized differently (by classification in the species-list, according to phylogeny in the species-tree, alphabetical order of latin and english common names, and by energy budget, which is useful to walk from one species to a related one). The following information is printed for each entry in the online AmP web-interface: (i) (meta)data with references, (ii) predictions, (iii) (meta)parameters and (iv) implied properties. Meta-data (as introduced in the Methods) contains information on classification, authors, dates (submission, modification, acceptance), level of completeness of data, types of zero-and uni-variate data, discussion points about the fit (such as assumed differences between males and females) and facts about the species.
Comparing species on the basis of parameters From 2013 till 2017 the number of AmP entries (Fig 3  , left ) was dramatically increased from ca 300 in 2013 to 1035 at 2018/03/12 to find out whether DEB models do apply to all animals and determine how problematic it is to have limited amounts of data for estimating DEB parameters. AmP receives entries which are submitted by the international scientific community and to date some 125 authors have contributed to the collection. Every author and their associated entry (with links to the entries) are listed on the AmP web-interface. Over the course of this study, we included a bit over 700 extra species to the collection since 2013 as well as converted the previous 300 entries to this new format. We were careful to select species such that the collection had a broad taxonomic scope, as well as included species of commercial relevance and species relevant for conservation and toxicity testing.
We adopted the "sb" as the default loss function for the AmP procedure and all of the parameters in AmP today use this loss function. Pseudo-data replace lack of information, serving as prior information on parameter values  [22, 23]. The more information the data contain the less the pseudo-data contribute to the resulting parameter estimates. How can we check that the resulting parameters in AmP are not dominated by the use of pseudo-data? We studied how the DEB parameter "allocation fraction to soma", κ (Table  1 ), was distributed across all AMP species (the graph is available on the parameters page of the AmP website). We refer the reader to  [33]  for an in-depth study of how κ, ½ _ p M , f _ p Am g and _ v are distributed across all of the species in AmP. Of relevance here, is that the values for the allocation fraction to soma, κ, in the collection turns out to follow a beta distribution with perplexing accuracy.
For fish, for instance, standard, fork as well as total lengths are traditionally measured. However, what length measure was used is not always well indicated in the study, and each length measure needs to be linked to quantifiers for mass to allow application of DEB models. (iii) Different data belong to populations that differ in (mean) parameter values (e. g. because they experience different environmental conditions), but data availability does not allow parameter estimation for each population, (iv) intrinsic stochasticity of physiological/ecological behavior, partly originating from differences in parameter values between individuals. A large scatter translates to uncertainty in parameter values, but little scatter does not imply that parameter values are well determined due to model plasticity, see  [32].
This life history is found in Elopiformes, Albuliformes, Notacanthiformes, Anguilliformes, Ophidiiformes, some Anura and Echinodermata. • sbp: Like model std but growth ceasing at puberty, meaning that the kappa-rule is not operational in adults. It has the same parameters as the model std. This life history is found in the one Calanus entry, while other copepods in AmP accelerate (see next section). We obviously need more data and better culturing techniques to become better organised on copepod models. a-models. The a-models apply to most species with a larval phase. They show metabolic acceleration at, or soon after, birth; the end of acceleration frequently coincides with morphological metamorphosis. s-models assume isomorphic growth over all life stages. DEB theory assumes that assimilation increases with surface area and maintenance with volume, but makes no assumptions on how surface areas relate to volume.