dataesr/scientific-paragraphs-categorization huggingface dataset:
The model is shown in the supplemental Figure  S1. The score plot demonstrates a good separation between unmanipulated, sham, and sepsis survivor groups as confirmed by the R 2 Y and Q 2 values (0. 959 and 0. 694, respectively) and the p-value of the CV ANOVA test is 0. 045. In the PLS model, the first component (horizontal) clearly separates the unmanipulated group from the sepsis survivor group and the second component, the sham group, is separated from the two other groups (Figure  S1 ). The loading plot shows that along the first component, several metabolites are changed in a decreasing order from unmanipulated to sham and to sepsis survivors. The same trend holds true for maltose and glucose, and amino acids such as alanine, beta-alanine, and glutamine. We compared NMR-acquired data of sham flies to the unmanipulated flies using an orthogonal projection on latent structures (OPLS) model.
The acquisition sequence was a NOESY1D (Nuclear Over-Hauser Spectroscopy One Dimension) sequence including the water suppression, a 60 ms spin-lock, 90 • pulse, and 3 s relaxation delay. The number of transient was 64 on 32 K data points. The spectra were obtained during two consecutive days and samples were randomly ordered. The free induction decay (FID) was processed in the NMR pipe using an in-house script for automation of Fourier transform with a 0. 3 Hz line broadening, baseline correction, and calibration using acetate signal at 1. 92 ppm while spectra were phased manually. The 30 spectra were binned into 0. 001 ppm bins with elimination of the water region between 4. 6 and 5 ppm to produce the X matrix, which is used for multivariate statistical analysis. The NMR spectra attribution were performed according to our prior works  [9] , the Human Metabolome Database (HMDB)  [38] , and previous reports on NMR metabolomics with Drosophila  [39, 40].
Statistical Analysis For multivariate analysis, NMR data collected in the X matrix are normalized along the spectral variables using the probabilistic quotient method and the variables are centered normalized. The principal component analysis (PCA), projection on latent structure (PLS), and orthogonal projection on latent structure (OPLS) analyses were performed using an in-house MATLAB (2011b) code (Mathworks, Natick, MA, USA) based on the Trygg and Wold method  [41]  as previously described  [11]. The PCA was used in order to detect any confounding factors (spectroscopy acquisition day, phase) and any outlier samples. The PLS method using the three groups as supervising factors was applied in order to detect the possible discrimination of the three conditions. Finally, OPLS models were used to compare the groups two by two.
Most laboratories interested in autophagy use different imaging software for managing and analyzing heterogeneous parameters in immunofluorescence experiments (e. g. , LC3-puncta quantification and determination of the number and size of lysosomes). One solution would be software that works on a user's laptop or workstation that can access all image settings and provide quick and easy-to-use analysis of data. Thus, we have designed and implemented an application called IFDOTMETER, which can run on all major operating systems because it has been programmed using JAVA (Sun Microsystems). Briefly, IFDOTMETER software has been created to quantify a variety of biological hallmarks, including mitochondrial morphology and nuclear condensation. The program interface is intuitive and user-friendly, making it useful for users not familiar with computer handling.
Finally, image microscopy is less labor intensive and has higher throughput. Appropriate software produces reliable results from large-scale experiments within hours versus months of tedious visual inspection. This improvement is more than an incremental technical advance because it removes the one remaining bottleneck for routinely conducting such experiments. Prior to the work presented here, researchers have used several open-source biological image analysis packages such as ImageJ/NIH Image  7  or CellProfiler. 8 Moreover, commercial software has been developed, mainly for the pharmaceutical screening market, by companies. 9 It is important to note that this software has been directly designed from autophagic measures in our lab and with the intent to improve problems observed with other software applications, which are generally image solutions with autophagy applications.
Such software applications, which are designed to determine autophagy measurements, are mainly used for only specific valuations. 10 owever, with IFDOTMETER, researchers can measure the most relevant hallmarks for determining the presence of autophagy. Here, we describe IFDOTMETER, a software program capable of objectively measuring a variety of cellular changes, mainly involving autophagy and cell survival determinations, with high throughput in a simple and easy manner. In this article, we demonstrate the technical details and provide a brief description of the software, validate the software in a variety of real-world biological scenarios, and demonstrate the breadth of its utility with the hopes of stimulating new ideas and helping the scientific community open their minds to IFDOTMETER software.
Software Description IFDOTMETER was developed to optimize the manner in which analysts study and process images presented in a microscope and acquired by a digital camera. The main aim of this tool is to automatically extract and analyze all of the information from images of interest in a batch mode (i. e. , in a sequential manner without human assistance). Therefore, experts can focus on completing other activities and tasks while the application works and generates the results. IFDOTMETER, in its first version, was created in the Java JDK 1. 6 + Netbeans development environment, which ensures the capability of being executed in any operating system with Java JRE integration, including Windows, Linux, and Mac OS. We have developed the totality of the application, including libraries for image reading and processing, math operations, and data exporting.
The main features offered by this tool include the following: IFDOTMETER has been developed following a schema composed by an interface, which collects all the information required to work from the user, and a processing module, which performs the respective operations to the image sequence (Fig. 1 ). Interface. IFDOTMETER has been designed to measure specific autophagy/apoptosis markers in whole immunofluorescence. It has a user-friendly interface (Fig. 2A ) that can be used to determine all of the parameters described below. File tab. A "File tab" contains a drop-down menu to open either specific files ("Open files" option) or an entire folder ("Open folder" option). With the "Open files" option, you can choose more than one image by clicking Ctrl + a mouse click. Help tab. The "Help tab" contains the authors' names and a direct link to the user manual.
The "Groups" option is used to measure all of the groups in a whole image according to threshold and pixels per group values. 4. Add images: IFDOTMETER can merge images by clicking this option and pressing the "Calculate" button. With more than two pictures, the software automatically matches couples of pictures in the order displayed in the "Image Name" window. Merged images are automatically saved in the same folder that contains the original pictures, which are named Merge 1, Merge 3, etc.
14 n this sense, the IFDOTMETER "Pixels per group" panel provides a simple way to quantify and separate lysosomes of specific sizes. We can label lysosomes with specific lysosomal antibodies such as LAMP1 or lysosomal markers such as Lysotracker Red DND-99 (Invitrogen, L7522). Application of IFDOTMETER was performed as follows: 1. Using MEFs, the conditions for the software were as follows: Threshold: 490, "Groups" selection, from 20 to 550 "Pixels per group" to measure 0. 1 to 0. 5 µm in diameter lysosomes, from 550 to 1400 to quantify 0. 5 to 0. 8 µm lysosomes and from 1400 to 3100 to evaluate 0. 8 to 1. 2 µm lysosomes. 2. IFDOTMETER separated one lysosome from all of the others (Fig. 2F ). The number of lysosomes in each defined range is shown in the "NumGroups" column (Fig. 2G ). 3. Using EBSS as an autophagy inducer, there were 121.
For example, MPP + treatment induces 52. 3% ± 6. 4% of cells with chromatin condensation, whereas 8. 1% ± 3. 2% untreated cells present chromatin condensation (Fig. 3E ). Cautions: It is recommended to obtain images from wellseparated nuclei because the software could consider closer nuclei as one. Mitochondrial Morphology. Mitochondria are highly dynamic organelles regulated by several processes, including fusion/ fission mechanisms. Depending on that, mitochondria appear as an interconnected tubular network or as a fragmented and discontinuous network. We can analyze mitochondrial morphology with immunofluorescence by labeling this organelle with antibodies directed against mitochondrial proteins (as Tom20  16  ), dyes (as Mitotracker  17  ), or plasmids (as DsRedmito  6  ). The application of IFDOTMETER in this case was performed as follows: 1.
Currently, the most widely accepted open-source program for image processing is ImageJ. It was developed in 1997 at the National Institutes of Health. 7 It is a powerful tool that can analyze multiple parameters in a single image, but its interface is mainly focused on single-picture analysis. Although multipleimage analysis is possible, it requires some programming skills that are not easy for members of the scientific community who are not familiar with programming. Regarding this problem, there are other free open-source software programs that attempt to ease batch analysis by avoiding source code, thus providing a simpler interface for users. Such is the case for CellProfiler  8  and Icy. 21 The former is free open-source analysis software that allows users to design their own image processing and measurements by adding modules to a pipeline and choosing specific options in each module.
Moreover, if you are not cautious when establishing conditions in such programs, the spreadsheet provided will contain an excess of information that may result in some additional work to select data that are actually useful. In this sense, IFDOTMETER generates a simple spreadsheet with specific measurements that can easily be used to perform further analysis. Cell Profiler generates multiple Excel files that may contain one discrete value to greater than 100 specific parameters for a single image, depending on the manner in which the experiment was designed; therefore, it is important to be aware of what you are looking for and discard information that is not needed. Icy software provides a single spreadsheet for each image analyzed without a summary table, which encumbers subsequent computational analyses.
In addition to its simplicity, IFDOTMETER includes a useful tool for checking measurements that the program is going to analyze prior to actual analysis. The "Preview" button automatically displays the area that satisfies your criteria colored in green, red, or yellow for a selected image in the "Image BIN window. " This option offers a great advantage because it provides a quick, visual, and easy way to test parameters in representative images before performing analyses. Cell profiler allows users to test whether their pipelines and modules run as well as desired, but the example provided corresponds to the first picture; therefore, it is more complicated to obtain a preview of your image analysis. Icy software marks in each image analyzed the region of interest considered after the analysis, so users are capable of reviewing what has been measured but are not capable of previewing analyses.
In summary, it takes approximately an hour to become familiar with the IFDOTMETER interface, and the "Preview" function allows for checking whether established settings will fit your search in a single click, thus providing users with an easy and intuitive way to understand how IFDOTMETER works and testing whether parameters are appropriate for the analysis prior to perform it. To determine whether IFDOTMETER could obtain results as reliable as other software programs, we analyzed the experiments mentioned above with other software programs. Figure  6  shows the results of LC3 puncta quantification with no treatment or Baf A1 + EBSS treatment that has been analyzed with IFDOTMETER and Cell Profiler. Both programs found significant differences between treatments, and there was no significant difference in the results obtained by each program (Fig.
6C ). The other biological hallmarks described in this article were analyzed with both programs, and the results obtained were similar to those shown in Figure  6 , because they did show differences between treated and untreated cells, whereas there were no differences between program scores (data not shown). Besides, we have developed a survey among four technicians familiarized with IF measurements regarding general user features of both programs, and they have found IFDOTMETER easier to operate and with a more intuitive display screen. Despite the fact that our software does not pretend to cover all scientific community requirements similar to the other open-source programs mentioned above, it is easier to perform multiple and automatic image analysis to measure routine hallmarks than the programs provided by others without losing measurement quality.
Because of this reason, our program becomes useful to researchers with little or no experience in programming and handling computer software or those who are overwhelmed because of information overload. In addition, because our program is open-source software, developers can modify or improve the source code or algorithms for their own experiments or purposes. In this study, a new image analysis software application, IFDOTMETER, was introduced. This software provides an interface for measuring an important number of cellular characteristics in the study of autophagy, including autophagic markers, autophagic flux, quantity and size of lysosomes, nuclear condensation, and mitochondrial morphology. The main advantage of IFDOTMETER is the simplicity in its interface and its ability to process a large number of images in the absence of a researcher.
This characteristic makes it possible to automate different processes, allowing researchers to perform other tasks while the program is working on quantifying results. The usability of the program has been tested in several cell models, including human fibroblasts, mouse embryonic fibroblasts, and neuronal cell lines such as SH-SY5Y. Given the growing interest in studying autophagy and the demand for image screening in the autophagy community, this software can become a powerful tool because it avoids tedious visual analysis of thousands of images, accelerating the rate of discovery. IFDOTMETER can be requested free of charge from Dr. Jose M. Fuentes (jfuentes@unex. es) with the only requirement of citing this article.
Acknowledgments We thank  A. López de Munain  ( Instituto Biodonostia, Servicio de Neurología, Hospital Donostia, San Sebastián, Spain ) for kindly providing HFs and  Dr. Terje Johansen  for the pDest-mCherry-EFGP-LC3B vector. We would like to thank  P. Delgado  and  G. Martínez  for invaluable technical assistance. The authors would also like to thank FUNDESALUD.
Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by  Instituto de Salud Carlos III  ( PI12/02280 ,  PI14/00170  [co-financed by  European Union FEDER  funds],  CB06/05/0041 ). R. G. -S. was supported by a postdoctoral contract from  Ramon Areces Foundation , M. R. -A. was supported by an  FPU predoctoral fellowship  ( Ministerio de Educación, Cultura y Deporte, Spain ), R. A. G. -P. was supported by a talent research contract ( Gobierno de Extremadura , Spain).
Acknowledgments KM wishes to thank  M. Backović ,  M. Krämer ,  F. Malotni ,  A. Martini  and  M. Pellen  for the collaboration, and also  F. Hosseini ,  J. D. Silva  and  K. Yamashita  for the MADDM and MICROMEGAS comparison. The participation in the DSU workshop was supported by the  Theory-LHC-France initiative of the CNRS  ( INP/IN2P3 ).
Could you talk about how you conducted your study? How did you do your fieldwork? G. B. -I had some return visits in 1985 and 1986, and then the later updates were made through other contacts, but not by visiting Ircam. The book manuscript went in, believe it or not, in summer 1993-it then took two years to come out. So, first of all, regarding my access to make the study at Ircam: I was a 28-year-old, female PhD student from Britain. I learned afterwards that a number of French sociologists had been knocking on Ircam's door, including Pierre-Michel Menger, I believe, and they hadn't been successful in getting access to do research on the place. I tell you this just to say that I think I was the beneficiary of a kind of reverse sexism. I believe I was unthreatening, perceived to be just a nice young British woman, a non-entity-someone who would write a thesis that would disappear.
I had this rule, a good anthropological-sociological rule: I wanted to talk to people from the very bottom-the cleaning staff and caretakers-through the secretaries, researchers, engineers, composers and composers' assistants or "tutors," to the very top-with the exception of Boulez, which we can address later. So I began a program of trying to get around all the corners of Ircam, to attempt to chart the culture of the entire institution. I did about one hundred recorded interviews; I took a lot of field "My responsibility is to be bold" Transposition, 6 | 2016 notes, and I observed many, many meetings and concerts. I built up a relationship with a couple of composers; the visit that is detailed most was the young British-Argentinian composer Alejandro Viñao. He was trying to use some of the most advanced Ircam software tools.
He was not a programmer, but he was an experienced and good studio user and a very good electro-acoustician, and so he was a promising composer for Ircam. There was a very clear distinction being made by the younger generation of Ircam staff between those composers who would walk in-for example Harrison Birtwistle, György Ligeti, Gérard Grisey, and others-, who knew nothing about computer music, had no intention of learning, and had always to be given a tutor or an assistant; and those composers who came to Ircam with a fascination, desire, and drive to try to understand what the research staff were doing with the most advanced software tools and so on. Alejandro Viñao was one of the latter. He wanted to try working with the "Chant" and "Formes" programs, environments being developed at Ircam at the time.
George Lewis and I became good friends and colleagues. So a number of key voices in the study have always said the book was insightful and important. Lewis took a great deal from the book; for example, my argument that computer music software is itself immanently encultured and is itself the bearer of aesthetic tendencies informed his evolving attitude towards his own software writing, including his well-known "Voyager" system. 11 So the Ircam study had a number of productive effects for some of those in the book. Adrian Freed remains a friend; he set up the Center for New Music and Audio Technologies (CNMAT) with David Wessel at Berkeley, and when I was at Berkeley in 2014, Adrian told me that the book, and its analysis of Ircam, fed into their thinking about what to do and what not to do with CNMAT.
Two projects looked comparatively at the influence of digitization and digital media in popular musics in Argentina and Kenya; two other projects looked broadly, again comparatively, at electronic and computer art musics and sound art in Britain and the city of Montreal as they are evolving with digital technologies; and these projects on art music were complemented by an ethnographic study of the most globally dominant music software package, the programme Max, which originated in Ircam. A further study focused on the digital recording, curation, and archiving of folk musics in North India. And then three studies focused broadly on consumption: one took Cuba and Montreal as sites, respectively, of digital scarcity and digital plenty; another study examined the illegal or "grey" economies that have sprung up around the sale of music for consumption on mobile phones in India; and a third ethnography involved a comparative study of internetbased music consumption in the guise of Spotify and an illegal peer-to-peer platform.
Quality of ToF data The raw measurements provided by ToF cameras are impaired by errors of different natures. A comprehensive panorama of rangecamera errors is out of the scope of this paper and we refer the reader to e. g. (Lefloch et al. , 2013 , Rapp, 2007)  for more insight about these phenomena. In this section, we illustrate systematic and random errors on a simple example. The repeatability of the raw data of the SR4500 was studied with 50 acquisitions of a white wall placed at 2 m of the camera. The integration time was set at 3. 3 ms. The standard deviation of these 50 measures was computed for each pixel and is shown in Fig. 4. In the central part of the image, the standard deviation is between 2 and 3 cm and it reaches 9 cm in the corners. We note that these results are qualitatively similar to those obtained by Kahlman  (Kahlmann et al.
Internal heating time and integration time As all photonic devices, range camera sensor needs time to reach stability  (Kahlmann et al. , 2006 , Rapp, 2007). For example, with integration times between 20 and 110 ms, the SR4000 needs about 40 minutes to reach stability  (Chiabrando et al. , 2009). In this experiment, we studied the internal heating time of the SR4500, at For each integration time, 50 acquisitions were taken every 5 min and we studied the raw data given by the Mesa software. The median and the standard deviation are presented for the central part of the image (30×24 pixels) in Fig. 5. The measured distance almost corresponds to the real distance at 3. 3 and 5. 5 ms while there is an important distance offset at 10 and 20 ms. Since the default value of the Mesa SR4500 integration time is 3.
Distance calibration The calibration of the orthogonal distances is computed from successive acquisitions of a plane for different camera positions. These successive planes (Fig. 8 ) are obtained by moving the camera and making acquisition of a wall. At each scanning position, two Trimble M3 tachometers with a measurement precision of 3 mm are used for locating the camera and measuring its distance from the observed wall. To make the absolute localisation of the SR4500 ToF camera easier, several stickers are applied on it (see Fig. 11 ). To find the stickers coordinates and the checkerboards centres in the world scene, the tachometer data are processed with the Autocad COVADIS plug-in. Four checkerboards are also placed on the wall (Fig. 12 ) and their four centers are acquired by each tachometer.
A crucial point for the method is to measure the reference planes accurately. Once 3 points on the camera and 3 points on the wall are known in world coordinates, it is possible to estimate the 3D Helmert transformation between the camera and the reference plane. However, the stickers applied on the ToF camera are too close to each others, and the obtained precision is not sufficient, especially on the components of the transform. Fig. 13 (a) shows that the reference and measured planes, that should be parallel, are indeed secant. To fix the reference plane orientation problem, we estimate the rotational components using the normal to each plane. Since the SR4500 data are very noisy, the normal to the camera point cloud is estimated using a robust Principal Component Analysis (PCA) algorithm, see  (Moisan et al.