dataesr/scientific-paragraphs-categorization huggingface dataset:
First, he confirmed the heterogeneity of sun exposure by using captors to carry out measurements of the radiation transmitted to soil level, as well as measuring the diffuse radiance under the palm trees. Then, by dividing the surface area into elementary triangles, he found that the average rate of transmission per triangle obtained by simulation using the AMAPpara software was very close (to within ±1 of 10%) to the measured rate. 30 The simulation therefore closely replicated the heterogeneity of the radiance. Lastly, using the same mock-ups, Dauzat simulated the total radiative climate, with its re-diffusions and re-interceptions, by using the computer to follow the history, or in other words the evolution, of a large number of incident beams originating from the sun throughout the day.
This phenomenological law, which had been known to ecophysiologists since the 1930s, postulates that the matter created during photosynthesis is continuously proportional to the plant's transpiration. This is true in cases where the plant is not subjected to hydric stress. At first glance, however, the transpiration itself depends entirely on the architecture. Since the AMAPpara software accounted for the primary growth schedule, including its actual sequence and its topology, if it was possible to evaluate the transpiration at each stage, then it should also be possible to precisely evaluate the production of matter itself. All that would remain to be done would be to allocate it differentially in order to simulate the secondary growth of the organs. This was all the more feasible since local allometry laws were available for each organ.
But at each of these stages, certain decisive variables were no longer subject to probabilistic construction, as was still the case in the AMAPsim software. The variable Q n , for example, was calculated analytically in accordance with the results of the architectural simulation at each stage. The plasticity of the architectural models (which for that matter retained their determinism within a stochastic framework) was thus controlled by the architecture itself, in its relationship to the water efficiency. Thanks to the phenomenological law of water efficiency, which is a validated mathematical model, we see the beginnings of a feedback effect from the architecture on the production by photosynthesis, and of a feedback from this production on the expression of the architectural model (its speed).
During the years that followed, Prusinkiewicz and his University of Regina computer graphics students developed what he called a "Virtual Laboratory" in botany. In reality, however, he retained his computer-scientist approach: the virtual laboratory, which he had hoped would be a decisive aid for botany, proved to be essentially a large database of visual data, hierarchized by categories of objects. It was conceived as an attempt at object-oriented programming based on his first software in C language in 1986: Pfg (short for Plant and fractal generator). The main function of the program was to present scientific information visually. Prusinkiewicz asserted that "new experiments" 13 could be carried out using this program, but he was thinking in terms of pedagogical experiments: Prusinkiewicz considered that -in the line of approach adopted by Piaget, Papert, Abelson and di Sessa -there was a continuity between scientific experimentation that produced truly new information and the type of learning experiments typical of students.
18 But since it was not possible to transcribe the dynamics of the rhizome, with its births and deaths, using these simple graphical marks, Bell came up with the idea of using a computer system that could illustrate the dynamics of growth and senescence over the course of time. He was convinced that it would be useful to retain a spatialized representation, and that it was necessary to use the computer in order to do so. Bell called his software RHIZOM, but he scarcely developed it, since he later began working in a less formalized manner on flowering plant morphology. At any rate, the common determination to spatialize  (Bell)  and to take architecture into account (de Reffye and Dauzat) played a key role for Kurth in the later convergence between logical models and architectural simulation in forest ecology.
Thus, in 1994, Kurth developed his own software, christened GROGRA (for GROwth GRAmmar), on a Silicon Graphics IRIX 5. 2 workstation. He kept Prusinkiewicz's L-system approach using turtles, but he considerably extended the rewriting rules so as to include more of the substance of the botanical laws that de Reffye and his team had highlighted earlier. To this end, Kurth met the AMAP researchers several times, and he recognized the debt he owed them. In order to move towards even greater botanical accuracy, Kurth was obliged to include no less than 26 different rules in his L-systems (as compared with Lindenmayer's original three). Since in this way the formalism became very intrinsically heterogeneous, the priority no longer lay in emphasizing the theorems that might, a priori, be drawn from it, even though this concern, which dates back to Lindenmayer, remained a consideration for Kurth.
With this increased computing complexitygreatly assisted, it is true, by the use of object-oriented programming -Kurth was able to precisely calibrate GROGRA on a 14-year-old spruce by taking measurements similar to those of AMAP. This shift towards architecture can also be seen in the forestry modelling work, starting in 1996, carried out by a team from the Finnish Institute of Forestry Research at Helsinki. Inspired by AMAP, and more particularly by Kurth, Jari Perttunen's team chose to dispense with all logical or mathematical formalism from the start, and began straight away writing an object-oriented program in C++. The team called this program LIGNUM because it also was intended to consider the functioning rather than just the architecture. The model was purely computer-based. It considered the tree as "a collection of simple units corresponding to the organs".
22 After a recap of the requirements in botany by Caraglio, Godin gave an exhaustive, systematic and almost axiomatic presentation of his "multi-level model" concept that had been integrated into the AMAPmod program since 1996. The set of axioms for the graphs displayed an unprecedented flexibility. Leaving aside simulation, but making the most of the distinctions acquired by AMAP in its earlier simulations, this particular set of axioms emerges as a new type of formal modelling of language and of plant-structure analysis. It is possible to define the projection operators between the different scales of description within the plant. The very restrictive central hypothesis that Godin set for himself, however, was that the multiscale model had a recursive structure. In other words, the structure of the model did not depend on the scale on which the plant was described, even though on different scales certain different attributes emerged.
They set GROGRA aside for a while in order to interface LIGNUM with AMAPmod. In this way, AMAPmod gradually became a standard of formal representation of plants in the more theoretical world of architectural modelling. As with some earlier cases in the history of computer science and software, there came a moment when compatibility was necessary in order for the dialogue to continue, and, as Dzierzon and Kurth wrote, "[the] intersubjectivity reduces the bias of our perception of ecological reality". 26 According to Dzierzon and Kurth, only in the presence of a common software, with a common formalization, would it be possible to continue to discuss ecological reality. However, a model structure, even when generalized, remained a simplification for the authors. The main role of interfacing was to expose the hidden assumptions in each model: the models could then be objectively compared and could criticize each other objectively, since this task had been delegated to the machine.
De Reffye and his students, including Xin Zhao and Hong-Pin Yan, discovered that this was because the meristems often ultimately generate only a small number of typical sub-structures in order to create the entire tree. Some simulated trees might contain the same sub-structure as many as 600 times. The 1991 "reference axis" was therefore abandoned in the new software, which was christened GreenLab for this occasion. In 2001, initially inspired by Godin's approach of nested sets, but having modified it in accordance with their observations regarding both the functioning of stochastic simulations and the observed real-life hierarchy of botanical architectural units, Xin Zhao and de Reffye proposed the notion of a "dual-scale automaton model". This was a two-level automaton; an automaton of automata.
35 In order to construct a simulated tree, it was no longer necessary to rely on the scale of the metamers from the outset. A sort of sub-structure was calculated once and for all, and once the automaton had ordered its reiteration with a certain probability, all the program had to do was to retrieve from the memory the content of the pre-calculated topological and geometric parameters in order to display this new sub-structure. 36 Almost no further calculation was then necessary. As a comparison, a 20-year old cherry tree took two minutes to be calculated by AMAPsim, and less than one second using GreenLab. In this specific case, the architectural simulation was therefore approximately 200 times faster. 37 This new computer model of course still used a Monte-Carlo type simulation, but in a much more limited manner than previously.
In order to achieve the main aim -the coupling with ecophysiology in order to create a structural-functional model -GreenLab largely reused the concepts and equations of AMAPhydro by considering the production of biomass, leaf by leaf, as a function of the architecture. 38 Finally, in this regard, it should be noted that because of this remarkable reduction in the number of computation steps necessary, GreenLab functioned regularly from 2002 on a simple PC running the Matlab formal calculation software. 39 This conceptual progress, together with the technological advances that have occurred in the micro-computing industry since then, have meant that AMAP could finally give up the systematic need for very rare and expensive graphics workstations. In turn, this has facilitated its international distribution as well as making it easier to compare its performance with that of other models.
The structural-functional model was then rapidly calibrated on plants -initially on plants without branches, such as the sunflower, cotton plant or maize. The collaboration of the Chinese University of Agronomy was invaluable here: it was they who dealt with supplying the necessary experimental data for the calibration. The calibration technique was made easier thanks to the formal simplifications due to the AMAPhydro step and to the development of the dual-scale automaton. This was one of the immediate effects of remathematizing the AMAP model: relatively classic statistical fitting procedures such as generalized least squares could finally be applied directly in order to economically identify the parameters. 40  Everyday use of GreenLab in agronomy thus finally seemed feasible. Another major outcome of this remathematization began to appear after 2001.
42 Both Quadrat, and especially Goursat, had participated in the conception of the Scilab software for the numerical resolution of optimization problems in science. This platform, developed in partnership with the École des Ponts 43 since 1990, has been a free software since 1994. The authors intended to integrate GreenLab in Scilab. This project retrospectively and publicly justifies the use of the "-Lab" suffix. The return to equations thus made it possible to "de-spatialize" to a certain extent the formalism of morphogenesis in order to reinstate in it the linguistic linearity of the algebraic equation or, at the very least, of the combinatorial analysis. The equation itself, while it cannot be inversed in order to give the optimal values for a given plant production directly, can at the very least be manipulated by fairly classic numerical optimization techniques.
Thus, in Chapters 3 and 5, I demonstrated that the AMAPsim software, for example, could produce an r-simulation of the dynamics of a system (an r-simulation of the "growing tree" system) by means of ex post reconstruction and using a stop-motion technique, without constantly relying on a p-simulation by dynamics, although this was what the AMAPpara software did. As we can see, what we are looking for in the temporal dimension of an r-simulation is that it should be able to refer iconically to the temporal dimension of the target system as seen from a dynamic perspective, for example. For this to occur, however, it is not always necessary that the p-simulation process that it derives from should itself be equally iconic in terms of the target-system dynamics. Furthermore, it should be recalled that an r-simulation, by nature, does not have to possess a temporal dimension.
Lastly, I will not go into detail again on the updated distinction that I drew -using the contrast between the first AMAPsim software and the AMAPpara software as an example -between resemblance by dynamics and resemblance of dynamics. Not only is it not necessary that the computation process should correspond at each step of time to a realistic phase of an actual process, as Wendy Parker essentially acknowledged, but the result of a process-simulation, termed "result-simulation" or "r-simulation" in Chapter 8, does not necessarily need to have a temporal aspect. Second, not every simulation is necessarily the calculation of a model. Here too, we must agree: if, by "model", we mean the mould or the container of all the heterogeneous rules that are set up in order to carry out a series of computations, then it is clear that there can be no computer simulation without a model.
It runs counter to the condensation function of traditional symbolic instruments (logic, mathematics). In this regard, a simulation is not always simply either the immersion of a model in time (as Stephan Hartmann suggests: doing so would restrict it to one single dimension of dilation), or the replication of a dynamic process per se. Although some of the simulation's results tend to be mimetic, it does not necessarily also have a mimetic dynamic. In other words, its successive states do not necessarily correspond mimetically to the states of the phenomena being simulated (this is the case of the first version of the AMAPsim software, compared with AMAPpara). For more distinctions and examples regarding computer simulations, see Chapter 8. Discrete event simulation A type of software-based simulation fostered by object-oriented programming, in which not only the numeric variables describing the system are discrete but also all the elements and possible events in the system are directly and strictly represented as discrete.
See the pioneering work of Hisao  Honda (1971). Growth unit Indicates the portion of stem that appears during a period of elongation. It may contain several internodes, since the latter are formed in the meristem and are only liable to elongation at a later point in time. Internode Bud-less segment of plant stem between two nodes (the points of attachment of leaves on the stem). Logical model Model based on the formal languages that were proposed in keeping with the logicist movement on reforming mathematics, such as that advanced by Alfred North Whitehead and Bertrand Russell in Principia Mathematica. In this regard, see the proposals of Joseph Henry  Woodger (1937)  and Aristid  Lindenmayer (1964 Lindenmayer ( , 1968 ). L-system Formal parallel rewriting grammar proposed by Aristid Lindenmayer in 1968.
Glossary 189 Numerical simulation Numerical simulation differs from software-based simulation in that it remains a step-by-step and approximated algorithmic processing of a formal construct that possesses beforehand a mathematical unity and homogeneity of conception and construction. This prior unified mathematical formulation does not exist, however, either in the case of algorithmic simulations, which are sometimes awaiting a theory (cellular automata), or in the case of software-based simulations. Object-oriented programming Type of programming that, from the end of the 1960s, and based on the SIMULA, SIMULA 67, C and C++ languages, broke with procedural programming in which the program was meant to be able to control from above the processes affecting the phenomena being modelled. By contrast, this type of programming defines the objects that then engage with other objects in accordance with the various attributes and behaviours that characterize them.
has not been abandoned. This shift is very clear in his latest publications. He is trying to interface many other models so as to be able to compare them on an objective basis, i. e. , from a quantifiable point of view. See Kurth (W. ), "Spatial structure, sensitivity and communication in rulebased models", in  Franz Hölker  (Ed. ),  Scales, Hierarchies and Emergent Properties in Ecological Models, Frankfurt am Main ,  Peter Lang ,  2002 , pp. 95-104;Dzierzon (H. ), Kurth (W. ), " LIGNUM: A Finnish tree growth model and its interface to the French AMAPmod database ", in  Franz Hölker  (Ed. ), ibid. , pp. 29-46. Ibid. , p. 46. Kurth (W. ), 2002, art. cit. , p. 99. See Fournier  (C. ), "Modélisation des interactions entre plantes au sein des peuplements. Application à la simulation des régulations de la morphogenèse aérienne du maïs (Zea mays L.
g. , polyphase model), ad hoc model, asymptotic model, multi-scale model Database, open ontology, popularization model Multi-aspect model for consultation, common scenario- exploration models Action research model, participatory model, companion modelling Decision model, decision trees, crisis-management model, decision-making heuristics Market forecast model, financial derivatives modelling Specific functions 11 Facilitate the interpretation of a theory: model of the theory 12 Facilitate the illustration of a theory: model for the theory 13 Facilitate an internal coherence test of the theory 14 Facilitate the applicability of the theory 15 Facilitate the calculability of a formal theory 16 Facilitate the hybridization and co-calculability of several theories 17 Facilitate communication between scientific actors 18 Facilitate consultation and collaboration between stakeholders 19 Facilitate the co-construction of representations and means of verification of mixed systems (human/ non-human) 20 Facilitate action on a mixed and heterogeneous system 21 Facilitate decision-making on action in a principally notional system (continued) Main function IV Facilitate the co-construction of knowledge V Facilitate decision-making and action.
Scale bars, 10 mm. Pearson correlation coefficients (PCCs) were calculated using ImageJ software (n = 5). Images are representative of at least three independent experiments. (B and C) HeLa TIM-1-expressing cells were transfected with a siRNA pool targeting clathrin heavy chain (CHC) or a non-targeting (NT) siRNA pool. Cell surface expression of TIM-1 and CD71 was assessed 48 hr after transfection by flow cytometry (B) or by confocal microscopy (C) under non-permeabilized conditions. Data shown are means ± SD of two independent experiments. (D) Confocal microscopy analysis of TIM-1 and DENV2 internalization. HeLa parental or TIM-1-expressing cells were incubated with DENV2 AF594 and mouse anti-TIM-1 primary antibody for 1 hr at 4 C and shifted to 37 C. At the indicated time points, cells were treated or not treated with trypsin, permeabilized, and immunostained with a goat anti-mouse Alexa 488-conjugated secondary antibody.
Cells were challenged with DENV2 JAM 24 hr after transfection. For each sample, infection was quantified by flow cytometry in the GFP-positive population. Data shown are means ± SD of two independent experiments. For (B) and (E), significance was calculated using one-way ANOVA statistical test with Tukey post-test (*p < 0. 01, **p < 0. 001. route for productive infection  (Acosta et al. , 2008; van der Schaar et al. , 2008). To study the kinetics of TIM-1 internalization from the cell surface, HeLa TIM-1 cells were co-incubated with saturating concentrations of a non-neutralizing anti-TIM-1 monoclonal antibody (mAb) and AF594-Tf for 1 hr at 4 C. Cells were either kept on ice (0 min) or shifted at 37 C to allow endocytosis and then analyzed at various time points. Internalized TIM-1/anti-TIM-1 complexes were detected using a fluorescent secondary antibody (Ab) under permeabilized conditions.
TIRF microscopy analysis showed that TIM-1 GFP puncta displayed distinct dynamic dots at the plasma membrane (Figure  3A ). Using TrackMate and MATLAB software, we analyzed 7400 TIM-1 GFP spots trajectories from 10 cells. TIM-1 exhibited three different types of motions at the cell surface (Figure  3B ). Although the majority of TIM-1 stayed confined within a limited area of the plasma membrane (76%), the remaining portion was either transported across the cell surface (19%) or diffusive at the plasma membrane (5%) (Figures  3B  and 3C ). Because TIM-1 internalizes through CME, we determined whether these confined TIM-1 molecules were associated with CCPs. TIRF studies performed with TIM-1 GFP cells transiently expressing dsRed-clathrin light chain (CLC) (Figure  3D ) showed that the majority of TIM-1 spots that co-localized with CLC displayed a confined displacement (Figure  3E ).
These data strongly suggest that TIM-1 is mostly confined in CCPs at the plasma membrane. To record virus internalization in live cells, dual-color TIRF images were acquired at 37 C immediately after DENV2 AF594 addition to the culture medium of HeLa TIM-1 GFP cells. The small size of DENV particles (50-60 nm in diameter) allowed virus diffusion into the narrow space between the cell and the coverslip. The dynamic of viruses bound to the bottom of the cells was thus accessible via TIRF imaging (Figure  3F ). Robust individual viral particle tracking requires high-speed imaging and low particle density. We therefore exposed cells to DENV2 AF594 at an MOI of 1 and immediately acquired images every second for 20-40 min. The dynamics of 27 DENV particles (in 8 cells) that associated with TIM-1 GFP were recorded.
Cells were then treated with trypsin to eliminate viral particles remaining at the cell surface, and vRNA was detected by FISH (Figure  4E ). In a second approach, we incubated parental and TIM-1-expressing cells with DENV2 AF594 and quantified virus binding and endocytosis using Fiji software. As expected, TIM-1 WT and TIM-1 Dcyt similarly bound DENV2 AF594 particles (Figure  4F , 0 min (4 C) -trypsin). Trypsin removed the signal at the cell surface (Figure  4F , 0 min (4 C) +trypsin). However, after 30 min at 37 C, TIM-1 Dcyt cells showed a strong defect in viral particle internalization compared with TIM-1 WT cells (Figures  4F  and S4D ). We then recorded DENV2 AF594 internalization in live cells using spinning disk confocal microscopy. Using Fiji and TrackMate, we quantified the number of internalized DENV2 AF594 particles every 2 s for up to 20 min (Figure  4G , red dots).