dataesr/scientific-paragraphs-categorization huggingface dataset:
Floral induction (FI) in shoot apical meristems (SAM) is assumed to be triggered by antagonistic endogenous signals. In fruit trees, FI occurs in some SAM only and is determined by activating and inhibiting signals originating from leaves and fruit, respectively. We developed a model (SigFlow) to quantify on 3D structures the combined impact of such signals and distances at which they act on SAM. Signal transport was simulated considering a signal 'attenuation' parameter, whereas SAM fate was determined by probability functions depending on signal quantities. Model behaviour was assessed on simple structures before being calibrated and validated on a unique experimental dataset of 3D digitized apple trees with contrasted crop loads and subjected to leaf and fruit removal at different scales of tree organization.
The model assumes a decrease in signal quantity with the distances from the emitting sources. FI in shoot apical meristems (SAM) was simulated based on inhibition and activation probability laws depending on both the quantity of inhibiting and activating signals. By fitting the model to a unique dataset on 3D digitized apple trees manipulated for their number of leaves and fruit 10 , we quantified the combined impact of such signals and distances at which they act on SAM and explored the underlying mechanisms that could explain within tree variability in FI.
Material and methods Model overview. The model (SigFlow) was developed in python and uses libraries from the OpenAlea platform  30. The model runs on 3D tree architectures coded in Multiscale Tree Graphs  16  (MTG) with three scales (tree, stem segments and metamers) and augmented with organ 3D coordinates  31. Segments are the parts of the stem between two branching points or one branching point and the axis extremity. Metamers are represented for the stem segments corresponding to the most recent shoots only and are composed of one leaf, one internode and an inflorescence if present (Supplementary Fig. S1 ). Fruit and leaves produce signals moving within the structure depending on the distance with an 'attenuation' parameter that can be tuned in order to simulate different signal distributions i.
Description of the experimental dataset. Data used for building the tree 3D representations and for calibration and validation purpose were taken from experiments carried out in 2017 on 10-year-old apple trees (cv. 'Golden Delicious') orchard, located at the SudExpé experimental station in Marsillargues, in the south of France (43° 66′ N 4° 18′ E). In this experiment described in Belhassine et al. 10  , leaves and/or fruit were removed in different parts of the tree (Supplementary Fig. S2 ). On trees set in ON (high fruit load) or OFF (crop load close to 0) conditions, fruit or leaves were removed on half of the shoots and half of the branches of trees trained as "solaxe" (one main vertical trunk) and on one side of trees with a Y-Shape (two main trunks). An additional set of trees not subjected to fruit or leaf removal but displaying a natural variability in crop load were observed during three years (2015-2016-2017).
3D coordinates were acquired using an electromagnetic 3D digitizer (3Space Fastrak; Polhemus Inc. , Colchester, VT, USA) at the trunk base, branching points and top and bases coordinates of each annual shoot (Supplementary Fig. S1 ). The 3D structures, including tree entities organized in three topological scales and their coordinates, were saved in MTG format. These structures were used to reconstruct leaf location and area along annual shoots and to reproduce in silico experimental treatments of leaf and fruit removal. Leaf area distribution along annual shoots was reconstructed based on allometric relationships as previously proposed  34  (Supplementary Methods S1). To build allometric relationships, data were collected on both 60 short (< 5 cm) and long shoots (> 5 cm) and individual leaf areas were estimated with a leaf area meter (LI 3100 Area Meter, LI-COR, Lincoln, NE, USA) every three leaves along the shoots.
Acknowledgements The authors thank the other  AFEF team members and SudExpé  for technical support during experiments and UMR PIAF for having provided the digitizing device. F. Belhassine PhD  was funded by  INRA  ( BAP department ) and ITK company. This work was also partly funded by the  ANR-DFG   Alternapp  project. The authors also thank  Dr J. Hanan  for his valuable advices on the model for his help in editing the manuscript.
How to provide students with scientific practice through virtual environments remains a question that has Open Access Higher Learning Research Communications 35 been challenging teachers and researchers. The term scientific practice refers to the set of norms and genres that are involved in the scientific activity, including development of testable hypotheses, evaluation of data obtained in testing hypothesis and theory, producing explanations, and communicating the results with peers. Reiser, Berland, and Kenyon (2012)  discussed how the practice of explanation in science is different than simply describing a given phenomenon. It comprises a set of processes of reasoning that aim to identify possible patterns in data that support the building of models and to fill in gaps in the explanation.
Science education via an online-learning approach can lead to the development of activities with virtual interactions without losing important features of the teaching-learning process. For example, teacher's mediation to promote a social and collective construction (involving language, conceptual knowledge, and interactions between students) can be effective in the online environment through using tools in virtual spaces that promote dialogue between the students and also among teachers and students. Recently, we have shown the potential of online investigative activities for the development of argumentative writing, an important marker of scientific literacy of high school students  (Mello, Marzin-Janvier, & Manzoni-de-Almeida, 2018). They engaged in a problem about antibody specificity and produced a report through the online LabNbook platform.
The resulting hybrid learning activity was organized in three phases as described in Table  1. In Phase 1, the teacher presents a theoretical class on the complement system and engages the students with the problem and the experimental question previously formulated by other students who had been enrolled in the face-to-face course before. The raw data from the experiments performed previously by other students is made available to students in the virtual learning environment Moodle. In Phase 2, students explore and explain the data by analyzing data, producing inscriptions relevant to the scientific field (graphs and tables, for example;  Latour & Woolgar, 1986) , and discussing findings with their peers, and draw conclusions about the results. In Phase 3, students share data and experience about the process of analyzing results with peers, being mediated by the teacher through the virtual learning environment.
The organ transplantation activity through online learning activity Design of this learning activity was based on the face-to-face activity described by Manzoni de  Almeida and Trivelato (2015)  and Manzoni-de-Almeida et al. (2016). Briefly, the face-to-face activity consisted of exposing and engaging students in a problem concerning science and health of organ (bone marrow) transplantation. Numerical data obtained by the professor from classical articles on polymerase chain reaction (PCR) and northern blot were made available to students. In short, students should use the data to identify cell types in the hypothetical sample obtained from mice bone marrow. These cell types could be undifferentiated cells, pre-B cells, and differentiated B cells. Each group received the data of one specific cell type.
The face-to-face approach was adapted in three stages for the online learning activity (see Table  2 ). The online learning activity was performed with the same material and procedures as the face-to-face activity. During Phase 1, students received through the virtual learning environment an explanation about the activity and received the raw hypothetical data previously obtained from the seminal articles. During Phase 2, students had to analyze the data received. To carry out this step, students were divided into groups that interacted through the Whatsapp© mobile application to negotiate the construction of graphs using the numerical data and to draw a conclusion to solve the problem. In Phase 3, students share data and experiences about the process of analyzing the results with peers, which is mediated by the teacher.
Related Works First, we analyse, in the following, how IoT platforms understand the integration of objects and how they design applications as service composition, and then we focus on human-machine interaction in IoT. BlueMix (IBM Bluemix 2019) is a platform "as-aservice" (PaaS) cloud, developed by IBM. It supports rapid development of analytic applications, visualisation dashboard, and mobile IoT applications. IBM secures the platform and infrastructure and provides the user with the tools to secure his application and connect his device data with it. IBM IoT foundation (IoTF)  (IBM Watson IoT Platform 2015)  is the hub where the user can set up and manage your connected devices. A device, in order to be connected, will require a device management agent that is a collection of logic installed on a device that allows it to connect to the Cloud Internet of Things services as a managed device.
AWS IoT (AWS IoT 2019) is a platform that enables users to connect devices to AWS Services (Amazon Web Services 2019) and other devices, secure data and interactions, process and act upon device data, and enable applications to interact with devices even when they are offline. It provides secure, bidirectional communication between Internet-connected things (such as sensors, actuators, embedded devices, or smart appliances) and the Amazon Web Services (AWS) cloud. This enables users to collect telemetry data from multiple devices and store and analyse the data. The rules engine makes it possible to build IoT applications that gather, process, analyse and act on data generated by connected devices at global scale without having to manage any infrastructure. Azure IoT Hub (Microsoft Azure IoT Hub 2019) is a fully managed service that enables reliable and secure bidirectional communications between millions of IoT devices and a solution back end.
Azure IoT Hub can reliably receive, process or store millions of events per second from devices for analysis and provides extensive monitoring for device connectivity and device identity management events. Service composition is often done manually by a software architect or sometimes computed automatically. (Cavallaro et al. 2010 ) presents an approach allowing to develop a service-oriented system, based on a model called service tiles, by building an assembly of service components that accomplishes a given goal. The assembly is computed automatically starting from the specification of a subset of the whole system, a few constraints, and the goals the application should fulfil. MACODO  (Weyns et al. 2010b,a)  uses a partially distributed architecture based on a master-slave schema. The master has complete knowledge of the assembly state and controls the dynamics in a centralised way.
IoT applications building is based on actors which are reusable software components that can represent a device, a computation, or a service. It comprises both a development framework for IoT application developers, and a runtime environment which handles the running application. Compositions are made by writing scripts called CalvinScript. Basically, an application consists of actor instances and connections between the ports of the actors, forming a data flow graph. To allow reuse scripts, it is also possible to define components. An application script can also contain deployment rules. CHOReOS  (Autili et al. 2014 ) composes distributed services by considering a global specification, called Choreography, of the interactions between the participant services. It enables large scale compositions or choreographies of QoS-aware, and heterogeneous services in IoT.
Quality in the service and interaction areas can be evaluated from different perspectives and therefore using different measurement methods: i) the first is related to the reliability of the software or equipment and can be measured accurately via technical means. ii) the second is intended to measure the subjective satisfaction of the customer and there is often no other means than a survey to get it (for example, usability tests like the SUS (Affairs 2013)). Objective and subjective measurements may be usefully combined for a better assessment of the whole user approach and is what we call Quality of Experience (QoE). The subjective part is what we name User Experience (UX). The UX design is quite similar to Interaction Design (IxD) in its approach. IxD defines the structure and behaviour of interactive systems (IxDA 2018).
JSF is a serverside technology that aims to facilitate the development of the user interface by clearly separating the "interface" part from the "business" part. JSF offers the assembly of server components which generate the code of their rendering and the management of the state of the graphical interface components. JSF is based on the notion of components, like that of Swing or Standard Widget Toolkit, where the state of a component is recorded when rendering the page, and then restored when the query returns. JSF is agnostic with presentation technology. It uses Facelets by default. Facelets are a presentation technology for the development of web applications in Java. It creates JavaServer Faces (JSF) views using HTML style templates and creates component trees. The composition of the functional core is studied in software engineering, and more specifically in component and service approaches.
Proposals for the QOS control of an IoT service composition We have applied SCC in the Cloud model. We now apply it to the IoT domain. In these areas, decisionmaking is based on data collected, computed, derived from measurements, synthesised and displayed but we are not sure about the veracity of these values. Indeed, if the processing time is too long, the information displayed is obsolete and no longer corresponds to reality. We must be sure that the processing performed by services is valid and compliant with their designs. There is a need for self-control mechanisms. Our component has proved its worth in the Cloud (The OpenCloudware project 2015; Tatiana Aubonnet and Noëmie Simoni 2013;  Aubonnet et al. 2015). In this section, we apply it to the IoT. The integration of numerous real-world objects, or things, onto the Internet, which aim is to create new high-level interactions with the physical world, is at the heart of the Internet of Things.
The analysis is done on site. Only its result is sent so that the volume of data exchanged and thus the communication resources are extremely low. The code is simplified and hence requires less computing resources. Monitoring and controlling components are generic so they are independent of the functional component and may be present at all levels of architecture. They are not intrusive because they are external to the functional component. They are inside the service component membrane and operate in parallel with the functional component. They have no effects on the second. We measure a QoS of each component (hardware or software) allowing better diagnostic of various malfunctions whereas most existing tools monitor network traffic or central processing unit (CPU) usage when they should monitor the functional component performance.
The Apollo Guidance Computer was saturated and could no longer perform all the tasks assigned to it. The overload of the computer was due to the sending of processing requests by the appointment radar at a frequency too high. The procedure incorrectly indicated to keep the rendezvous radar on. The overload was caused by the large flow of data from both the landing radar and the rendezvous radar that remained on. We will show that our approach makes it possible notably to detect this type of problem but also to precisely locate which service is the cause. The saturation of a computer due to an increase in requests can indeed be detected by the QoSControl. The response time would exceed the expected threshold and trigger an OutContract. In the case of crewed aircraft, the user would then thus know that the data displayed on the screen would not be reliable and could act accordingly.
Only two connections are required: GPIO Port power and a flat cable that connects to the DSI port. The monitor has a multi-touch screen for 10 fingers (Fig. 3 ). Both platforms include an Inertial Measurement Unit (IMU). In the case of the Raspberry Pi card, it is part of the Sense HAT expansion card. Note that Android Things operating system has been specially designed for the Internet of Things. Even if both operating systems (Android and Android Things) share common APIs which makes it easier to share the code from one to the other, Android Things is still under development and the code needs to be often adapted to a version change. Using a combination of accelerometers, gyroscopes and sometimes magnetometers, an Inertial Measurement Unit is an instrument used in navigation to estimate the orientation of a moving object (roll, pitch and yaw angles), its linear velocity and its position.
2d  and Supplementary Movie 1). Since kymographs can provide information regarding the velocity of the flow only at one arbitrarily defined point, we set out to assess whether reducing E-cadherin would affect the dynamics of actin around the entire cell contour. To this end, we employed the BioFlow software, which allows for non-invasive, global visualization and analysis of flow fields based on temporal analysis of the positioning of fluorescently labeled structures  35. In our study, as it has been described previously  36  , the BioFlow software reported a movement of cytoplasmic EGFP into the forming bleb; however, we also found that the mCherry-labeled actin structures were flowing away from the region of the forming protrusion (Fig. 2e  and Supplementary Movie 2). In addition, upon a similar analysis conducted prior to bleb formation, we also observed retrograde flow of the E-cadherin-EGFP molecule, although at a slower speed than that of actin (Supplementary Fig.
Migration track analysis. Time-lapse imaging was performed using an Axio. Imager. M1 (Zeiss) and an Axio. Imager. Z1 (Zeiss) controlled by the VisiView® software (versions 2. 1. 4 and 4. 2. 0. 2, Visitron Systems, GmbH). Imaging was performed using a ×10 water-immersion objective (Zeiss, NA 0. 3) at a rate of one frame per 2 min over 120 min in total, starting at 6 hpf. Three focal planes (50 μm apart) were imaged to generate Z-stacks. Assessment of somatic phenotypes. Imaging was performed on 10 hpf embryos using a LeicaMZ16F microscope equipped with an AxioCam MRc5 (Zeiss) and controlled by the ZEN software (version lite 2012, Zeiss, Germany).
Subcellular localization of DN E-cadherin-EGFP. Imaging was performed on 8 hpf embryos using an LSM710 confocal microscope (×20 and ×40 water-immersion objectives, Zeiss, NA 0. 5 and NA 0. 75, pinhole 69 μm, 1024 × 1024) controlled by the ZEN software (version 2010 B SP1, Zeiss, Germany). Subcellular localization of E-cadherin-EGFP in the PGCs. Time-lapse imaging was performed on 7-9 hpf embryos using a Zeiss AxioImager. M2 microscope controlled by the VisiView® software (version 4. 0. 0. 14, Visitron Systems, GmbH) and equipped with a dual view filter (MAG Biosystems, Exton, PA), Photometrics cameras (Cascade II and CoolSNAP ES2) and VS-Laser Control. Frames were captured at 2 s intervals using a ×63 water-immersion objective (Zeiss) with 488 and 561 nm lasers and 500 and 200 ms exposure times, respectively.
Whole-mount immunohistochemistry. Imaging was performed on 10 hpf fixed embryos using a Zeiss AxioImager. M2 microscope controlled by the VisiView® software (version 4. 0. 0. 14, Visitron Systems, GmbH) and equipped with a dual filter (MAG Biosystems, Exton, PA), Photometrics cameras (Cascade II and CoolSNAP ES2) and VS-Laser Control. Frames were captured using a waterimmersion ×40 objective (Zeiss, NA 0. 8) with a step size of 2 μm. Actin signal intensity profile. Live imaging was performed on 7-9 hpf embryos using an LSM710 confocal microscope (×40 water-immersion objective, Zeiss, NA 0. 75, pinhole 154 μm, 512 × 512, 15 s per frame) controlled by the ZEN software (version 2010 B SPI, Zeiss, Germany). Dynamics and distribution of actin and blebs within the cell. Time-lapse imaging was performed on 7-9 hpf embryos using a Zeiss AxioImager.
M2 microscope controlled by the VisiView® software (version 4. 0. 0. 14, Visitron Systems, GmbH) and equipped with a dual view filter (MAG Biosystems, Exton, PA), Photometrics cameras (Cascade II and CoolSNAP ES2) and VS-Laser Control. Frames were captured at intervals of 500 ms for E-cadherin knockdown experiments and 1 s for β-catenin inhibition experiments using a ×63 water-immersion objective (Zeiss) with 488 and 561 nm lasers and 200 ms exposure time. Rac1-and RhoA-FRET. Live imaging was performed on 7-9 hpf embryos using an LSM710 confocal microscope (×40 water-immersion objective, Zeiss, NA 0. 75, pinhole 280 μm, 512 × 512, 7. 75 s per frame) controlled by the ZEN software (version 2010 B SP1, Zeiss, Germany). Subcellular localization of myosin light chain 12. 1-EGFP. Live imaging was performed on 10-14 hpf embryos using a Zeiss AxioImager.
M2 microscope controlled by the VisiView® software (version 4. 0. 0. 14, Visitron Systems, GmbH) and equipped with a dual view filter (MAG Biosystems, Exton, PA), Photometrics cameras (Cascade II and CoolSNAP ES2) and VS-Laser Control. Frames were captured at 2 s intervals using a ×40 water-immersion objective (Zeiss) with 488 and 561 nm lasers and 600 and 300 ms exposure times, respectively. Actin and blebs distribution in PGCs expressing Cxcl12a. Time-lapse imaging was performed on 7-9 hpf embryos using an Axio. Imager. Z1 (Zeiss) controlled by the VisiView® software (version 4. 2. 0. 2, Visitron Systems, GmbH) and equipped with Photometrics camera Prime 95B and VS-Laser Control. Frames were captured at 5 s intervals using a ×40 water-immersion objective (Zeiss, NA 0. 75) with 488 and 561 nm lasers and 300 and 400 ms exposure times, respectively.
MO-treated clones experiments. Live imaging was performed on 7-9 hpf embryos using a Zeiss Lightsheet Z. 1 microscope equipped with a ×20 water-immersion objective, 488 and 561 nm lasers, and controlled by the ZEN software (version 2014 SPI, Zeiss, Germany). Up to eight embryos were ramped in 1% low melting point agarose in translucent tubes (Fluidflon FEP-Schlauchabschnitt 1. 6 × 2. 4 mm (Wd: 0. 4 mm)) which were covered with methylcellulose. The chamber of the microscope was filled with 0. 3× Danieau's buffer and heated to 28 °C. Time-lapses were acquired with a time resolution of 1 frame per minute and a z-resolution of 2 μm. For the acquisition, single side illumination was employed and a zoom of 0. 8% was used. Migration track analysis. Images were acquired and Z-projected using the MetaMorph (Molecular Devices, LLC, version 7.