dataesr/scientific-paragraphs-categorization huggingface dataset:
5 to 120 K) in the MS1 acquisition step. However, this did not significantly improve the output of the analysis workflow. The datasets obtained in our standard conditions were nonetheless considered to be of sufficient quality to validate the methods and workflows. These experimental uncertainties have important consequences when computing the incorporation of  12  C in SLIM-labeling experiments and the fraction of nonlabeled peptides in quantitative experiments. Indeed, we use the experimental M 0 and M 1 data with reference to the corresponding theoretical values computed both under the NC condition and under 100% enrichment in the 12C condition, together with the elemental composition of the peptides. Under ideal conditions, the P′( 12 C) value should be in the range of 0. 9893-1, and the nonlabeled peptide fraction α between 0 and 1.
However, when running our workflow to compute these factors, we found a large fraction of peptides (3-4 000) with α values (as an example) between 1. 005 and 1. 007 instead of 1, or between -0. 008 and -0. 009 instead of 0. We chose to correct the value of these outliers to 1 or 0, respectively. After these corrections, the average number of high-quality data points to process was 11-12 000 per dataset. Figure  4A , computed using eq 4, shows the 12 C incorporation levels in the peptides from the experimental standard curves, composed of mixtures of known amounts of  12  C-labeled-and nonlabeled peptides (100% NC, 75% NC-25% 12C, 50% NC-50% 12C, 25% NC-75% 12C, and 100% 12C) from the yeast strain S288c. Equation 4 states that the variation of  12  C levels follows an equilateral hyperbola, as observed in the experimental data.
The leu2 mutation affects not only the penultimate step of leucine biosynthesis (3-Isopropylmalate to (2S)-2-Isopropyl-3-oxosuccinate reaction) but also an early step of the valine and isoleucine biosynthesis pathways at the 2-oxobutanoate production reaction. Our data show that certain compensatory mechanisms were activated in BY4742, as the Ilv2,3,5 proteins were overexpressed, as well as Leu1,4,5,9 and Bat1,2, which are directly involved in terminal reactions of leucine and valine biosynthesis. We made a similar observation concerning the histidine regulon, in which His1, 4, 7, and 2 were more abundant in the BY4742 genetic context. However, the extent of the differences in protein synthesis was less than that observed for the leucine regulon. Analysis of the proteins of the lysine biosynthesis pathway showed a general increase in the abundance of proteins involved in lysine biosynthesis in the S288c context in our dataset, presumably caused by the absence of the Lys5 target protein Lys2.
■ CONCLUSIONS Here, we present an original and highly efficient workflow to process bSLIM-labeling data based on open-source resources. The overall data analysis process is rapid, i. e. , less than 20 min from raw data to protein quantification, and the input and output use standard file formats (mzML,. tsv, Excel files). The workflows are robust and well adapted to processing data in experiments in which proteins are fully labeled with  12  C, as well as in experiments in which proteins are only partially labeled, due to the requirement of essential amino acids not yet available as U-[ 12 C]-AA. The workflows are easy to edit for adaptation for specific experiments. It is possible to extend them with new functionalities and to add additional tools and features to the analysis. For example, we used X!Tandem as a database search engine for peptide identification in the present workflow, but it is possible to add other search engines and combine the results files in a single idXML file for feature extraction.
e. without an on-board high-sensitivity accelerometer) and to distinguish between gravitational and non-gravitational forces acting on the spacecraft. For more details see  Santoli et al. (2020). The primary objective of the MPO Magnetometer (MPO-MAG, Fig. 19 ) is to collect magnetic field measurements in order to describe Mercury's planetary magnetic field and its source in unprecedented detail. This will help us understand the origin, evolution and current state of the planetary interior. The requirement is to determine all the terms associated with the internal field up to the octopole with high accuracy, using accurate magnetic field measurements on the low orbit of MPO. This will be supported by similar measurements made on Mio, in order to distinguish the effects of the magnetospheric currents on the MPO measurements and to use the Mio measurements directly to augment the database for the determination of the internal terms.
The range rate is measured from the Doppler shift of an electromagnetic wave transmitted from ground, received by the spacecraft and coherently re-transmitted back to ground. The spacecraft range is obtained from the measurement of the time delay between transmission and reception of a known modulation of the carrier. MORE data will be combined with the measurements of the non-gravitational acceleration by the BepiColombo Accelerometer ISA to generate an orbital solution and the estimate of the physical parameters of interest. SIMBIO-SYS high-resolution images will be used for the optical tracking of surface landmarks in order to determine the rotational state of the planet. For more details see  Iess et al. (2021). PHEBUS (Probing of Hermean Exosphere by Ultraviolet Spectroscopy, Fig. 24 ) is an Ultra Violet (UV) spectrometer working in the wavelength range from 55 nm (Extreme Ultra Violet, EUV) to 330 nm (Far Ultra Violet, FUV) and devoted to the characterisation of Mercury's exosphere composition and dynamics.
ELENA has a 1-D field of view for mapping neutral particle released from the surface; the second dimension of the data will be provided by the movement of the spacecraft. ELENA's field of view (FOV) is 4. 5 • × 76 • , directed towards the surface of Mercury (nadir) with an angular resolution of 4. 5 • × 4. 5 •. ELENA measures energetic neutrals between 20 eV and 5 keV. 2) STROFIO (STart from a ROtating Field mass spectrOmeter) is a neutral particle spectrometer, which investigates the exosphere gas composition. It is a novel type of mass spectrometer. The neutral particles enter into the system and become ionized. The ions are released into the focusing optic and move on a constant trajectory to the 2D detector system where the time of flight is measured, from which the mass/charge can be calculated.
Acknowledgements The authors wish to thank the entire  Project Team of industry especially Airbus Defence and Space GmbH (Airbus-DE, TAS-I and Airbus-UK) , the  Project Team at JAXA , and the  Project Team at ESA , the  Operations Team at ESOC  and the  Science Operations Team at ESAC  for all their hard work to make this mission possible and for their contributions to the system definition and for providing the information for this article. The authors would also like to thank the two reviewers for their detailed, thorough review and for many helpful comments and suggestions to improve the quality of the paper. Special thanks to  Jan van Casteren ,  BepiColombo Project Manager , who passed away much to early.
Method This research is based on the modelization on several studies, which contributed to different neural and astrocyte modeling. Though we tried to implement the equations as close as possible to the original works, we adapted some parameters to study neuron-astrocyte network activity. The postsynaptic bouton's dynamics is based on  Tewari and Majumdar (2012) , the AMPAR trafficking on  Shouval, Castellani, Blais, Yeung, and Cooper (2002) , the dendritic compartment on  Kepecs and Raghavachari (2002) , the neuron on  Izhikevich (2003) , and the astrocyte on  Stimberg, Goodman et al. (2019). The implementation details and parameter justification are in the corresponding subsection. The latter study provided the simulation code for astrocyte's dynamics. We based our simulation code on theirs and consider that the overall model extends theirs.
Numerical Method. For the differential equations, we used the forward Euler method, with dt = 0. 1 ms. The program was written in Python, under the BRIAN2 simulator  (Stimberg, Brette, & Goodman, 2019) , based on the Stimberg, Goodman et al. (  2019 ) study. For each combination corresponding to the cases «with astrocyte», «without astrocyte» and «switch frequency» condition, we performed 30 simulations. The simulations were carried out on the cluster of the Computing Center of the University of Burgundy (Linux 64 bits, processors Intel Xeon E5-2640v3 (2P, 8C/P). The code can be found at http://modeldb. yale. edu/266794. responses  (Werner & Mountcastle, 1965; Tolhurst, Movshon, & Thompson, 1981) , Poisson statistics has often been used to describe the neural firing patterns, so we followed the classic rejection method to generate Poisson spike trains.
(i. e. , g max ). As the minimal NMDAR conductance is 0. 01 nS, it would be much lower than the lowest AMPAR conductance (0. 35 nS). The NMDAR contribution to the postsynaptic depolarization would be too low to induce any significant change. The maximal NMDAR conductance is 0. 6 nS, between the minimal and the maximal AMPAR conductance (0. 35 nS to 1 nS), making its contribution to the postsynaptic depolarization meaningful. The synaptic NMDAR conductance was g NMDAR = 2g max , without astrocyte g max , with astrocyte. (2. 49) In this study, we fixed the randomness of each code to perform paired simulation for the «without astrocyte» and «with astrocyte» conditions (i. e. , in each pair of simulation, connectivity, signal, and noise were the same for both conditions). We performed 30 independent simulations for each «switch frequency» level (i.
Parameter Analysis. In this study, we recorded several simulation variables to analyze glial cells' influence over the network activity. Then we used the following measures: • AMPAR density: Average expression of the AMPAR density for a neuron's synapses, averaged over the neuronal population layer. • Population firing rate: Average population instantaneous firing rate for one layer, computed with a time window of 25 ms. For both AMPAR density and population firing rate, we computed the crosscorrelation function between these data and the input stimulation signal (see Figures  3  and 4 ). To analyze the effects of conditions («without astrocyte» and «with astrocyte») on these data, we fitted a linear mixed model (LMM) on the maximal points of each correlation. For firing rate and AMPAR density correlation with the signal pattern, we kept the best correlation coefficient and the lag at which it is observed.
Implementation of AI in HPC The advancement of ML and Deep Learning (DL) have brought higher performance and more accurate solutions to applications in different domains, e. g. computer vision and natural language processing. ML and DL algorithms are often data intensive and computation demanding. Industry and academia seek the solutions in Cloud computing, as it allows large datasets to be easily ingested and managed to train algorithms, and enables ML and DL models to scale efficiently at low cost. Compared with Cloud, HPC systems show the advantages in computational power, storage and security  [10]. Exploiting HPC infrastructures for ML and DL training is becoming a topic of increasing importance  [11]. AI applications are usually developed with high level scripting languages or frameworks, e.
Containers can run inside VMs as this is the case in most of the Cloud clusters  [23]. There are multiple technologies that realise the concept of containers, e. g. Docker  [24] , Singularity  [25] , Shifter Fig. 3  Structure VMs and containers. On the VM side, the virtualisation layer often appears to be Hypervisor while on the container side it is the container runtime  [26] , Charlie Cloud  [27] , Linux LXC  [28]  and Rkt Core OS  [18]. Docker may be the most popular one on Cloud. Singularity developed for HPC systems shows the following merits: • Run with user privileges and need no daemon process. Acquisition of root permission is only necessary when building images, which can be performed on user working computers; • Seamless integration with HPC. Singularity natively supports GPU, Message Passing Interface (MPI)  [29]  and InfiniBand.
Its architecture is composed of a master node and many worker nodes. Kubernetes runs its containers inside pods that are scheduled to the worker nodes. A pod can encapsulate one or multiple containers. Kubernetes provides its services by deployment that are created by submission of yaml files. In the yaml file, users specify the services and computation. Kubernetes is based on a highly modular architecture which abstracts the underlying infrastructure and allows internal customisation, such as deployment of different software defined network or storage solutions. It supports various Big-Data frameworks (e. g. Hadoop MapReduce  [34] , Apache Spark  [35]  and Kafka  [36] ) and can be connected with Ansible  [37]  which is a widely-used software orchestration tool in Cloud clusters. Kubernetes includes a powerful set of tools to control the life cycle of applications, e.
Khan et al. [1]  proposed to containerise HPC workloads and install Mesos  [47]  and Marathon on HPC clusters for resource management and container orchestration. Its orchestration system can obtain the appropriate resources satisfying the needs of requested services within defined Quality-of-Service (QoS) parameters, which is considered to be self-organised and self-managed meaning that users do not need to specifically request resource reservation. Nevertheless, this study has not shown insight into novel strategies of container orchestration for HPC systems. Julian et al. [40]  proposed their prototype for container orchestration in an HPC environment. A PBS-based HPC cluster that can automatically scale up and down as load demands by launching Docker containers using Moab scheduler. Three containers serve as front-end system, scheduler (it runs workload manager inside) and compute node (launches pbs_mom daemon).
More compute node containers are scheduled when there are no sufficient number of physical nodes. Unused containers are destroyed via external Python scripts when jobs complete. Similarly, an early study  [41]  described two models that can orchestrate Docker containers using an HPC resource manager. The former model launches a container to mimic one compute node which holds all the processes assigned to, whilst the latter boots a container per process. However this work seems to be less appealing to HPC systems. MPI applications dominate HPC programs, which can be automatically scaled with Singularity runtime support. Wrede et al. [48]  performed their experiments on HPC clusters using Docker Swarm as the container orchestrator for automatic node scaling and using C++ algorithmic skeleton library Muesli  [49]  for load balance.
The login node only submits the TORQUE job to the HPC cluster and is not included in the TORQUE compute node list. Job submission on the HPC cluster side will not be scheduled to this login node. The Kubernetes cluster incorporates a master node that schedules the jobs to the worker nodes. The work nodes deliver the services or perform computation. This architecture brings the following merits: • Provide a unified interface for users to access the Cloud and the HPC clusters. Jobs are submitted in the form of yaml ; • Except Singularity, no additional software packages are needed to be installed in the HPC system. Hence it gives little impact on the working environment of existing HPC users; • Users have flexibility to run containerised and non-containerised applications (on the HPC side); • The performance of compute-intensive or data-intensive applications can be significantly improved via execution on the HPC cluster; • Containers scheduled by Kubernetes to HPC clusters can take advantage of the container scheduling strategies of Kubernetes, where TORQUE lacks its efficiency.
Architecture of torque-operator We developed a tool named Torque-Operator that bridges Kubernetes and TORQUE. Torque-Operator is an extension of WLM-Operator  [50]. It connects with Kubernetes by creating a deployment called torque-operator on the Kubernetes cluster as shown in Fig. 5. The tool is written in the Golang programming language. A virtual node (named torque-sycri-k8s-2-batch in Fig. 6 ) is created, which corresponds to one TORQUE queue. Figure  6  shows the virtual node information. Virtual node is a concept in Kubernetes. It is not a real worker node, however, it contains the information of its corresponding HPC queue (e. g. the number of nodes per queue) and can schedule the jobs to real worker nodes. The virtual node connects Kubernetes to other APIs and allows developers to deploy pods and containers with their own APIs.
7  Line 20. Besides the four containers, Torque-Operator also includes a Linux service program named red-box running on the login node as shown already in Fig. 4. Redbox builds a Unix socket which allows data exchange among the Kubernetes and TORQUE processes. Torque-Operator introduces a new Kubernetes object kind i. e. Torquejob. As users can use kubectl get pods to display the pod information, they can perform kubectl get to show the status of jobs submitted to TORQUE. Users can also view the status of TORQUE jobs using the PBS commands qstat on the login node (marked in red dashed line in Fig. 4 ). Fig. 7  An example of the Kubernetes yaml script. The script encloses a PBS script. Submission is performed as a normal yaml job submission, i. e. $kubectl apply -f $HOME/cow_job. yaml The PBS job script is encapsulated into a Kubernetes yaml job script.
Use cases The proposed architecture and tool herein are implemented in the testbed of the EU research project CYBELE 2. We present two use cases from the CYBELE project for functionality validation and performance evaluation. The two use cases herein are named Pilot Wheat Ear and Pilot Soybean Farming, respectively. In addition, we give the performance evaluation on an open-source MPI benchmark: BPMF 3 ("Pilot and benchmark description" section). All the three applications are containerised in Singularity. Table  2  lists the key software packages and their dependencies encapsulated in Singlarity images for the three applications. It is straightforward to build a Singularity image which is as easy as installing software packages in a Linux system. We illustrate a snapshot of the Singularity build script for Pilot Soybean Farming in Fig.
Pilot and benchmark description Pilot Wheat Ear is targeted for provisioning of autonomous robotic systems within arable frameworks. More specifically, the application provides a framework Fig. 9  A snapshopt of the Singularity build script for Pilot Soybean Farming for automatic identification and count of wheat ears in fields from the data collected by sensors on ground so that it enables crop yield prediction at early stages and can suggest decisions for sales planning. The application trains a DL algorithm written in Python on Fastai/Pytorch  [52] [13] framework based on a series of RGB images (138 images, 95 MB in total). An example of the images is illustrated in Fig. 10a. Pilot Soybean Farming focuses on the application of ML in soybean farming. Its goal is to develop a prediction algorithm that is able to infer hidden dependencies between the input parameters and the yield.
The ML algorithm, more specifically the Random Forest algorithm  [53] , is implemented in Python's Scikit learn library  [54]. The training dataset consists of two Sentinel-2  [55]  images (the total size of images: 4. 9 GB) with 13 spectral bands, at a resolution of 10m and yield monitor data (total size: 569 MB) from Austrian soybean farms. The pipeline includes modules for image stacking, cropping to the field Fig. 10  Examples of Pilot images. The Sentinel-2 image deliver information of three key stages of soya growth boundaries, yield map interpolation from point measurements and a Random Forest module for yield prediction. An example of the Sentinel-2 image (only a single band of the image is displayed) is shown in Fig. 10b. The aforementioned pilots are in developing stage and are not yet publicly accessible.
Performance evaluation Performance evaluation is carried out on the testbed with the technical specifications indicated in Table  4. Singularity is the container runtime of our choice, as it provides a secure means to capture and distribute software and their environments. Kubernetes supports Docker by default, though it can be adjusted to support services for Singularity by adding Singularity-CRI 4  [60]. Table  5  concludes the list of core applications to be installed on the testbed. In general, Kubernetes cluster can support both the Singularity runtime and the Docker runtime. Ideally the worker nodes which run Docker containers should not be linked to HPC clusters. Romana is deployed as the network model for the container network interface (CNI) of Kubernetes. Without this additional network model, the pods located on different hosts could not establish communication with each other.
This will hugely depend on the development of the AI use cases of the CYBELE project. Currently, container orchestration policies rely on Kubernetes. The next steps can be carried out on improvement of the container scheduling of TORQUE. In addition, the login node can accept the regular Kubernetes yaml file instead of embedding a TORQUE PBS script within it, and the orchestrator is able to analyse the job size so that it can determine whether to submit jobs to HPC clusters or schedule them to the Cloud clusters. In the latter case, the embedded PBS yaml will be automatically generated for HPC job submission.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement NO. 825355. Yield monitor data in Pilot Soybean Farming have been generously granted by  Donau Soya  association from farms across the Lower Austria region, through  CYBELE  project and are subject to NDA. The authors would like to express the gratitude to  Dr. Joseph Schuchart  for proof-reading the contents.
, 2017). The new version presented in this article is called v2020r1 (for the year of distribution of this first release). Numerous new developments are included in this new version, and most of what is described here has already been published in articles by the CHIMERE developers. As for all previous versions, the code is open source and freely available on a website. All databases used to run Published by Copernicus Publications on behalf of the European Geosciences Union. simulations were also selected for their open data status. In addition to the CHIMERE code itself, this new version is also distributed with the OASIS3 -Model Coupling Toolkit (MCT) and Weather Research and Forecasting (WRF) models. We can group and summarize these developments as follows: 1. The online coupling with WRF via OASIS3-MCT to calculate the possible retroaction of aerosols on radiation (direct effect) and cloud formation (indirect effect)  (Briant et al.
To avoid this problem, output files are written locally, then moved to remote disks at the end of the simulation only. imegan: it enables one to select the land-use database used by MEGAN to calculate the biogenic emission fluxes. Two datasets are available in the CHIMERE distribution: imegan = 1 for the dataset with a resolution of 2. 5 min, with monthly leaf area index (LAI). imegan = 2 with a resolution of 30 s and with 8 d LAI  (Sindelarova et al. , 2014). iusedms: this flag is for whether to take into account DMS emissions or not. Option 0 is for "no emissions", and options 1, 2, 3, or 4 are to select a parameterization such as iusedms = 0 (no DMS emissions); iusedms = 1  (Liss and Merlivat, 1986) ;  Wanninkhof, 1992) ;  Nightingale et al. , 2000) ;  Elliott, 2009). -iusedms = 2 ( -iusedms = 3 ( -iusedms = 4 ( iusesalt: like for the DMS emissions flag, this flag selects if emissions have to be taken into account (0 for "no emission") and what parameterization to use: iusesalt = 0 (no sea-salt emissions); iusesalt = 1  (Monahan, 1986) ;  (Monahan, 1986 ) +  (Martensson et al.