dataesr/scientific-paragraphs-categorization huggingface dataset:
Coregulatory network assembly in the context of lipid accumulation We reconstructed a coregulatory network from our GSE35447 transcriptomic data set, deposited in NCBI Gene Expression Omnibus database  35  (https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE35447). These data were generated with the Agilent platform (A-GEOD-15177-Agilent-031148 Yarrowia lipolytica V2) and correspond to 80 samples taken during a timecourse experiment in which Carbon/Nitrogen (C/N) ratio was increased to induce nitrogen-limiting conditions and lipid accumulation. Lipid yield and content are dependent on the nature of nutrient limitation. N limitation is the most widely used to induce lipid production, as it gives the best conversion yield with glucose. 36 Samples were obtained from a D-stat culture, where the dilution rate was kept constant while one of the cultivation parameter (temperature, C/N ratio) was modulated at a constant rate), 37 at 26 different time points, for three biological replicates.
We improved the reliability of the inferred network by running CoRegNet with a minCoRegSupport parameter of 0. 2 and a curated list of 151 TFs identified by our team from previous studies, homology and sequence analyses. PPI for Y. lipolytica were downloaded from the STRING database,  38  which provide interactions based on either experimentation, homology with better known organism such as S. cerevisae, or prediction. These evidences were therefore incorporated into the network (Pvalue = 3. 12e-42). The resulting network (YL-GRN-1) contains 111 transcription factors, 4451 target genes and 17,048 regulatory interactions. Further information about network inference is available in Materials and Methods. The association between gene name and official common name is provided in Supplementary Table  1.
The TF influence heatmap generated in this way provides a visual representation of transcriptional programs. Patterns were identified in the transcriptional program, defining several phases during the GSE35447 D-stat experiment (Fig. 1 ). Neither carbon nor nitrogen was limiting in the reference state (C/ N ratio = 7. 89), but four other phases could be defined, as follows: (a) Phase I (t ± = 123. 67 h, C/N ratio = 8. 63) corresponds to the early response to decreasing nitrogen levels. This pattern was first observed at about t = 120 h, when nitrogen became limiting. 37 his phase persisted until the C/N ratio reached 11. 70. Below this value, nitrogen limitation triggered new regulators, leading to lipid accumulation in the second phase. (b) Phase II (t = 139. 58 h, C/N ratio = 11. 70) appeared to be associated with early adaptation to nitrogen limitation: at this stage, yeast metabolism adapts to the nitrogen limitation of the environment, so as to maintain maximal growth while performing the normal functions, despite resource limitation.
See Supplementary Table  3 ). From this list, we identified master regulators on the basis of YL-GRN-1 (Table  1 ). The projection of both the top 10 most influential TFs and master regulators over the YL-CoRegNet-1 cooperativity network (Fig. 2 ) highlighted the high degree of connectivity of these TFs within a portion of the network and suggested that they acted in synergy during lipid accumulation. Validation of TF activity as a tool for identifying physiological phases A second network, YL-GRN-2, corresponding to the transition from biomass production to lipid accumulation, was reconstructed from our previous transcriptomic studies (GSE29046)  41  consisting of 11 sampling points, regularly spaced over the period of fed-batch culture, to validate the potential of TF influence for identifying relevant time points corresponding to important physiological changes in the absence of prior knowledge.
The data set was studied with the following CoRegNet parameters: minCor-egSupport = 0. 25, minGeneSupport = 0. 2. The influence heatmap for YL-GRN-2 presented three clear phases corresponding to the stages in the transition from biomass production to lipid accumulation identified and relating to (A) biomass production, (B) early lipid accumulation and (C) late lipid accumulation, respectively (see Supplementary Fig. 2 ). Use of a cooperativity network to identify evidence-supported coregulatory relationships and to identify new candidate coregulators A co-regulation network (YL-CoRegNet-1) was reconstructed from YL-GRN-1, as shown in Fig. 2. In this network, each node represents a TF, and the gray edges correspond to co-regulation by two regulators with a sufficient number of target genes in common.
In particular, the red edges represent co-regulation for which evidence of protein-protein interactions has been obtained. Evidence-supported co-regulatory relationships are well represented in the network and are highly interconnected. A Negative and positive influences are indicated from blue to red, with color intensity proportional to the influence value. Time and C/N ratio are indicated on the right, as described by Ochoa-Estopier and Guillouet  37  and in the GSE35447 data set review of the similarity-based annotations associated with the recovered TFs available from GRYC (http://gryc. inra. fr), genolevures, NCBI and from previous studies  41, 42  highlighted the presence of TFs known or assumed to be involved in lipid metabolism (e. g. , GZF1, GZF2 or GZF3), carbon or nitrogen metabolism (e.
Several modules were manually identified by projecting TF influence from the different phases onto the cooperativity network thanks to the interactive visualization interface from CoRegNet. Those sets of TFs activated in each phase were highly interconnected with one another into region of high density in the network (Supplementary Fig. 1 ). The largest module corresponds to the TFs associated with phase II, as shown in Fig. 2. Two other modules can be identified, corresponding to the TFs activated during phases I and III, and those activated during phases I and IV.
The influence of the TFs was estimated in the different samples and a matrix of influence over time and increasing C/N ratio was generated. Changes in influence over the course of the experiment were consistent with the observed physiological changes and stages of lipid accumulation. Indeed, the sensitivity of Y. lipolytica to nitrogen limitation led to changes in TF influence patterns at each key time point. The influence matrix is therefore a powerful tool for highlighting physiological changes in the absence of prior knowledge. From this matrix and the YL-CoRegNet-1 cooperativity network, we were able to identify different modules providing potential drivers of the lipid accumulation phases and possible coregulators of interest. Finally, TFs were ranked and the TFs with the highest ranks during phases I and II were overexpressed in a wild-type strain, with the Gateway overexpression system.
Experimental setting and transcriptomic data collection Chemostat and D-Stat experiments were performed in a 3 L stirred tank bioreactor with a working volume of 1. 5 L, with a Braun Biotech International Biostat B (Sartorius AG, Germany) and MFCS/win 2. 0 acquisition software. The temperature was regulated at 28°C and the pH at 5. 6 by the online addition of 5 M NaOH. Continuous culture was initiated 11 h after inoculation, when the glucose consumption was complete. For chemostat culture, the bioreactor was fed continuously with mineral medium (devoid of (NH 4 ) 2 SO 4 ) supplemented with 23 g L -1 glucose at 0. 108 L h -1. The bioreactor was fed with a second reservoir containing 60 g L -1 (NH 4 ) 2 SO 4 at 0. 0117 L h -1 , corresponding to a C/N ratio of 7. 75 molC. Nmol -1. The working dilution rate was 0.
Scanning was performed with an Agilent scanner and images were further processed with Feature Extraction v10. 0 (Agilent, USA). Data were processed and normalized with the Limma Bioconductor package. 48 Local background estimates were corrected by the "normexp + offset" method, using an offset value of 10. A scale normalization method was applied to normalize background between arrays. ID REF = VALUE = log2 (fluorescence), based on background-subtracted, normalized data. The processed data are publicly available from the NCBI GEO data repository under the name GSE35447. The resulting data set corresponds to 80 samples for 6539 genes, with three technical replicates of 26 time-points with an increasing C/N ratio, plus four replicates of the reference point. Constructing TF-target Gene regulatory network (YL-GRN-1) and TF-TF cooperativity network (YL-CoRegNet-1) Complex phenotypes are believed to arise from cooperative transcriptional programs rather than from regulation by a single regulator.
CoRegNet was developed to study such programs and to reconstruct large-scale contextspecific co-regulatory network from transcriptomic data. It was shown to outperform other network inference algorithms, particularly for small sample numbers,  31  an advantage when studying a non-conventional yeast, such as Y. lipolytica, for which few transcriptomic datasets are available. CoRegNet uses an algorithm, h-LICORN (hybrid-Learning Cooperative Regulation Network), to infer a list of GRNs from a discretized transcriptomic data set and a list of known regulators on the basis of a frequent itemset mining approach. 30, 31   riefly, in a first step, it efficiently searches the discretized gene expression matrix for sets of co-activators and co-repressors by frequent items search techniques and locally select combinations of co-repressors and co-activators as candidate subnetworks.
In a second step, it determines for each gene the best sets among those candidates by running a regression. h-LICORN was shown to be suitable for cooperative regulation detection [5,6]. The continuous data can be used alone to refine the original network by selecting for each gene the GRN with the best R 2 score based on the linear model used to estimate the expression. However, CoRegNet can also refine GRNs by incorporating evidence into the network using an integrative selection algorithm proposed by the modENCODE consortium  49  and applies it to the selection of local GRN models. In essence, the goal is to score each GRN (each interaction in the original method) using both the transcriptomic data and the integrated evidences to select the set of best GRN. Each GRN is scored by the inference method h-LICORN and by each of the integrated data set.
Finally, GRN are given the proportion of validated interactions as a score. Following this, to each GRN is associated as many scores as they are integrated regulatory and cooperative datasets in addition to the network inference R 2 score, all which range from 0 to 1. The original study proposes two approaches to merge the scores, an unsupervised and a supervised approach. While both are implemented in the CoRegNet package, the unsupervised approach was shown by the authors to have better performances. It is simply an unweighted average of each of the scores. Finally, for each gene, the GRN with the maximum merged score is selected. The refined network obtained is then transformed into a cooperativity network, based on the common targets of regulators. We identified regulators and regulatory states associated with lipid accumulation in Y.
lipolytica, by applying CoRegNet to the preprocessed GSE35447, as described above. CoRegNet was run with a default minCoregSupport = 0. 1, with a curated list of 151 TFs retrieved from previous publications and from homology analysis. Y. lipolytica interactome data relying on either experimentation, in-silico prediction, or most commonly on homology analysis were downloaded from the STRING database,  38  and used as evidence for network refinement. CoRegNet is freely available as a Bioconductor package.
Context-specific transcriptional program visualization Both the network and its influence heatmap can be visualized through a dedicated tool implemented in CoRegNet, using Shiny application, with features for displaying the main sets of co-regulators in specific samples, stages or subtypes. The network is represented as a graph, in which each node is a regulator, each gray edge is a co-regulatory relationship and each colored edge is a co-regulatory relationship for which evidence is provided. The size and color of the nodes are proportional to the differential expression and value of TF influence, respectively.
ACKNOWLEDGEMENTS This work was supported by a public grant from the  French National Research Agency (ANR)  as part of the " Investissement d'Avenir" program , through the " IDI 2016 " project funded by the  IDEX-Saclay ,  ANR-11-IDEX-0003-02  for P. T. This work was supported by the  CHIST-ERA  grant ( AdaLab ,  ANR 14-CHR2-0001-01 ) for P. T. , M. E. This work was performed, in partnership with the SAS PIVERT, within the frame of the  French Institute for Energy Transition (Institut pour la Transition Energétique (ITE) P. I. V. E. R. T. (www. institut-pivert. com)selected as an  Investment for the Future ("Investissements d'Avenir "). This work was supported, as part of the Investments for the Future, by the  French Government  under the reference  ANR-001-01. We thank  Nicolas Morin  for processing the transcriptomic data set GSE35447 and helpful discussions.
Data availability All data and tools mentionned in this article are freely accessible, in particular, transcriptomic data that support the findings of this study have been deposited in NCBI Gene Expression Omnibus database with the accession code GSE35447 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE35447). CoRegNet is freely available as a Bioconductor package.
Gene expression is estimated using image intensities of a fluorescent reporter. Since reporter levels vary too much for reliable segmentation, morphological cell data are acquired by imaging a second, constitutively expressed, fluorescent reporter. Software controllers, associated with individual cells or cell groups, process these data and return expression activation/repression signals for delivery to each cell. Cells are individually stimulated by projecting an RGB image of the signal intensities, mapped to appropriate color channel and cell locations, onto the light-responsive cells using a modified overhead projector (Methods, Supplementary Figs. 1  and 2 , Supplementary Movie 1). Maximum cell numbers are thus constrained by the time required to visit and set up each field of view, perform data acquisition, analysis and control computations, and stimulate every cell at a sufficient frequency.
In our experiments, six minute control intervals permit initial tracking and control of 200-400 cells in four to eight fields of view at ×100 magnification, with maximum cell attrition-limited durations of several days (Methods). In closed-loop mode, once defined, experiments are implemented entirely programmatically and without intervention. To enable fully autonomous operation, we wrote custom MATLAB software to manage all aspects of the experiment (Methods). The modular software eases modifications of the physical system, and simplifies revision of controllers and experiment protocols. To independently regulate expression in single cells, we rely on light-responsive transcription mechanisms. We employ a recently optimized ccaSR-based system with low leak and large dynamic range, which includes a synthesis pathway for the necessary phycocyanobilin (PCB) chromophore  31.
Methods Bacterial strains and plasmids. See Table  2. Imaging and projection system. Our system integrates image data acquisition and processing, cell stimulation, and environmental regulation. All physical device control and data processing is executed through custom software in MATLAB (version 2014b, with statistics, image processing, distributed computing toolboxes) using MicroManager  48  , SUNDIALS  49  , mexOpenCV  50  , and java packages (Supplementary Software). Cell growth and expression data are derived from images collected with a motorized inverted microscope (Body: Olympus IX83, Stage:Märzhäuser, Objective:Olympus UPLSAPO100XOPH, Camera: Hamamatsu Orca Flash4. 0v2, LED-based fluorescence illuminator: Lumencor SpectraX) in the CFP (x438/29, m483/22) and YFP (x513/22,m543/22) fluorescence channels.
This per-cell data are passed to experiment-dependent software controllers that update cell state estimates and determine the subsequent activation (~535 nm) or deactivation (~670 nm) light stimuli to be delivered to each cell (Supplementary Methods). The list of per-cell stimuli is converted to red and green boxes in an RGB image, overlying the positions of their corresponding cells (typically covering the terminal ~11 μm of channel, e. g. , position of dashed yellow box in Supplementary Fig. 2 ). The image is then spatially transformed to register projector to camera image planes (openCV function cv. warpPerspective, using homographies determined at the experiment outset with projected chessboards and the functions cv. findChessboardCorners and cv. findHomography). Shading corrections (low-pass frequency filtered images of reflected flat field projections, generated at experiment setup) are applied to each color channel.
4% glucose, 0. 01% Tween20) is flowed through the device at 4 ml h -1 This work for 1 h, and 1. 5-2. 0 ml h -1 thereafter. The experiment control is engaged (Supplementary Fig. 9 ). Experiment calibration, providing per-channel camera and projector offsets from the PDMS-glass interface focal plane, projector-camera image transforms, and projector shading correction are performed. For each control location on the chip, measurement areas for individual cells are specified (typically, by a 2. 6 × 5. 2 μm box at the end of a growth channel), and a software controller/target program is associated with each. Once all control locations have been populated and the system begins to acquire data and stimulate the cells, it runs automatically until the conclusion of the experiment. For experiments involving media switching, a stopcock and syringe pump flows are adjusted at the appropriate time (0.
Acknowledgments We are grateful to  M. Lang ,  H. Janovjak ,  M. Khammash ,  A. Milias-Argeitis ,  M. Rullan ,  G. Batt ,  A. Bosma-Moody ,  Aryan ,  S. Leibler , and members of the Guet and Tkačik groups for helpful discussion, comments, and suggestions. We thank  A. Moglich ,  T. Mathes ,  J. Tabor , and  S. Schmidl  for kind gifts of strains, and  R. Hauschild ,  B. Knep ,  M. Lang ,  T. Asenov ,  E. Papusheva ,  T. Menner ,  T. Adletzberger , and  J. Merrin  for technical assistance. The research leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement no. [291734]. (to R. C. and J. R. ),  Austrian Science Fund  grant  FWF P28844  (to G. T. ), and internal  IST Austria Interdisciplinary Project Support.
The ExaNoDe project addresses this issue thanks to the package level integration and separation of the building blocks of a compute element (compute, memory, I/O, acceleration) based on nanotechnology, the Chiplet approach (Subsection III-A). This new and promising design concept allows multiple combinations of a compute element to be built with a significantly lower NRE cost, tailored to specific application scenarios. In addition, the Chiplet approach will also help to reach higher compute density, thanks to the integration of multiple compute elements and memories on the same chip. An example of the benefits of this approach is related to compute accelerators (e. g. CPUs, Many-core CPUs, FPGAs), widely used in HPC to boost the performance of some classes of applications. However, such accelerators are usually integrated in a compute node via PCI-e links, thus limited by the memory bandwidth of such interface.
III. THE EXANODE APPROACH The main goal of the ExaNoDe project is to deliver a prototype-level compute element integrating some core technologies that are in line with an HPC system targeting Exascale. The aim is not to create a production level compute element, but instead to pave the way with technology towards Exascale by providing system integrators and software teams a tool for further research, oriented to industrial applicability. However it is not enough to build the core technology of the compute element to actually reach Exascale performance at the applications level. Programming such future massively parallel and heterogeneous systems is not going to be trivial, and programming models and runtime systems will have to be adapted/re-designed accordingly. Another important issue to be considered is resilience, since increasing the number of compute units and their complexity will inevitably lead to an increase of the failure rate.
The ExaNoDe memory model instead will enable a real global address space (GAS), where data are actually shared between multiple partitions without the need to perform adhoc memory transfers. This removes the dependency from any software API to build the shared memory abstraction. Such a global shared address space leverages the capabilities of the modern ARMv8-A processor to create a global shared memory up to 48 bits in size. In addition, each SMP node within the larger GAS can have its own private 48-bit local memory partition, in which it can simply map pages from the shared GAS into its local partition and make this visible to applications through their specific virtual address space. Fig. 4  shows an example of the ExaNoDe memory scheme, with two compute nodes each of them with their own local memory partition divided between DRAM, peripherals and an unused part.
In addition, it provides a coherent partitioned global address space model, without requiring a global cache coherence mechanism, by specifying that each page in the GAS is only cacheable in a single coherence island (which may or may not be the same as the island on whose DRAM the page is currently mapped). Since there is no need for a global cache coherence hardware mechanism, this model is scalable in the same way that the message passing model is. A UNIMEM enabled platform needs support both by hardware and software. The routing in the UNIMEM global address space will be implemented in the FPGAs and the Chiplets (see previous section), while the software support will be integrated in the firmware and operating system of the node.
C. Software infrastructure As already anticipated, besides the hardware prototype design and implementation, the ExaNoDe project partners will devote significant effort on software research and development as medium used for testing the compute element with real-world applications, to extend programming models and runtimes towards Exascale and to support the unique characteristics of the ExaNoDe platform, including UNIMEM. Fig. 5  shows the four layers of the software architecture developed to run on each compute element, spanning the bare metal/OS level up-to the programming and application level. Firmware and Operating system: ExaNoDe firmware will enable UNIMEM data movement mechanisms and integration with peripheral devices, leveraging the memory hierarchy to provide to the OS an interface for light RDMA operations.
Within the node, the firmware will accelerate interactions between different modules, bypassing heavy legacy protocols and implementing more direct interfaces to NVM and accelerators. The firmware will also provide support for outof-node communication by implementing shared (virtualized) DMA engine(s) and Ethernet NIC(s), as well as switching and routing for the next-level interconnect. The ExaNoDe operating system will be based on NUMA-aware Linux that supports dynamic distributed memory management and user-level socket-based communication across servers using RDMA to offer existing TCP/IP communicating applications the benefits of UNIMEM system architecture.