dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioinformatics analyses Source code availability, access to intermediate result files and reproduction of all analyses. All the source code written for this project is available in the Github repository https://github. com/thomasdenecker/iHKG/. The analysis starts with reading the MAnGO data file deposited in GEO (see previous section). This ensures continuity between the work on raw data (standard results of microarrays processed by a technical platform) and the work of data mining and analyses performed for this article (our contribution). To reproduce our data output tables, our selections of genes and our output graphs, a docker image is provided, with RStudio software (https://rstudio. com/) and all the necessary R packages installed: https://cloud. docker. com/ repository/docker/tdenecker/ihkg/general.
The website to explore the functional networks of co-expressed genes is available at https://thomasdenecker. github. io/iHKG/. Statistics for differential expression. Our transcriptomic results are from two-color microarray experiments with a common reference, i. e. a mixture of RNA extracted from all the other growth conditions (see previous section). To compare gene expression levels between 'BPS' and 'Control' on the one hand, and between 'FeSO4' and 'Control' on the other hand, a design matrix was defined as explained in the LIMMA user guide available online https://bioconductor. org/packages/release/bioc/ vignettes/limma/inst/doc/usersguide. pdf. This matrix allows identification in the entire dataset of the different conditions to be compared and the associated biological replicates.
Data mining of GO terms, definition of Meta-GO and allocation of iron responsive genes to general functions. Gene Ontology (GO) terms were used to define the Meta-GO (Figure  3 ). They were extracted from the 'Biological Process' section of the GO database  (35). The li-Figure  3. Data mining of GO terms to define a limited number of general functions to which all iron responsive genes can be assigned. (A) Schematic representation of the process applied to manage GO terms in this study. Starting from a list of GO terms (all terms in 'Biological Process' here), they are located in the DAG provided by the GO database. Levels are associated to each GO term according to their position in the DAG (see 'Materials and Methods' section and Supplementary Figures S10). This is illustrated here with colored boxes.
The network of co-expressed genes were represented using a combination of the library igraph (https://cran. r-project. org/package= igraph) for the calculation of the positions of nodes (R function 'layout nicely') and the software Cytoscape (36) for dynamic network exploration in https://thomasdenecker. github. io/iHKG/. Source of gene descriptions and annotations, sequence data and orthologous relationships between genes. Gene description and annotations were retrieved from the Candida Genome Database (CGD)  (37)  and the GRYC database (http://gryc. inra. fr/). Functional enrichment of GO terms was performed with the version of GOterm Finder  (38).
Thirty degrees is the usual temperature at which C. glabrata is cultivated in laboratories, while 37 • is the temperature at which C. glabrata develops in the human body. Since iron homeostasis is an important physiological process involved in host-pathogen interactions, it was important to assess the potential effect of the temperature. Transcriptomics experiments were performed with microarray technology, to quantify gene expression changes with respect to appropriate controls (see 'Materials and Methods' section). As a result, experimental data were obtained in the five conditions referred to hereafter as C1, C2, C3, C4 and C30-37, for all C. glabrata genes that could be monitored with microarrays.
These three steps are detailed below. Step 1: Computational strategy to place all the iron responsive genes in a small number of functional categories. Unlike the classical approach which consists in identifying functional categories (generally GO terms) significantly enriched in our list of genes  (38) , we wanted here to define a small number of cellular functions (fewer than 10) and assign all of the 637 iron responsive genes to only one of the pre-defined functions. This limited number of functions was required to allow subsequent gene network inference and visualization. To define our functions, we first classified all GO terms (more than 40 000) available in the 'Biological Process' section of the GO database into Meta-GO groups (see 'Materials and Methods' section). As illustrated Figure  3A , the 'Meta-GO' are GO terms, which share a specific level in the GO directed acyclic graph (DAG), the hierarchical organization of terms defined by the Gene Ontology Consortium  (35).
Step 3: Computational strategy to interactively explore the functional networks of co-expressed genes. The main interest of Figure  5  is to give a global overview of the gene expression processes used by the pathogenic yeast C. glabrata to face low and high iron conditions. We integrated here more than 100 000 qualitative and quantitative heterogeneous kinds of information (GO terms, microarray data, gene annotations in public databases, etc. ) to obtain a unified picture of multiple cellular processes. We added to Figure  5  the names of around fifty genes, which we found to agree with current knowledge of iron homeostasis in C. glabrata (see the next section). It is clear, however, that further exploration of these networks could be considered, examining for instance the disconnected (isolated) genes, or searching for the location of genes with new putative functions related to iron homeostasis.
To ensure the diffusion of our data, as well as their subsequent exploitation by ourselves and by others, we developed an interactive web viewer (called 'iHKG viewer'). This website is publicly accessible (see 'Materials and Methods' section). Several features were implemented to (i) enable dynamic exploration of each functional network of co-expressed genes, (ii) enable rapid location of any genes in the network and (iii) obtain for any gene, all the available annotations available in the public databases GRYC and CGD (Figure  6 ).
g. ATG19, ATG32, ATG41 (functional network 'Metabolism'). Genes involved in Fe-S clusters or heme biosynthesis and assembly were spread into the functional networks 'ISC synthesis and assembly' (e. g. ISA1, CFD1), 'Regulation' (e. g. GRX4), 'Metabolism' (e. g. HEM4, HEM15), 'Redox Signaling' (e. g. HEM13, COX15). This splitting between different networks arises from our decision to assign genes to only one functional category. Genes encoding proteins with functions directly or indirectly related to redox mechanisms were thus separated from the metabolic pathway in which they participate (as illustrated here with the heme biosynthetic pathway). The reader who is disturbed by this separation of functions can refer to the global network accessible here https://thomasdenecker. github. io/iHKG/all.
Genes involved in the copper transfer toward the protein Fet, e. g. the gene CAGL0J07980g (ortholog of S. cerevisiae gene ATX1), CAGL0M08602g (ortholog of the S. cerevisiae gene CCC2), CAGL0D04708g (ortholog of the S. cerevisiae gene CTR1) and CAGL0I02508g (ortholog of the S. cerevisiae gene CTR2) were also identified in the functional network 'Transport/trafficking', as well as genes coding for actors in the vacuolar or mitochondrial iron export system, e. g. CAGL0H08822g (ortholog of the S. cerevisiae gene MMT1), FTH1 (CAGL0M05511g), SMF3 (CAGL0A03476g). Gene annotations in public databases. To further explore our list of iron responsive genes, independently of the literature, we used gene annotations available in public databases. We could thus identify a set of genes which (i) were classified as iHKG and (ii) were given a 'Standard Name' (for example, TRR1 or TSA1).
Potential specificities of Type I and Type II gene functions. To conclude our description of iHKG and associated func-tional networks, we examined the repartition of iHKG of Type I and Type II in each functional network. Results are presented in Figure  5C. We observed that the allocation of iHKG greatly differ between Type I and Type II. Differences were particularly important for networks 'Metabolism' (8% in Type I compared to 39% in Type II), 'Transport/trafficking' (18% in Type I compared to 6% in Type II) and 'ISC synthesis and assembly' (1. 4% in Type I compared to 5% in Type II). To better explain this observation, we searched for significantly enriched GO terms in lists of Type I and Type II genes, respectively, using the GO database sections 'Cellular component' and 'Molecular function' ('Biological Process' was already used to define Meta-GO, Figure  3 ).
00076). It is the particular role of Type II genes to protect internal cellular functions which is highlighted, through the use of traditional mechanisms of stress response (ribosomal proteins) and stabilization of key processes that critically depend on iron (heme utilization or the pathway for iron-sulfur cluster synthesis and assembly). Again, this is relevant to the observation that Type II genes are more represented in the networks 'Metabolism' and 'ISC synthesis and assembly' than Type I genes. To summarize this section, we observed in our functional networks of co-expressed genes, many genes already known for their role in iron homeostasis in C. glabrata. Such an observation gives credence to the biological interest of the transcriptomics dataset we produced in this study. We also described genes for which annotations were available in public databases and whose functions remain coherent with iron homeostasis processes in C.
At last, we performed the experiments at two different temperatures, i. e. 30 • C and 37 • C. It was important to verify that at 37 • (the human body temperature), the transcriptomic responses remained consistent with those described at 30 • , which is the classical temperature used for C. glabrata cell cultures in laboratories. To our knowledge, this represents the first transcriptomic dataset published for C. glabrata that allows gene expression comparison between 30 • and 37 •. Even if we observed that the temperature did not have a critical effect on the transcriptomic response which arises either in low or in high iron conditions, we still observed several hundred genes with differential expression between the two conditions (Supplementary Data S1). These results will be a valuable resource to control in any transcriptomics project that genes identified based on laboratory experiments performed at 30 • C, are not completely de-regulated at 37 • C.
In this work, our mining of the data relies greatly on the representation of the C. glabrata genes in functional networks of co-expressed genes (Figures  4  and 5 ). To infer these networks, we combined a large amount of information, both numerical (measurements of gene expression) and descriptive (GO terms, Types I or II classifications, gene names, etc. ). We divided the global network of coexpressed genes (which initially comprised the 636 iron re-sponsive genes) into six functional sub-networks, referred to as 'Metabolism', 'Regulation', 'Redox Signaling', 'Transport trafficking', 'ISC synthesis and assembly' and 'Others' (https://thomasdenecker. github. io/iHKG/). Our aims were (i) to limit the number of genes in each network, thus improving the visualization and exploration of graphs and (ii) to provide a global picture of the cellular functions in which substantial gene expression rewiring was observed.
For 10 of them, we observed interesting variations in expression levels reported in recent multi-omics datasets (RNAseq and mass spectrometry technologies). (ii) We detected differentiated regulatory mechanisms underlying transcription of the iHKG of Type I and Type II. In Type II genes, we found as enriched DNA sequence in promoters, the motif AGGG. It is the stress response element (STRE), i. e. the DNA binding site of transcription factors Msn2 and Msn4  (44). Such observation is relevant with the manner Type II genes were defined in this study (Figure  1 ) and our anticipation they could include genes involved in the general stress response of cells (ESR genes for instance  (39) ). In Type I genes, we found as enriched motif, the sequence TGCACCC. It corresponds to the DNA binding site of the transcription factor Aft1  (45) , one of the main regulators of iron homeostasis  (15).
Our results show for the first time that, in C. glabrata, HAP1 is also deregulated in excess iron conditions. At last, even if we observed the global responses at 30 • C and 37 • C are similar, we could see several iKHG that were more de-regulated at 37 • C, which is the human host temperature. Among them, we found AWP2 (CAGL0K00110g) and ERG5 (CAGL0M07656g). We provide here interesting transcriptomic data showing that they may be essential for the yeast homeostasis in iron varying conditions, and hence for the C. glabrata adaptation in the human body compartments. To conclude, the data we provide in this work is full of new research perspectives. It can help to improve the functional annotation of C. glabrata genes (only 5% of genes are 'verified' in the CGD database) and increase our understanding of the specificities of this human pathogen.
At last, the third class 'iHKG' refers to a new set of genes with particular functions important for the cell to counterbalance external fluctuations in iron availability, in any direction (low or high). (C) Schematic representation of the two types of iHKG based on the de-regulations observed in our dataset, respectively in 'low' (-) and 'high' (+) iron conditions. 'Type I' are iHKG with opposite deregulations in low and high iron conditions, whereas 'Type II' are iHKG with constant (or parallel) deregulation in low and high iron conditions.
These two-faced functional networks highlight iHKG in the pathogenic yeast C. glabrata. An emblematic illustration of the typical homeostatic role played by iHKG is the co-expression graphs obtained for the function 'Transport/trafficking', in which most of the genes match the definition of iHKG of Type I. Gene names were placed on the graphs, when they were available, according to the CGD database. (C) Allocation of Type I and Type II genes in general functions defined in this work, with the percentage of genes assigned to each function, considering iHKG of Type I (left) or type II (right).
DATA AVAILABILITY Microarray raw data files are accessible through GEO Series accession number GSE139363 (https://www. ncbi. nlm. nih. gov/geo/). Source code and result data files can be found here: https://github. com/thomasdenecker/iHKG/. The website to explore the functional networks of co-expressed genes is available here: https://thomasdenecker. github. io/ iHKG/. GEO Series accession number is GSE139363.
nVista Inscopix miniscope The Inscopix company was founded in 2011 by Kunal Ghosh, Mark Schnitzer and Abbas El Gamal in order to commercialise their miniature microscope. Some years later, their technology has evolved and the Inscopix miniature microscope, called nVista, is now widely used. The characteristics of the current version of the nVista microscope are presented in Table  2. It possesses a softwarecontrolled electronic focusing mechanism (300 μm) that does not require any manual intervention. The miniscope is connected to a DAQ box which has the capacity to store data on an SSD and/or an SD card. The DAQ box is also equipped with ethernet and WIFI connectivity, which allows remote control of the miniscope and data streaming through Web-based acquisition software. On purchasing the nVista system, researchers will receive a full support package, which makes it probably the easiest way to start working with deep-brain imaging with a miniscope.
Researchers will benefit from field scientific consultants that will, through site visits and online communication, help them set up the technology and troubleshoot experimental and conceptual challenges (from Ca 2+ indicator selection, to data analysis) in order to ensure the success of the research project. It is worth highlighting that the nVista miniscope comes with a 1-year manufacturer's warranty that can be prolonged to 5 years. For damaged scopes sent back to the company, turn-around time is typically around 1 week. In our own experience, this warranty has been valuable; we have had to return damaged nVista miniscopes to Inscopix 3-4 times per year for repair. This support package comes at a price of course; currently, the nVista system (consisting of one nVista 3. 0 scope + accessories + 1-year warranty + help + software) costs in the region of 70-80,000 USD.
Enhancement and motion correction Overall, several steps are necessary to obtain valuable signals. First, the data needs to be 'enhanced' by applying a spatial filter that enhances the signal intensity and removes as much background noise as possible. It is then necessary to perform movement correction in order to align (or register) images. This step not only helps correct for movement that can occur when animals are grooming, chewing etc. , but also helps with brain tissue movement in areas close to ventricles. Indeed, we have observed that the brain shows movement even when animals are static, which we hypothesise is due, at least in part, to cerebral spinal fluid flowing through the ventricles. Non-rigid motion-correction algorithms that run on Fiji (such as Moco  (Dubbs et al. 2016))  or Matlab (such as NoRMCorre  (Pnevmatikakis & Giovannucci 2017 )) have proven useful in solving large-scale image registration problems.
Extraction of calcium signals in regions of interest When images have been treated and motion corrected, they are ready to be analysed. Fluorescence in regions of interest (ROI) can be measured over time. The selection of ROI can easily be done by hand (ROI Manager in Fiji) in very scattered populations, but for large data sets that contain a dense populations of neurons, automated cell-sorting algorithms may be better suited to extract spatial and temporal properties of individual cellular Ca 2+ signals  (Mukamel et al. 2009 , Pnevmatikakis et al. 2016). In brief, most cell identification algorithms run principal component analysis (PCA) to remove signals that mostly encode noise. Dimension reduction is then followed by independent component analysis (ICA) to identify the spatial and temporal properties of visualized cells by segmenting the data into statistically independent spatial and temporal signals.
Analysis packages To facilitate the management of big data sets as well as data analysis by non-experts, open access analysis packages have become available and are constantly improving. The following three packages have been used successfully by the miniscope community: (1) CaImAn, a computational toolbox for large scale calcium imaging analysis, which includes movie handling, motion correction, source extraction, spike deconvolution and result visualisation  (Giovannucci et al. 2019) , which is the most up-todate package; (2) MiniscoPy, a python-based package adapted from CaImAn specifically to analyse miniscope recordings  (Zhou et al. 2018) ; and (3) MiniscopeAnalysis, an updated version of the initial miniscope analysis package developed for the miniscope project. As an alternative to these packages, Lu and colleagues  (Lu et al.
2018)  have developed a fully automated algorithm called the miniscope 1-photon imaging pipeline (MIN1PIPE), which contains several stand-alone modules and can handle a wide range of imaging conditions and qualities with minimal parameter tuning, and automatically and accurately isolate spatially localized neural signals. We have worked with MIN1PIPE and found it efficient and easy to use and adapt to our needs without having to be a specialist in data analysis. Inscopix clients will benefit from the user-friendly 'Inscopix Data Processing' software that performs all steps of the analysis (filter, motion correction, cell registration for longitudinal studies, extraction of the signal) with very limited parameter tuning. We have found it very easy to use and convenient for recordings with good SNR and limited motion artefacts.
Funding P C and J J W acknowledge financial support from the  Medical Research Council  (fellowship  MR/N008936/1  to  J J W ) and the  Engineering and Physical Sciences Research Council  (grant  EP/N014391/1  to  J J W ). P C and P M acknowledge financial support from the  Agence Nationale de la Recherche  ( ANR-15-CE14-0012-01 ,  ANR-18-CE14-0017-01 ),  France-Bioimaging  ( INBS10-GaL/AR-11/12 ),  Institut National de la Santé et de la Recherche Médicale ,  Centre National de la Recherche Scientifique ,  Université de Montpellier , and  Fondation pour la Recherche Médicale  ( DEQ20150331732 ).
We named these IoT "intelligent devices": IoTligent  [3]. Intelligence here is used in order to mitigate radio collisions and other jamming effects (propagation, malicious attacks, etc. ) in the ISM bands. Low cost here is to be considered in terms of processing power, processing resources, memory footprint, protocol overhead, and frequency resources usage. After exposing the issues we target in this work and the corresponding hypothesis in Section 2, Section 3 reminds the foundation of the learning algorithms used in IoTligent. Then, we show how we validated our approach through several gradual stages of experimentations. Measurements 1 of Section 4 give results of a proof-of-concept made in laboratory conditions using SDR (software-defined radio) platforms in order to validate the learning approach.
PoC setup As illustrated in Fig. 2 , this PoC is based on 4 USRP (Universal Software Radio Platforms) from Ettus Research and National Instrument 1  transmitting in the 434 MHz ISM bando (in order to not interfere with surrounding IoT networks usually deployed at 868 MHz). The development is made with GNU Radio 2  software, and the source code of the PoC is published on-line  3  , in order to ease the full reproducibility of our results. We have not implemented a real IoT standard in this PoC, in order to show that it can be applicable for any IoT standard. However, global characteristics are rather corresponding to the LoRa context (not ultra-narrow band, reduced number of channels, frame duration around a few hundreds of milliseconds, etc. ). One or two (or more) USRP platforms are playing the role of IoT devices that can run (or not) the proposed learning algorithms, as platforms PF#3 and PF#4 of Fig.
We can see on Fig. 4  that if we use the same scenario of traffic as in Fig. 3 , but at a different time, i. e. , with a very dense traffic on channel #0, less dense on channel #1, even less dense on channel #2, then transmission appears on channel #3, but it is indeed just even less dense. At that time of the experiment, our IoTligent device is moving from channel #2, where maybe it faced some collisions in the downlink transmission of ACK, to channel #3, where several successive transmissions and receptions seem to occur. Figure  5  is a screenshot taken at some moment during an experiment that gives the details of the learning algorithm operation embedded in IoTligent device. We can see in the top-left red data the number of selections of each channel. There is a clear disequilibrium with channel #3 that has been much more (17 times) used than channel #2 (8 times), itself more used than channel #1 (6 times) and channel #0 (only once).