dataesr/scientific-paragraphs-categorization huggingface dataset:
Clonal composition of in vitro differentiated murine progenitors To visualize barcode expression patterns across all cell types in the dataset we performed hierarchical clustering and heatmap visualization in CellDestiny (Figure  10B ). This analysis showed that most barcoded progenitors differentiated into basophils, monocytes and neutrophils in this culture system, a result corroborated by quantifying barcode diversity in each cell type using the Shannon Index (Figure  10C ). Diversity measures across differentiated cell types were similar for the myeloid lineages irrespective of whether in vitro cultures were seeded with LK and LSK starting cell populations (Figure  10C ). Larger differences were observed when assessing erythroid and lymphoid lineages, however (Figure  10C ). The heatmap visualization and barcode diversity plots also showed that a number of progenitors did not share barcodes with any mature differentiated cell types (Figure  10B )-suggesting they may be differentiation inactive-a progenitor phenotype that has also been described in vivo using in situ barcoding  (8, 28, 43).
This increases confidence that lineage restriction cannot be explained by sampling biases whereby clones would appear to be non-shared simply because they are rare and therefore more likely to escape detection. In addition, we observed the same pattern when analysing LSK and LK cells separately, ruling out the hypothesis that this pattern is driven only by Sca1 - lineage-restricted progenitors such as GMPs (Figure  10F ). We corroborated this result by performing a lineage-bias classification analysis which takes the relative abundance of each barcode in each lineage into account. This analysis showed that the majority of the monocytes or the neutrophils were produced by uni-lineage progenitors, irrespective of the threshold used to classify each progenitors (Figure  10G ). Through the various graphics available in CellDestiny, we have independently re-analyzed a published dataset combining lineage tracing and gene expression analysis of single cells.
Our analyses suggest that lineage decisions in this system occur at the level of LSK cells. This is consistent with the conclusions reached in the original study, wherein the authors also incorporated transcriptomic and temporal information to enrich their analyses. Importantly, key lineage tracing metrics such as clonal diversity, clone size distributions and barcode sharing cannot be assessed using existing single cell RNA sequencing toolkits. This case study illustrates how CellDestiny can be used to efficiently visualize and explore complex lineage tracing datasets, identifying key trends at the level of barcode expression patterns to direct and inform informing downstream statistical analyses as well as multi-omics data integration.
Discussion To summarize, single-cell lineage tracing is a powerful tool to study cellular genealogies in vivo, with many applications for fundamental and applied biomedical research. In recent years, a number of single-cell lineage tracing methodologies have emerged, but tools to analyze such data remain limited. This limitation is critical as lineage tracing datasets are complex and multidimensional, characterized by high levels of variability, sparsity and technical noise. Context-specific bioinformatics pipelines have been developed to address these challenges, but the lack of standardized approaches makes it difficult to compare results across independent studies. This issue is further exacerbated by the technical demands of lineage tracing, with very few researchers capable of performing both experiments and data analysis steps.
User-friendly analytical software is important for increasing the accessibility and reproducibility of lineage-tracing, and for bridging the gap between specialists in data generation and specialists in data analysis. In this article we present an open-source computational toolkit, CellDestiny, that performs visualization and analysis of lineage-tracing datasets, complementing the existing genBaRcode and barcodeTrackR which focus on barcode pre-processing and longitudinal analyses, respectively. All three toolkits address the fundamental challenge of making single cell lineage-tracing analysis more accessible and reproducible, while the specific advantages of CellDestiny are: (i) the ability to incorporate study metadata into the analysis framework permitting greater versatility, (ii) the focus on visualizing experiment-level quality control metrics, providing visualization tools to assess the consistency of technical replicates, and to assess the frequency of repeat used barcodes.
Importantly, this app is intended as an exploratory tool, and is not intended to replace rigorous statistical analysis and testing of the data which should be performed by data analysis specialists. As a concrete example we may observe a statistically significant difference in barcode diversity between two individuals, but such an effect could potentially be explained by sampling biases. We therefore strongly advocate that biological insights obtained by using the app should be independently assessed through rigorous statistical analysis. Through case studies in lentiviral barcoding and lentiviral integration site analysis we illustrate the functionality and versatility of CellDestiny. Specifically, we show how CellDestiny can be used to assess data quality, as well as analyzing clonal diversity, clone-size distributions and lineage commitment parameters.
As long as datasets are available in a count matrix format where rows are cell identifiers (barcode sequence, insertion site, etc. ) and columns are samples, they are compatible with CellDestiny, for example CRISPR-Cas9 barcoding  (8) , polylox barcoding  (12) , and single-cell RNA barcoding  (35). To summarize, CellDestiny is an easy to use and versatile open-source software toolbox that permits the visualization and analysis of lineage tracing data. We anticipate that CellDestiny will play a key role within a broader ecosystem of analysis tools that increase the accessibility, reproducibility and standardization of single cell lineage-tracing approaches with implications for basic research and for gene therapies.
Input data format Lineage tracing dataset are typically organized in a matrix of absolute read counts where rows are cell identifiers (barcode sequence, insertion site, etc. ) and columns are samples and so this is the input format of CellDestiny. Two types of data matrix can be loaded to CellDestiny. The first matrix is for data quality control which requires a matrix with technical replicates across individuals (e. g. , sample1_dupA, sample1_dupB) to check for PCR errors and repeat usage. CellDestiny also accepts as input count matrices where technical replicates are merged (e. g. , by summing values or taking the mean value across technical replicates). The second type of input data is a metadata file giving Supplementary information about study design and sample information for instance cell types, organs, treatments, etc.
Data preprocessing A pre-processed count matrix can be loaded in the QC module of the application to visualize key quality control metrics (Figure  1 ). In the CellDestiny package, the functions ReformatQCmatrix(), MakeDuplicatesMatrix() and MakeRepeatUseMatrix() create the input matrices to check QC by plotting the barcode sharing between duplicates using the function PlotDuplicates() and between individuals using the function PlotRepeatUse(). Once these QC metrics have been visualized read counts from duplicates are summed or averaged resulting giving a new matrix that can be loaded in the analysis module of the application.
Barcode sharing To explore barcode sharing across samples, CellDestiny allows the user to visualize shared barcodes between two cell types cell types (using the MakeDotplotMatrix() and PlotDotplot() function in the package). In the dotplot visualization, barcode abundances can be plotted on different scales (logarithmic or arcsin). The dotplot can be complemented by a piechart (using MakePieChartMatrix() and PlotPieChart() in the package) which gives the percentage of shared and unshared barcodes between the two variables of choice. Shared barcodes can also be assessed across 3 cell types cell types with ternary dotplots (using the MakeTernaryPlotMatrix() and PlotTernaryPlot() function in the package). In the ternary plot, dot size varies with the total abundance of the barcodes in the samples.
Lineage bias To classify barcodes by their lineage bias, CellDestiny uses a threshold based classifier lineage described  (18). In summary, an additional normalization step per barcode is applied in each individual, thereby enabling categorization of each barcode into classes of biased output towards the analyzed cell types. Specifically, in our absolute count matrix where rows are barcodes and columns are samples, we first normalize the absolute read counts by the sample specific total number of reads (column-wise normalization). Following this step we then normalize each barcode such that each row sums to 1 (rowwise normalization). This allows us to see how the barcode is distributed across different cell types. Barcodes are assigned a bias based on whether the % read abundance exceeds a threshold value.
If one barcode contributes to a given lineage above the designated threshold then this barcode is assigned to be biased towards that lineage. Barcodes for which the % read abundance exceeds a threshold value across multiple lineages are classified as multi-outcome. More precisely: Let R bc represent the number of reads for barcode B in cell type C, and let P bc represent the proportional read abundance per barcode per cell type P bc = R bc i R bc The barcode is then classified as lineage biased if P bc equals or exceeds a threshold value B = unbiased, P bc < threshold biased, P bc ≥ threshold In the CellDestiny app, the threshold used for categorization can be tuned manually. In the CellDestiny package, MakeCategoryMatrices() prepare the data to input the PlotCategories() and the complementary PlotCategoryCounts() functions that output the number of barcodes per category and the summed contribution of all the barcodes in this category.
Heatmap and correlogram Similarities between samples can be visualized using a heatmap together with hierarchical clustering. In the package, the function MakeHeatmapMatrix() prepare the data to plot heatmaps using the PlotHeatmap() function. Several options are available for the distance and algorithm used for clustering. Additionally, a correlogram can be plotted (MakeHeatmapMatrix() and PlotCorrelogram() in the package) that illustrates the correlation of barcode abundances between all pairs of variables. If the cumulative graph has a concave shape, it means that a cell population is dominated by a small number of large clones. On the contrary, if the shape is linear, the sample is composed of a number of clones which contribute equally to the cellularity of the population. The second type of graph is a frequency distribution plot (MakeBarcodeFrequenciesMatrix() and PlotBarcodeFrequencies ()) in the package) where the user can choose between histogram or density curve -based representations of the data.
(B) Assessing repeat usage of barcodes by comparing barcode abundances across distinct individual mice from case study 1. (C) Same as panel (B) but using a heatmap to compare all the mice together. Renormalized data is arcsin transformed, clustering based on Euclidian distance and complete linkage. The frequency of repeat use barcodes is 3. 4% in this dataset. After sample QC a total of 60 barcodes were recovered for lung cDCs across three mice (m1 = 32 barcodes, m2 = 17 barcodes, m3 = 11 barcodes).
Acknowledgments We would like to acknowledge the contribution of members of the Perié team and members of the bioinformatics platform at  Institut Curie , who provided critical feedback on the software and on the manuscript. We thank  Luca Biasco  for providing the integration site data. We also thank the  Institute Curie  flow cytometry, next generation sequencing, and animal facility and the  UMR168 BMBC facility  for their technical help.
Funding This work was supported by grants from the  Labex Cell(n)Scale  ( ANR-11-LABX-0038  and  ANR-10-IDEX-0001-02 PSL ). This work is part of a project that has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme 758170-Microbar  (LP). JC was supported by a  Foundation ARC  fellowship.
(  11 ) or  (12). However, obtaining qi,fit from the Elovich equation is somewhat cumbersome because q in Eq. (  7 ) appears on both sides of the equation. In other words, the Elovich equation is implicit in q. Therefore, it is necessary to incorporate a numerical scheme within the least-squares fitting algorithm to calculate qi,fit at each iteration. Software packages such as MATLAB and Mathematica provide a convenient way to implement such a least-squares fitting procedure. In this work, the loading-implicit Elovich equation was fitted to isotherm data using Mathematica. The nonlinear least-squares fitting was provided by the command NonlinearModelFit, while qi,fit was estimated at each iteration using the command FindRoot, which uses Newton's method to solve Eq. (  7 ). Although the Elovich equation is treated as an expression implicit in q in the leastsquares fitting method described above, it is actually possible to convert the loading-implicit expression to a form that is explicit in q.
The inversion of the loading-implicit Elovich equation is achieved by means of the Lambert W function, as explained in Section 2. 2. Similar to the Langmuir and Freundlich equations, the inverted Elovich equation given by Eq. (  9 ) can be fitted to adsorption data using a conventional nonlinear regression procedure to minimize the SSR. The Lambert W function is available in several software packages such as MATLAB, Maple, Mathematica, and Mathcad, but not Excel. Highly accurate analytical approximations can be used to evaluate the Lambert W function  [42] , e. g. , in Excel. For numerical computations, a Fortran routine is also available  [43]. The loading-explicit Elovich equation, Eq. (  9 ), was fitted to the sulfadiazine adsorption data (case 1) using Mathematica. The commands used were NonlinearModelFit and ProductLog/LambertW.
Today's environmental, energy and economic challenges require changes in the control strategies of HVACs' systems, since they account for more than 60% of the building energy consumption. An optimal control law should be applied to reduce this consumption. To achieve this goal, a detailed description of the building is required, its construction components as well as the description of the occupants' activities. A good knowledge of all building components is essential, especially the thermal characteristics of the building envelope, to build an energy model of the building. The dynamic thermal simulation tool, as EnergyPlus in this case, allows us to set up a huge database of the thermal behavior of our architectural model. This database was used for the training of an Artificial Neural Network model for modeling the thermal behavior of the building, with the aim of controlling the thermal comfort of the occupants, which means maintaining the temperature of the room within a setpoint temperature range.
In addition to being very constraining, the input of all the necessary information for the white box model is very time consuming  [8] , and even if all these conditions are satisfied, the real behavior of the building could differ from the behavior predicted by the white box model. Thermal simulation tools, generally, use the white-box approach, which means that the results of its simulations are based on physical relationships, unfortunately these tools do not allow advanced control (such as MPC) of HVAC systems within their internal control library  [9]. The development of these control methods is often difficult at the level of those thermal simulation tools, Matlab allows the development of this type of controller. A thermodynamic model of a building was realized by Lapusan et al. [10]  on Matlab/Simulink.
The mathematical formulation of such control law is: minimi ze u J (y 0 , u) = N -1 ∑ k=0 ( (y (k) -y r (k)) T • Q • (y (k) -y r (k)) + u (k) T • R • u (k) ) subject to : y (k + 1) = f N N (y (k) , u (k) , p (k)) , y (0) = y 0 , u (k) ∈ U, ∀k ∈ [0, N -1] y (k) ∈ X, ∀k ∈ [0, N ] (10) Since the nonlinear function is based on an ANN, which predict with a high accuracy the zone temperature, compared to the temperature calculated by EnergyPlus (Fig. 2. ), this will be a NNMPC, which uses the neural network model to predict the future response of the plant and the optimization stage finds the values of u that minimize J. The NNMPC was implemented on Matlab, we also use CasADi  [28]  to solve our optimization problem, which needs an NLP, IpOpt  [29] , to be solved. The current state and perturbation variables were measured in EnergyPlus, where the building model is implemented, and then we solve our optimization problem for optimal control in Matlab, the EnergyPlus python plugin assure the communication between the two simulation tools, Fig.
3. At each time step, the input values needed to predict the temperature of the future area were fed to Matlab to calculate the optimal control inputs by minimizing the given objective function over a finite prediction horizon. The prediction horizon was set to 1 h, with a sampling time of 10 min, which was the same sampling time for the data used to train the model. Fig. 4. shows the temperature of the studied zone from the ANN model and the EnergyPlus model after we applied the optimal control law extracted from the MPC, the decision variable for the optimization problem was the mass flow rate of the supplied air, with a constant temperature of the supplied air.
Conclusion In this paper, a MPC based on an ANN model was developed to improve the energy performance of buildings. The ANN model was trained by a dataset obtained from a DTS tool, EnergyPlus in our study. The trained model was used as prediction model for the well-known optimal control strategy MPC. The NNMPC was implemented on Matlab, the communication between the Matlab software and EnergyPlus was provided by the EnergyPlus python plugin. The developed control strategy reduced the energy consumption for cooling and heating by 23. 74% and 39. 02%, respectively, compared to On/Off control, during the 24 h simulation. also maintains the thermal comfort by reducing considerably the oscillation of the temperature around the setpoint temperature. The deployment of this system on a large scale could have a significant impact on the socio-economic ecosystem and the environment.
3 Also, cloud computing is representing a growing share of these companies' revenue and profits: net sales of AWS increased from $7. 9 billion (7. 4% of Amazon's overall net sales) in 2015 to $35 billion (12. 5%) in 2019, representing 63. 3% of Amazon's operating income ($9. 2 billion out of $14. 5 billion) (US Congress, 2020). The value of Google Cloud Platform increased from $4 billion in 2017 (3. 7% of Google's revenue) to $8. 9 billion in 2019 (5. 5% of Google's revenue) (Alphabet -Google, 2020). Tech giants (and other minor cloud providers) sell computing services able to accommodate the information systems of companies in almost every productive sector. In relation to AI, they provide as a service every step of the AI technology system, from computing power and datasets 4 to AI models, visualization tools and platforms and protocols to build AI applications.
, 2016). In any case, European companies' prioritized activities now appear much less dynamic than activities related to software, Internet and platform technologies  (Veugelers, 2012). This has undoubtedly influenced European companies' progress in the digital era. Over time, competition in ICT (including digital) became fiercer and promising European ICT start-ups were acquired by US corporations, such as Skype acquired by Microsoft and Shazam acquired by Apple. It may be argued that these acquisitions contributed to further curtailing the emergence of European digital champions. As the European Commission (2020b) explains, US firms are the top acquirers of start-ups worldwide, with tech giants right at the top of the list. This report also shows that US companies acquired 27% of all the start-ups acquired in Europe (even by other European firms) in 2018.
Methodology We developed a methodology for studying Siemens's science and technology priorities focusing on its role as producer and user of AI based on analysing its patent portfolio and scientific publications over the last 20 years. We retrieved a corpus of Siemens's granted patents (43,725) between 1998 and 2017 from Derwent Innovation. 8 A corpus with all the scientific publications authored by Siemens or any of its subsidiaries between 1998 and 2019 (24. 615 scientific publications) was obtained from the Web of Science. We used these corpora to conduct network and lexical analyses of Siemens's science and technology strategy. Publications and patent data were processed using tools provided on the CorText platform. 9 We built co-occurrence maps using proximity algorithms. These maps associate entities (terms from the semantic analyses of patents' and publications' content) according to the frequency of their co-occurrence within a corpus of texts  (Barbier et al.
Hence, this metric measures the global distribution of the co-occurrence of each pair of nodes with all the other nodes. It is the preferred metric for textual network analysis  (Barbier et al. , 2012). To obtain a clearer sense of the evolution of topics in Siemens's patents and publications portfolios, we used CorText to generate a 'tubes layout' for each dataset (Figures  1  and 2 ). The tubes layout provides a simplified depiction of how clusters evolved over time. Clusters are shown as coloured rectangles labelled with their two most frequent multi-terms and their width represents the number of records they contain. A cluster's evolution over time is represented with grey tubes that connect clusters (rectangles) from different periods. The intensity of the grey reflects the similarity between clusters in terms of number of shared nodes.
Our findings for this period show, also, that the company considers digitalization as a service relevant to the energy, transportation and industrial production sectors. For instance, in the case of energy research, this is reflected in the fact that the most connected and most frequent multi-terms of the yellow cluster, which correspond to energy technologies and include 'steam turbine', 'gas turbine' and 'power plant' are linked to terms associated with smart grids and the IoT. These results are in line with other research that shows that Siemens has indeed 'invested heavily in IoT and CPS [cyber-physical system] related projects'  (Liao et al. , 2017: p. 2 ). In all four periods, a few multi-terms include the word data (e. g. 'dataset', 'data rate'). Some of these terms are related to mobile and communication technologies.
15 According to Siemens, MindSphere receives data from '30 million automation systems, 70 million contracted smart meters, and 800 thousand connected products' (Siemens PLM Software, 2017: p. 5). Its customers own their data and can decide whether Siemens can access them (Siemens PLM Software, 2017: p. 5). To provide these digital services, since Siemens abandoned its more general ICT initiatives, its alternative became to rely on tech giants' digital technology. Siemens purchases cloud services, including digital infrastructure, from diverse tech giants. As Siemens does not own hyper-scale data centres, data are stored in tech giants' clouds and often processed with their AI services. As explained in the Literature Review, Amazon, Microsoft and Google are the main data and AI providers, acting as intellectual monopolies that rent these intangibles as services on their clouds.