dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitation This study is not exempt from limitations. First, we did not initially perform a phantom study. To overcome this limitation, as recommended in the radiomics quality score (RQS) proposed by Lambin et al. [66] , we are currently adapting our methodology for a study on an anthropomorphic phantom filled with synthetic lesions as obtained from PET/CT images and created by the 3D printer. This model will reflect realistic tumor shapes and heterogeneity uptakes for a prospective evaluation of RFs robustness. Second, we limited our analysis to the RFs provided by the LIFEx software. Even if LIFEx's RFs are the most representative ones, they are only a limited set of the biggest group of RFs provided by the IBSI nomenclature, furthermore, two IBSI categories are not included; further and more comprehensive studies on this aspect are needed.
Fast and efficient differentiation in light, low background activity in the dark. In order to evaluate efficiency and background activity, cells harbouring the differentiation system were cultured to the exponential phase in batch and then subjected to either blue light or kept in darkness. Samples were taken at regular intervals and passed through the cytometer (Fig. 1a , bottom). We used flow cytometry data to compute the differentiated fraction by applying a threshold on mNeonGreen fluorescence (Fig. 1b ). We observed only a marginal increase in the differentiated fraction after 72 h of culture in the dark suggesting low background activity (Fig. 1c , grey inset). Induction with blue light triggered differentiation. Moreover, the efficiency of the system in budding yeast was superior when compared to existing light inducible systems in S.
1d  and 2c ). Next, we wondered if hysteresis is present in our system given repeated stimuli. To gauge the extent to which hysteresis affects differentiation dynamics, we induced cultures with repeated The plot shows the evolution of population mNeonGreen fluorescence as a function of time. The threshold was set such that only cells that have expressed detectable amounts of mNeonGreen are classified as differentiated. c Efficiency and background differentiation. Twenty colonies were picked and cultured in batch for 72 h in dark or induced at t = 0 for 4 h. Measurements were taken at t = 0, t = 24 h and t = 72 h. Bars represent means from a single experiment. Error bars signify standard deviation. Individual data points from colonies are depicted in a scatter plot overlaid on the bar plot (blue and grey insets).
We found that continuous light resulted in similar differentiation fractions as discrete pulses for the same total duration of induction (Fig. 2f ). To be able to predict the differentiated population fraction for a given light input, we developed an ordinary differential equations (ODE) model with a single parameter, the differentiation rate (Supplementary Note 3). This model was fitted to two datasets of steady-state differentiation fractions post induction with mutually exclusive single pulses. Both fits captured observed behaviours well and resulted in similar estimates for the differentiation rate (Fig. 2d ). A full description of the model as well as our fitting strategy are presented in the Supplementary Note 3. We also used the model to predict differentiation dynamics emerging from additional light input sequences and found that model predictions were in good agreement with observed data (Fig.
21 ), we set out to test if this could be exploited to modify the differentiation dynamics. We cloned two recombination cassettes of unequal to-be-excised region (C and N 0 ) along with the differentiation system (Fig. 5c ). We observed that the shorter site recombined first (CN 0 ) and subsequent pulses led to Globally, model predictions were in good agreement with the data (Supplementary Fig. 15 ). d Setup for single reactor control. Cells harbouring the GAuDi system were continuously cultured in the exponential phase. The culture was set to a target level of differentiation and continuously monitored via automated flow cytometry measurements that were analysed online. The system state was estimated from analysed data and sent to the model predictive control (MPC) module. The MPC module provided an optimized light sequence to maintain the culture at the desired set point (Supplementary Note 5).
Induction was carried out in a custom Falcon tube holder fitted with LED strips. Turbidostat: Turbidostat refers to our custom LED-equipped continuous culture platform and control software, ReacSight (Supplementary Note 1 and Supplementary Fig. 1 )  28. Experiments done in the turbidostat followed a similar protocol as described above (see section on cell handing). Cells were allowed to grow in the dark until exponential phase and induction was started only after the growth rate stabilized. Samples were taken automatically from the turbidostat at regular intervals, diluted 20 times with a pipetting robot and passed through the cytometer. The entire vessel, including pumps and tubing, was autoclaved before each experiment. Unless stated differently, the experiments used a "grow and dilute" programme where cells were allowed to grow until OD 0.
6 and then diluted to OD 0. 4. The growth rate was computed by calculating the slope of linear curve fit to the log of OD data with time. Information regarding the times when the dilutions took place was stored, in addition to the OD and LED status as csv files. Data were acquired and preprocessed using the ReacSight software framework. Subsequent treatment of the data was done in Python. Cytometry and data analysis: All cytometry measurements were acquired with a Guava EasyCyte BGV 14HT benchtop cytometer using the InCyte software (version 3. 3). The settings were kept constant for all experiments. For turbidostat experiments 5000 events were recorded for each sample, except for leakage and efficiency experiments (Fig. 1c ), for which, 50,000 events were recorded. No compensation was used during acquisition.
Data were deconvolved after acquisition (Supplementary Figs. 4  and 5 ). Data were gated using kernel density-based methods (Supplementary Fig. 3 ). Python was used for data analysis and visualization (Supplementary Note 1). Microscopy: Microscopy was performed on the inverted microscopy platform Leica DMi8 S. Live cell imaging was either performed in Ibidi µ-slide VI 0. 4 (80606) or CellASIC ONIX platform with the microfluidic plates (Y04C) provided by the vendors. The details for the exact excitation and emission spectra of the fluorophores and filters used can be found in Supplementary Table  1. Unless specified differently, the interframe interval was 6 min. During time-lapse live cell imaging, the chamber temperature was maintained at 30 °C. We used an in-house software, called MicroMator, for the automated acquisition and cell tracking.
Cell segmentation was achieved via SegMator, an in-house neural net-based segmentation algorithm  30. Python was used for data analysis and visualization (Supplementary Note 2). FIJI (ImageJ 1. 52i) was used for the processing of images shown in the manuscript. ODE model fitting and parameter estimation: ODE models were solved using solve_ivp solver from SciPy. integrate library. Models were fitted using the leastsquare method from SciPy. optimize library. Parameter search for each model was done locally (gradient descent) with multiple initial guesses for parameters and bounds on parameter values between 10 -10 and 10. To account for the delays, mean squared deviations were calculated after shifting model predictions by 60 min for the differentiation system ODE model and 120 min (differentiated & nondifferentiated), 360 min (dead), and 300 min (growth rate) for the GAuDi ODE model.
MPC experiments: ODE models were used in an MPC framework  28. The framework consisted of solving the model given a light sequence. This sequence was then optimized using a least-square method from SciPy. optimize library to minimize the error between predictions and the target set point starting from an initial state. The optimization for light sequence was done for a time horizon of 5 h in the form of 10 duty cycles of 30-min period. Cultures were sampled every hour for two reactor control experiments (Fig. 3b, c ) and every 2 h for single reactor control experiments (Fig. 3e ). The light sequence was updated at each timepoint. To estimate the state of the system, cytometry data were analysed online to determine the state at the time of sampling. This estimate of sampling time state was then used as initial conditions for the model and the current state was estimated by solving the model for a time, τ.
Acknowledgements We thank  Sebastian Sosa Carrillo  for providing plasmid backbones,  Steven Fletcher  and  Achille Fraisse  for assistance with the microscope acquisition software,  Virgile Andreani  for help with model fitting, and  Zachary Fox  for providing software for image analysis. We thank the members of the  Batt group ,  Gael Yvert , and  Hyun Youk  for conducive discussions. We are grateful to  Mustafa Khammash  ( ETH Zurich ),  Bernd Mueller-Roeber  ( University of Potsdam ), and  Matthias Peter  ( ETH Zurich ) for providing plasmids. C. A. is enrolled in the  Frontières de l'Innovation en Recherche et Education (FIRE)  doctoral school hosted by  Université de Paris. This work was supported by the  H2020 Fet-Open COSY-BIO  grant (grant agreement no. 766840 ), by the  Inria grant IPL COSY  and by  ANR  grants  CyberCircuits  ( ANR-18-CE91-0002 ),  MEMIP  ( ANR-16-CE33-0018 ), and  Cogex  ( ANR-16-CE12-0025 ).
(2011) ) or of star-forming regions  (Pilleri et al. (2012) ) using radiative transfer models. Similarly, the GREAT instrument on board the Stratospheric Observatory For Infrared Astronomy (SOFIA) now provides large-scale spectral maps in the C + line at 1. 9 THz  (Pabst et al. (2017) ). The Atacama Large Millimeter Array (ALMA) also provides final products that are spectral cubes (see e. g. ,  Goicoechea et al. (2016) ). A majority of astronomical spectrographs to be employed at large observatories in the future will provide spectral maps. This is the case for the MIRI and NISPEC instruments on the James Webb Space Telescope (JWST) and the METIS instrument on the Extremely Large Telescope (ELT). Although such 3D datasets have become common, few methods have been developed by astronomers to analyze the outstanding amount of information they contain.
Classical analysis methods tend to decompose the spectra by fitting them with simple functions (typically mixtures of Gaussians) but this has several disadvantages: 1) the a priori assumption made by the use of a given function is usually not founded physically, 2) if the number of parameters is high, the result of the fit may be degenerate, 3) for large datasets and fitting with nonlinear functions, the fitting may be very time consuming, 4) initial guesses must be provided, and, 5) the spectral fitting is usually performed on a (spatial) pixel by pixel basis, so that the extracted components A. Boulais, O. Berné, G. Faury, and Y. Deville:  Unmixing methods for astronomical hyperspectral datasets are spatially independent, whereas physical components are often present at large scales on the image.
A first application on real data of one of the methods presented here is provided in  Foschino et al. (2019). The proposed methods should be applicable to any hyperspectral dataset fulfilling the properties that we will describe hereafter. The paper is organized as follows. In the next section we present the adopted mathematical model for hyperspectral astronomical data, using tow possible conventions, spatial or spectral. We describe the mixing model and associated "blind signal separation" (BSS) problem. In Section 3, we describe the preliminary steps (preprocessing steps) that are required before applying the proposed algorithms. In Section 4 we describe in details the three methods that are used in this paper, that is, NMF (with an extension using a Monte-Carlo approach referred to as MC-NMF) and two methods based on sparsity (Space-CORR and Maximum Angle Source Separation, MASS).
Two of these dimensions are obtained directly from observations X (M and N). The third dimension, common to both Â and Ŝ matrices, is the number of sources L, which must be estimated. Here, we consider astrophysical hyperspectral data that have the properties listed below. These are relatively general properties that are applicable to a number of cases with Herschel-HIFI, ALMA, Spitzer, JWST, etc: -They do not satisfy the condition of independence of the sources. In our simulated data, elementary spectra have, by construction, similar variations (Gaussian spectra with different means, see Section 5. 1). Likewise, abundance maps associated with each elementary spectrum have similar shapes. Such data involve nonzero correlation coefficients between elementary spectra and between abundance maps.
In each run, a set of L elementary spectra are identified. The total number of obtained spectra at the end of this process is p × L. These spectra are then grouped into L sets {ω 1 , ω 2 ,. , ω L }, each set representing the same column of Â. To achieve this clustering, the method uses the K-means algorithm  (Theodoridis & Koutroumbas (2009) ) with a correlation criterion, provided in Matlab (kmeans). More details about the K-means algorithm are provided in Appendix A. To then derive the estimated value â (v) of each elementary spectrum, at each velocity v in a set ω , we estimate the probability density function (pdf) f ω ,v from the available p intensities with the Parzen kernel method provided in Matlab (ksdensity). Parzen kernel  (Theodoridis & Koutroumbas (2009) ) is a parametric method to estimate the pdf of a random variable at any point of its support.
Combination stage: This final step consists of estimating the L spatial sources from the estimation of elementary spectra and observations, under the nonnegativity constraint. Thus for each observed spectrum of index m ∈ {1,. , M}, the sources are estimated by minimizing the objective function: J( ŝm ) = 1 2 x m -Â ŝm 2 2. ŝm 0, ( 17 ) where x m is the m th observed spectrum (i. e. , the m th column of X) and ŝm the estimation of spatial contributions associated with each elementary spectrum (i. e. , the m th column of Ŝ ). This is done by using the classical nonnegative least square algorithm  (Lawson (1974) ). We here used the version of this algorithm provided in Matlab (lsqnonneg). The abundance maps are obtained by resizing the columns of Ŝ into P x × P y matrices (reverse process as compared with resizing C).
First of all, as explained in Section 3. 2, preprocessing is necessary to minimize the impact of noise on the results. For this particular method, we must keep the spatial data consistency. The aforementioned spatial thresholding is achieved by retaining only zones Z whose power is greater than a threshold. Typically some zones contain only noise and are therefore unnecessary for the spectra estimation step (detection and estimation stages of SpaceCorr). As for the NMF, we set the A. Boulais, O. Berné, G. Faury, and Y. Deville: Unmixing methods for astronomical hyperspectral datasets threshold to α n = max Z X(Z) F × 0. 2. Definition 1: A source is "active" in an analysis zone Z if its mean power is zero in Z. Definition 2: A source is "isolated" in an analysis zone Z if only this source is active in Z.
Too little zones do not allow one to reliably evaluate the correlation parameter ρ{x i , x j }(Z), hence to reliably evaluate the single-source quality of the zones. Conversely, too large zones do not ensure the presence of single-source A. Boulais, O. Berné, G. Faury, and Y. Deville: Unmixing methods for astronomical hyperspectral datasets zones. Furthermore, the size of the zones must be compatible with the data. A large number of source signals or a low number of pixels in the data can jeopardize the presence of single-source zones for each source. Thus, it is necessary to relax the sparsity condition in order to separate such data. The size of these single-source zones being a limiting factor, we suggest to reduce them to a minimum, i. e. , to one pixel: we assume that there exists at least one single-source pixel per source in the data.
8 where the case of low SNRs is considered). It is important to note, finally, that the objective here is not to produce a realistic simulated astrophysical scene or simulated dataset, but rather to have synthetic data that fulfill the statistical properties listed in Sect. 2. 3, and in which we can vary simple parameters to test their effect on the performances of the method. We also note that  Guilloteau et al. (2019)  are currently developing a model of scene and instruments to provide a realistic synthetic JWST hyperspectral datasets, and the present method could be tested on these upcoming data.
The last point to be specified in these tests is the choice of the method used to initialize MC-NMF with the "Map" initialization selected above, namely SpaceCorr or MASS. The results of Table 2 refine those derived above from Fig. 6  to 9, with a much better statistical confidence, because they are here averaged over all sources and 100 noise realizations, instead of considering only one source and one noise realization in the above figures. The results of Table  2 , in terms of NRSME and SAM, thus show that SC-NMF-Map yields slightly better performance than MASS-NMF-Map in the most sparse case, whereas MASS-NMF-Map provides significantly better performance in the least sparse case. This result is not surprising, because SC-NMF-Map sets more stringent constraints on source sparsity, but is expected to yield somewhat better performance when these constraints are made (thanks to the data averaging that it peforms over analysis zones).
Our various tests on simulated data also show robustness to additive white noise. Since the initialization of NMF with SCA methods was here shown to yield encouraging results, our future work will especially aim at developing SCA methods with lower sparsity constraints, in order to further extend the domain where the resulting hybrid SCA-NMF methods apply. A first application of the MASS-NMF-Spec method presented in this paper on real data is presented in  Foschino et al. (2019)  and shows the potential of such methods for current and future hyperspectral datasets. -Deleting the observed spectra having a power lower than 90% of the maximal observed power. Each of these tests led to the same, unexpected, trend as that observed in the first column of Fig B. 1. On the contrary, the expected behavior was observed in the following additional test, where the sparsity of the sources was varied.
We have implemented the pypgatk package and the pgdb workflow to create proteogenomics databases based on ENSEMBL resources. The tools allow the generation of protein sequences from novel protein-coding transcripts by performing a three-frame translation of pseudogenes, lncRNAs and other non-canonical transcripts, such as those produced by alternative splicing events. It also includes exonic out-of-frame translation from otherwise canonical protein-coding mRNAs. Moreover, the tool enables the generation of variant protein sequences from multiple sources of genomic variants including COSMIC, cBioportal, gnomAD and mutations detected from sequencing of patient samples. pypgatk and pgdb provide multiple functionalities for database handling including optimized target/decoy generation by the algorithm DecoyPyrat.
Introduction Proteogenomics is a rapidly developing multiomics field that integrates genomics and transcriptomics information with proteomics to improve gene annotation, often uncovering novel or non-canonical protein-coding regions in the genome  (Branca et al. , 2014). One of the most important applications is in the study of cancer cells and tumors, where identifying cancer-specific proteins holds great potential in both elucidating cancer biology and in developing cancer therapies. However, the discovery of such proteins remains particularly challenging and is still largely linked to evidence from genome sequencing data, rather than directly from the protein data that have become abundant  (Perez-Riverol et al. , 2019). Recent applications of proteogenomics have enabled multiomics detection of novel peptide sequences that are not present in the canonical protein database.
For instance, Ruiz  Cuevas et al. (2021)  recently identified a large number of non-canonical proteins in B cell lymphomas. However, customized protein databases are needed to enable the identification of such peptides. Recently, tools for generating sample-specific protein databases have been implemented using genomic sequencing data  (Ruggles et al. , 2016)  and transcriptomics data  (Cesnik et al. , 2021; Cifani et al. , 2018). Since matching sequencing data is not available for a large fraction of the currently available proteomics datasets, resources have been developed to provide protein databases generated from cancer somatic mutations and genomic variants  (Zhang et al. , 2017). To make progress in high throughput proteogenomics analysis, we present a Python application integrated into a Nextflow workflow to facilitate the generation of proteogenomics databases from sample-specific and public resources under varying conditions (e.
Non-canonical protein databases Non-canonical proteins are a product of translation of transcripts that are not reported as protein coding in the reference protein databases, or a product of out-of-frame translation of canonical transcripts  (Ruiz Cuevas et al. , 2021). While many of the non-canonical proteins could be attributed to the yet incomplete reference databases, they might also be attributed to the activation of those genes under certain conditions such as genetic and epigenetic misregulation in cancer  (Zhu et al. , 2018). We have developed the dnaseq-toproteindb tool to generate protein sequences from non-canonical transcripts such as pseudogenes and lncRNAs by performing threeframe translation. It also extracts alternative reading frames from canonical protein-coding genes to enable the detection of out-offrame cryptic proteins.
Variant protein databases Detection of altered proteins from proteomics data requires the inclusion of the mutated sequences in the target databases. However, due to a large number of potential DNA variants, only potentially relevant variant sequences should be included to keep the database size under control. Here, we implemented methods to automate generation of variant proteins from publicly available cancer mutations datasets, cancer cell lines and custom Variant Calling Format (VCF) files obtained from genome sequencing. cosmic-toproteindb and cbioportal-to-proteindb enable the generation of cancer-type specific protein databases by generating mutated protein sequences based on genomic mutations identified in cancer samples. cosmic-to-proteindb curates mutations from the Catalogue Of Somatic Mutations In Cancer (COSMIC).
It allows filtering the mutations based on cancer type or tissue of origin. Alternatively, cbioportal-to-proteindb translates genomics mutations reported by thousands of cancer studies through cBioPortal. pypgatk enables downloading and processing mutations from ENSEMBL and gnomAD resources. vcf-to-proteindb translates the genomic variants into variant protein sequences. The variants can be filtered based on functional consequences as well as allele frequency to enable a special focus on common variants. The vcf-toproteindb command accepts a custom VCF file from any species or sample of interest and generates a database of altered proteincoding sequences, which is valuable when whole-exome or wholegenome sequencing data are available; for instance to detect cancer neoantigens from passenger mutations.