dataesr/scientific-paragraphs-categorization huggingface dataset:
Mutational signatures Small variants. Signatures were extracted from the VCFs using SigProfilerExtractor, comparing decompositions with 2 to 10 de novo signatures and using the number of signatures maximizing stability while minimizing cosine differences in signature reconstruction. Temporal signature activities were reconstructed using R package TrackSig separately on public (present in both parent and PDTO) and private alterations (present only in one sample). All signatures are reported in Fig. S5  and Table  S4 , and signatures known to be due to sequencing artifacts were removed and signature contributions normalized to sum to 100% for Fig. 5.
Evolutionary analyses Mutation clustering and clonality. Somatic alterations were clustered and classified as clonal or subclonal using deconvolution of VAF distributions with R package DPclust  (Nik-Zainal et al. , 2012). Input files were prepared using R package dpclust3p, focusing on alterations with good coverage (above the target depth, 30X or 60X depending on the samples; see Table  S4 ) and in clonal CNVs regions with consistent CN estimates in at least 2 samples from the same line for more accurate reconstructions. We used 5000 iterations after a burn-in phase of 1000 iterations. DPclust computed cancer cell fractions (CCFs) based on VAF and CN states and provided a likelihood that each alteration belongs to each cluster. In order to obtain high-confidence clonal reconstruction, we assigned alterations to a given cluster only if their likelihood to belong to this cluster was greater than or equal to 95%; other alterations were classified as "Uncertain" clustering.
Clusters with less than 2. 5% of assigned alterations were excluded. Clusters with an estimated location above a CCF of 0. 95 in all samples were considered clonal. We summed up the likelihoods of belonging to each subclonal cluster, and considered alterations with a likelihood sum greater than or equal to 95% as subclonal. Venn-euler diagrams of shared clonal and subclonal alterations in Fig. 5 (A) were computed using R package eulerr. Clonality of driver small variant alterations not included in the DPclust model fit was subsequently assessed by finding the closest cluster in terms of CCF considering only alterations with the same CN as the focal driver variant. Clonality of CNVs was assessed using PURPLE estimated allelic fractions, with CNVs with both minor and major allele copy number estimates close to an integer value (with a threshold of 0.
We then ran clonevol independently on each randomly sampled centroid, and selected 20 resulting possible phylogenies; we selected the replicate with the CCF centroids closest to the point estimate for visualization. We plotted the results using R package fishplot  (Miller et al. , 2016). Because no clonal cluster and alterations were identified for LNET2, we used a polyclonal model; for all other samples, a clonal cluster was found and we used a monoclonal model. Note that alterations absent from a sample but that cluster with alterations at a non-zero frequency in this sample are thus inferred as part of a subclone detected in the focal sample; such mutations usually have a lower coverage in the focal sample, explaining the detection failure. In particular, although absent from sample LCNEC4 organoids, the APC and PTPRZ1 mutations reported in Figure  4B  were detected at a very low allelic fraction (6 and 7%, respectively) and were assigned to cluster 2 from Figure  S5A , which is at low but non-zero frequency in the PDTOs (Figure  5B ), and indeed coverage at their positions in the PDTOs were lower than in the original tumor (59 and 24 vs 89 for APC, and 96 and 24 vs 109 for PTPRZ1), making failure of detecting them if present in this cluster likely.
ACKNOWLEDGEMENTS We thank all patients participating in this study as well as the teams approaching patients for consent and collecting tissue, including the  Utrecht Portal for Organoid Technology (U-PORT ;  UMC Utrecht ) -in particular  Anneta Brousali ,  Jorieke Salij ,  Onno Kranenburg , and  Renate Bezemer  -and the clinical studies department at the  NKI -in particular Jan-Nico Ridderbos. We are grateful to the  Foundation Hubrecht Organoid Technology (HUB)  and employees, including  Patrick de Kort  and  Calinda Dingenouts , for their work supporting ethical regulatory affairs. We thank  Utrecht Sequencing  for bulk RNA-sequencing services. We acknowledge financial support from the  NET Research Foundation  (2017  Petersen Accelerator Award  to H. C. ),  Worldwide Cancer Research  ( 2020 Grant Round  to L.
10. 0. 5430 Thermo Fisher scientific Seqera labs (Di Tommaso et 35050061 https://www. nextflow. io/ HEPES NEN patient-derived tumor organoids Thermo Fisher scientific this study al. , 2017) 15630080 Table S1 Penicillin-Streptomycin GATK version 4. 1. 7. 0 Critical commercial assays Thermo Fisher scientific Broad institute 15140122 https://gatk. broadinstitute. org/hc /en-us Noggin-Fc fusion protein RNeasy Mini Kit bwa2 version 2. 0 U-Protein Express QIAGEN (Vasimuddin et al. , 2019) Cat# N002-100 ml Cat# 74104 https://github. com/bwa-mem2/b wa-mem2 R-spondin3 conditioned medium All-Prep DNA/RNA Kit made in house QIAGEN Cat# 80204 Trim-galore version 0. 6. 5 Babraham Institute https://www. bioinformatics. babr N-Acetyl-L-cysteine RNase-free DNase Set Sigma-Aldrich QIAGEN A1965 Cat# 79254 aham.
1. 2 Thermo Scientific Sigma-Aldrich (Cameron et al. ) Tecan Agilent-Dako Thermo Fisher Scientific Tecan (Islam et al. , 2021) Dako Stemgent DAKO (Shannon et al. , 2003) Dako Peprotech V&P Scientific, Inc (Gillis and Roth, 2020) Peprotech V&P Scientific, Inc CRAN https://github. com/hartwigmedic al/hmftools/tree/master/purple 1. 00496 MA5-13096 12605010 GA62661-2 https://cytoscape. org/ Cat# 04-0004-10 GA660 VP 830SV-50CB Cat# 100-18B IR628 Cat# 100-19 VP 418SV2-2-50CB-CC Rabbit monoclonal anti-EGFR (EP22) FGF10 Deposited data R package trend version 1. 1. 4 Cell Marque Peprotech R CRAN CMC41432010 Cat# 100-26.
, 2015; Paper et al. , 2016; Ruiz-Orejón et al. , 2016). To date, the majority of studies on spatiotemporal abundance and characterization of plastics have been conducted in the northwestern and westcentral Mediterranean  (Llorca et al. , 2020; Paper et al. , 2016)  and only a few studies in the eastern basin address polymeric characterization of particles  (Adamopoulou et al. , 2021; Kedzierski et al. , 2022). Furthermore, quantification of plastics is often reduced to total abundance or weight measurements and abundance values do not distinguish fragments of varying size and shape. Because plastics are heterogeneous in nature, plastic surface area has been suggested as a key metric for quantification alongside plastic count data  (Rivers et al. , 2019). To contribute this critical knowledge gap, TARA Mediterranean, a largescale expedition with a cross-disciplinary approach, crossed the entire Mediterranean Sea in 2014 between June 3 and November 8 with the aim to gain a better understanding on the sources, transport, distribution and characteristics of surface floating plastic in the different Mediterranean sub-basins.
A systematic and standardized analysis was carried out on the samples from 124 stations allowing the study of geographical distribution, abundance, weight, size, surface, circularity and category of plastic fragments. An important dataset of ~75,000 plastic debris was built and is available for further studies. In addition, a set of 4723 polymer spectra of different size classes of plastics was also constituted  (Kedzierski et al. , 2019b (Kedzierski et al. , , 2022)). We tested the hypothesis that the distribution and properties of plastics are not homogeneous across the Mediterranean and that regional differences exist in terms of loads and characteristics of plastics. Knowledge concerning the distributions and properties of plastic debris is essential in assessing the efficacy of existing regional policies in the Mediterranean, and the actions required from producers and consumers to reduce plastic pollution.
In order not to interfere with an ongoing project we did not sample the Adriatic Sea (DeFishGear. net). Due to legal issues, it was not possible to sample the Gulf of Sidra and the coastal areas of most of the Levantine basin. Geographical coordinates of sampling sites and data are available at Zenodo Data Publisher (https://doi. org/10. 5281/zenodo. 6551501). Sea surface temperature and salinity were continuously recorded by a thermosalinograph (TSG, Seabird SBE45) that measured temperature and conductivity at a sampling frequency of 0. 1 Hz. A high spectral resolution spectrophotometer (WETLabs ACs) measured at 4 Hz the absorption and attenuation of light from which the concentration of Chlorophyll a, particulate organic carbon concentration, and a size parameter for micron-sized particles were derived as in Boss et al.
We also measured the volume of filtered seawater in each tow using a mechanical flowmeter positioned at the center of the net mouth (Hydro-Bios). Comparisons of the data by the 2 methods showed that the values are significantly correlated with the flowmeter data (y = 1. 59×, r 2 = 0. 52, p < 0. 001, N 124) underestimating the volumes given by the GPS. This means that plastic abundance was consistently lower with the GPS than with the flowmeter data. We therefore chose to express the results using the distances traveled calculated by the TSG based on the observations made during the survey that the submerged part of the net (0. 16 m) always remained in the water filtering the entire sampled area due to the low towing speed of the net thus avoiding possible clogging, backflow and vertical movements of the net.
Plastic analysis The sorted plastic was counted, measured, and weighted. Plastic items were then scanned twice, in black and white and in colour to optimize processing, using the ZooScan Hydroptic system  (Gorsky et al. , 2010). A total of 75,030 items were analyzed. Particles are automatically detected and their morphological attributes are extracted through post-processing with Zooprocess and Plankton Identifier softwares (Fig. S1 ). The following parameters were calculated for each particle: the surface area (mm 2 ), length (mm), the equivalent spherical diameter, the circularity (defined as 4π area/perimeter 2 ) with values ranging from 0 to 1; 1 indicating a perfectly circular object. The circularity was only given for rigid fragments. By including recovered microplastics entangled with zooplankton and aggregates, microplastics were identified down to a size of 333 μm.
Plastics were also semi-automatically classified into six shape categories (rigid fragments, films, foam, granules, rope filaments, and microfibers) through an AI-based machine learning process. Firstly, a dataset corresponding to the typology of each plastic was built. Then the software classifies plastics scanned into different categories based on multiple parameters (as size, transparency, and shape) incrementing the database, and at the end of the process, a human validation corrects the errors in classification. Once digitized, all the plastics particles were transferred to the Ifremer LERPAC laboratory (France) to be re-analyzed following guidelines for monitoring, analysis, and evaluation by the MFSD and GESAMP  (Galgani et al. , 2013; Kershaw et al. , 2019). Briefly, plastics were counted and the three-size fractions (< 5 mm, between 5 and 20 mm, > 20 mm) were dried in an oven at 50 °C for 24 h and weighed on an electronic balance (accuracy: 0.
Linear regression models 2 and Pearson correlations were used to compare the relationships between plastic category, concentration, weight, surface area, and size. Outliers were detected using Tukey's Hinges percentiles of 25% and 75% and 1. 5 × interquartile. The Kruskal-Wallis test using Chi-Square distribution was used to test for differences in plastic concentrations (abundance and weight), plastic area, and sizes between Mediterranean sub-basins. Data from the Alboran Sea do not meet this significance criteria and are not included. Analyses of covariance (ANCOVA) and post hoc tests were carried out to test differences in the plastic size distribution among basins and differences in categories of plastics (data from foams do not meet the criteria and are not included). All analyses were performed using Matlab (TheMathWorks).
Here, we define the Plastic Emission Probability of each land-based source as follows. 2. 6. 3. 1. Cities. Cities with more than 50,000 inhabitants and less than 20 km from the coast were selected, for a total of 185 cities. The positions and populations of the cities, updated for 2015, were downloaded from the Urban Cities Database of the Global Human Settlement database. This dataset also provided the outline of each city, which was used to identify the city location along the coast. The Plastic Emission Probability of each city was obtained by multiplying its population, the rate of mismanaged plastic waste of its country  (Jambeck et al. , 2015) , the percentage of relative city contribution p c (50%), and finally by normalizing it. The following formula was employed: PEP Ci ¼ pop i ⋅ MPW country i ð Þ ∑ Ncities i¼1 pop i ⋅ MPW country i ð Þ ⋅ p c =100 (1) where PEP Ci is Plastic Emission Probability of the i-th city, pop i is the population of the i-th city, and MPW (country) is the mismanaged plastic waste rate of the country where the city is found.
Using imaging methods, we obtained a comprehensive and standardized database of ~75,000 plastics which, coupled with their chemical characterization  (Kedzierski et al. , 2019b (Kedzierski et al. , , 2022) )  provide us with a unique dataset that can be used for later investigations. These results allowed us to obtain reliable estimates of plastic dynamics and to identify the advanced state of plastic degradation that supports the hypothesis of multiple stranding and resuspension of plastic particles. This may indicate that the processes leading to the formation of microplastics in the Mediterranean were mainly initiated on land or in rivers. Monitoring changes in plastic morphological parameters is an important step in understanding the fate of plastics in the oceans and should be combined with quantitative land-sea assessments and the tracking of atmospheric deposition.
Acknowledgments We thank the Tara" schooner, crews and the teams that helped with the collection of samples during the cruise. The authors are grateful to  Marie-Emmanuelle Kerros ,  Gerasimos Korres ,  Juliette Mauri ,  Lisa Rouaud-Clergues ,  Maxime Olsommer  for their help in samples analysis. We thank  MERCATOR-CORIOLIS  and  ACRI-ST  for providing daily satellite data during the expedition and Copernicus Marine Environment Monitoring Service (CMEMS, http://marine. copernicus. eu/). We are also grateful to the  French Ministry of Foreign Affairs  for supporting the expedition and to the countries that granted sampling permission.
The power consumption can be easily measured by observing the current or the voltage on a shunt resistor or the EM radiations using a magnetic probe. The relative ease with which these attacks can be applied makes them extremely powerful. The power analysis attacks can either exploit the relationship between the measured power and the operations executed by the device known as Simple Power Analysis (SPA)  [1]  or by understanding the correlation between the processed data and the measured power called the Correlation Power Analysis (CPA)  [1]. A closer inspection of physical attacks reveal that the basic attribute of security, i. e. , Confidentiality cannot be transferred to the hardware realization of a cryptographic algorithm. Once under the control/possession of an attacker, there are often no technical or time limitations to characterize the device, underlying security circuits, and eventually extracting the secret key.
5. A total of 10,000 traces has been recorded for each variant. For the system with active PR, the number of traces depend upon the reconfiguration time (see Equation (  1 )). The physical realization of netlist randomization has been accomplished by implementing a self-configurable system (Figure  4 ) based on the concepts presented in Section 3. The proposed system consists of two partial regions (PA0 and PA1) for hosting any of the generated variants. For partial reconfiguration, the Internal Configuration Access Port (ICAP)  [18]  is used to configure the regions PA0 and PA1 with partial bitstreams. The 32-bit wide ICAP interface can operate at a maximum frequency of 100 MHz. Therefore, the theoretical throughput of the ICAP interface is around 400 MB s   As mentioned before, most of the features of the implemented self-configurable system have been borrowed from the system shown in Figure  1 , however, for the sake of simplicity some features such as the TRNG have been implemented in the ChipWhisperer software.
Evaluation As mentioned earlier (see Section 3) the power-profile of a PR system is a confluence of several components. In this section, we attempt to isolate the influence of these components by analyzing the power consumption of our PR system under different scenarios. The collected power traces are then passed to a CPA script which quantifies the difficulty of an attack for a particular scenario. As mentioned in Section 5, for experimentation we use a total of 11 designs which include 10 variants and the baseline implementation. The results quantify the effect of following components/parameters: 1. Placement: the location of a variant PA0 or PA1.
The USB clock and PR were switched off during these measurements. Version June 25, 2022 submitted to J. Low Power Electron. Appl. 9 of 16 by analyzing the power consumption of our PR system under different scenarios. The collected power traces are then passed to a CPA script which quantifies the difficulty of an attack for a particular scenario. As mentioned in Section 5, for experimentation we use a total of 11 designs which include 10 variants and the baseline implementation. The results quantify the effect of following components/parameters: 1. Placement: the location of a variant PA0 or PA1.
Additional USB Clock The ChipWhisperer design has two clock domains: the USB and the encryption clock. The USB clock fixed at 96 MHz is used to send and receive data to/from all the control and status registers, while the encryption clock is dedicated for the target AES implementation  [17]. By disabling the USB clock, a measurement can be made without any switching in static region of the design. Only the circuit-under-test (the AES implementation in this case) can be measured without noise from remaining circuitry in the system. For this work, we intend to quantify the influence of different parameters influencing the resistance against SCA. Therefore, we collect traces for both scenarios with/without USB clock. The results were collected for a randomly selected variant, v0 in this case.
Each row details a given integration stage in the early visual system (from top to bottom rows: RGC, LGN, PGN, V1). When available, experimental population averages (empty squares and circles with error bars) were used, otherwise, multiple single-cell recordings (various filled symbols) were used. The original firing rates are reported on the top of the vertical axis, using the same color code. The number of recorded model cells is reported in the lower right of column a (green), and, when available from experimental studies, in the other plots (black). When available, experimental data having multiple stimulus feature variations in the same study were used over data from single feature studies. We resorted to macaque data when data for cat was unavailable (see the stimuli section of Methods for experimental data sources).
Methods and Materials Here we provide the description of the principles used to construct the simulated cortical and thalamic regions, the various connection pathways within and between these regions, and the cellular and synaptic properties. We also detail the experimental protocols, the procedures for the collection and analysis of data, and the software stack used to develop and simulate the model. The full code listing and parameters are available on the project page on GitHub (https://github. com/dguarino/T2).
Acknowledgments We would like to thank  Damien Depannemaecker ,  Karolína Korvasová  and  Alain Destexhe , for stimulating discussions and critically reading the manuscript. This work was primarily done at the  UNIC (Unit of Neuroscience, Information and Complexity, UPR CNRS  3293). The work was funded by grants to Y. F. and A. D. from the  Agence Nationale de la Recherche  ( ANR-Horizontal- V1  ( ANR-17-CE37-0006 ), the  Paris-Saclay IDEX  (NeuroSaclay and i-Code) and the  FET-Proactive  Grant Agreement No  269921  (BrainScales). D. G. received continuous funding from the  EC Human Brain Project  (HBP, grant agreement, n. 604102 ). The work was also funded by grant to J. A. from  Charles University in Prague  ( PRIMUS/20/MED/006 ) and joined grant to J. A. and D. G. (CZ MSMT project No. 8J20FR006 ).
Software stack In order to replicate as closely as possible the same experimental conditions as those in the literature, we need to have an experimental setup, not just a one-shot simulation. Therefore a whole infrastructure is needed, to prepare different types of stimuli, to operate selective inactivation of layers and units, to perform recordings and analysis. We modeled the thalamo-cortical loop using Mozaik (Antolik and Davison 2013, see code at https://github. com/antolikjan/mozaik). Mozaik is an integrated workflow framework for large scale neural simulations, intended to relieve users from writing boilerplate code for projects involving complex heterogeneous neural network models, complex stimuli and experimental protocols and subsequent analysis and plotting. It is built on top of the following tools: imagen (for stimuli generation), PyNN 0.
8. 0 (for simulator independent neural network model definition, see http: //neuralensemble. org/PyNN/), NEST simulator 2. 1. 0, Neo (for exchange and internal representation of data), matplotlib (for plotting). Additional analysis code was written using the scipy library (https: //www. scipy. org/). All the code is available online (see https://github. com/dguarino/T2). Experimental data were digitized by scraping the original published material using WebPlotDigitizer 3. 11 (http://arohatgi. info/WebPlotDigitizer).
Considering α + β < 1 allows us to achieve accurate estimation of the IF for the three considered components. The computational time associated with the competing methods applied on the MCS depicted in Figure  4  is reported in Table  3  for different frequency resolutions. All the experiments have been computed by using Matlab R2021b with an Intel(R) Xeon(R) W-2123 CPU @ 3. 60 GHz. Although the computational gain is implementation-dependent and could be improved via parallel programming, we notice that the proposed method as well as the RD  [11]  are more time consuming than the Brevdo method  [33]  when performing IF estimation for all frequency resolutions. Nonetheless, we remark that the complexity of the proposed method is similar but faster than  [11]. The most computationally expensive step of the proposed algorithm is the computation of the pseudolikelihood, which has to be performed for each time instant.
These CBS-proteins are widespread, and the analysis of 333 cyanobacterial genomes revealed the presence of many variants (Figure  5 )  [58]. A CP12-like protein was reported in the freshwater diatom, Asterionella formosa, that was associated with GAPDH and the ferredoxin NADP reductase, but the sequence of this protein is not available  [59, 60]. In contrast, in the marine diatom, Thalassiosira pseudonana, three CP12 proteins were identified, CP12-1 and CP12-2 were predicted to be localized in the chloroplast, and only CP12-2 was found in expressed sequence tags (ESTs) database and further characterized  [61]. The gene coding for this protein in other diatoms was also found. In diatoms, nonetheless, PRK/GAPDH/CP12 has never been found  [62] , and it seems that the absence of two cysteine residues at positions 245 and 248 on diatom PRK could explain this  [63].