dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, at moderate compression factors -up to around 15 -20, at which point the bunch length approaches 100 fs, we see relatively good agreement between the codes and experimental data to within 10 %. It can also be seen that the ELEGANT simulations reproduce the analytic estimates for emittance growth in Figs. 13a  and 14a , suggesting that the code accurately reflects the predictions of the 1D theory; however, this also shows that the 1D theory may be inadequate for describing the effect of CSR in more extreme bunch compression scenarios. The fact that the codes which calculate the CSR fields directly from the retarded potentials give a closer agreement with experimental data further suggests that there are limits to the applicability of the 1D CSR approximation. • • • • • • • • • • • ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ 70 • • • • • • • • • • • ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ 70 • • • • • • • • • ■ ■ ■ ■ ■ ■ ■ ■ ■ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ -2 -1 0 • • • • • • • • ■ ■ ■ ■ ■ ■ ■ ■ ■ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ -2 -1 0 As the compression factor is increased -up to a maximum value of 64 -more significant discrepancies between the simulation results appear.
Decline in the Arctic sea ice extent (SIE) is an area of active scientific research with profound socioeconomic implications. Of particular interest are reliable methods for SIE forecasting on subseasonal time scales, in particular from early summer into fall, when sea ice coverage in the Arctic reaches its minimum. Here, we apply the recent data-adaptive harmonic (DAH) technique of Chekroun and Kondrashov, (2017), Chaos, 27 for the description, modeling and prediction of the Multisensor Analyzed Sea Ice Extent (MASIE, 2006(MASIE,  -2016) )  data set. The DAH decomposition of MASIE identifies narrowband, spatio-temporal data-adaptive modes over four key Arctic regions. The time evolution of the DAH coefficients of these modes can be modelled and predicted by using a set of coupled Stuart-Landau stochastic differential equations that capture the modes' frequencies and amplitude modulation in time.
The main goal of this article is thus to develop a novel data-driven sea ice model that includes memory effects for participation in the SIPN-coordinated summertime projections for September SIE  (Stroeve et al. , 2015). Section 2 introduces the Arctic sea ice data set used in this study, namely the Multisensor Analyzed Sea Ice Extent (MASIE) data. Sections 3 and 4 present key mathematical and numerical details about the DAH decomposition, and DAH-based MSLMs, respectively, as well as the usage of the latter models for prediction. Section 5. 1 demonstrates how DAH-MSLM helps decompose the complex regional dynamics in the MASIE data set by harmonic patterns that enables an efficient stochastic modelling. Section 5. 2 discusses retrospective forecasts of September MASIE in 2013-2016 by our DAH-MSLM model that obey SIPN submission protocols.
Data There are two kinds of data sets related to real-time monitoring of Arctic sea ice. The widely used Sea Ice Index (SII) data set of the National Snow & Ice Center relies on satellite passive microwave measurements as its only source; it provides 35 years of daily sea ice concentrations from 1979 through the present. The passive microwave satellite observations are automatically processed by the NASA Team  (Cavalieri et al. , 1996)  and bootstrap algorithms  (Comiso, 2014)  to create daily maps of sea ice concentrations; each of the two algorithms has its own biases and limitations. On the other hand, the MASIE in the Northern Hemisphere  (Fetterer et al. , 2010)  uses several satellite data sources including passive microwave, but it is also based on visual analysis and other data sources and undergoes a form of manual data fusion.
The main purpose of MASIE is to provide accurate daily ice conditions to support navigation and operational forecast models, and it is available since 2006. Another difference between SII and MASIE is spatial resolution; the nominal resolution of the latter is 4 km, much higher than the 25 km of the former. The underlying data for the MASIE are aggregated further in space, and the final product provides information about Northern Hemisphere-wide SIE, as well as for 16 smaller Arctic regions http://nsidc. org/data/masie/. Meier et al. (2015)  performed comprehensive comparisons of the SII and MASIE data sets and found that MASIE shows higher Arctic-wide extent values throughout most of the year, largely because of the limitations of passive microwave sensors in some conditions, such as surface melt.
During the periods, however, that lead to the peak of the melt season, i. e. May-June, as well as at the end of the melt season and the beginning of freeze-up, i. e. late September-October, MASIE tends to indicate lesser ice extents than estimated by SII. Since our main purpose here is to develop a data-driven model for SIPN submission, MASIE was chosen because it is updated and readily available in real time. Also, to the best of our knowledge, MASIE is currently not used by empirical models submitted to SIPN. We have used MASIE time series for the North Hemisphere from 1 January 2006 through 27 September 2016. The original daily data were aggregated into a weekly resolution of 52 weeks in each calendar year, as well as combined into four large Arctic regions by adding the underlying subset of 16 regional time series  (Fetterer et al.
This study focuses on the analysis, modelling and prediction of these four time series that we will refer hereafter as the MASIE data set (see Fig. 2a ). Weekly anomalies of these time series, after removal of the seasonal cycle, are shown in Figure  2b  for the full time length of the available MASIE data set and in Figure  3  for the last four corresponding calendar years, i. e. from 2013 to 2016. As shown by these figures, the time evolution of these anomalies does not reveal any recurrent features such as in other climatic fields like ENSO, making its modelling and prediction particularly challenging (cf. Chekroun et al. , 2011a). The total anomaly is dominated by large deviations in the fall, such as in September 2012, with its historic minimum for both the SII and MASIE data sets, while September 2006 exhibited a large positive anomaly.
Multilayer Stuart-Landau models We turn now to the key aspects of the modelling of a complex data set such as the MASIE data set, by exploiting the DAH eigenspectrum. Instead of addressing the direct modelling of the multivariate data set X, we aim instead at modelling the DAHCs, ξ j , and then at using the reconstruction formulas (3. 16) and (3. 17). The DAHCs are thus the trans- formed coordinates mentioned in Section 1. As we explain below, the modelling of these transformed coordinates is simplified due to (i) the near-phase-quadrature property satisfied by any DAHC pair and (ii) its narrowband character. To setup the ideas, consider a DAHC pair ξ ξ ′ t t ( ( ), ( )) j j associated with a pair of DAH eigenvalues λ λ ′ ( , ) j j , such that λ λ = -′ j j with j and ′ j that belong thus to the same subset  f ( ) associated with a frequency f.
(2015). Recalling that each DAHC is of length ′ N given by (3. 15), in order to predict the original variable for K steps ahead, first an MSLM prediction of the DAHC pairs is performed for K steps ahead, and then extended DAHCs of length + ′ N K are formed by concatenation followed by a convolution with the associated DAHMs according to (3. 16). The RCs thus obtained are added in each frequency bin to yield the corresponding HRCs given by (3. 17). The latter HRCs, in turn, are added across the frequencies to obtain the DAH-MSLM predictions in the original variables. We turn next to the application of the DAH-MSLM approach for modelling and prediction of the MASIE data set.
Recalling the interpretation provided by (3. 6), the separation between the DAH pairs at a given frequency f reflects how the singular values of the cross-spectral matrix f ( ) C defined by (3. 5) are distributed for our MASIE data set. As explained in Section 3. 2, the DAHMs allow us to define also a phase spectrum  Φ given by (3. 13) and shown in Figure  2d , as  varies from 1 to B f. This phase spectrum reflects here the complex nature of sea ice dynamics across the spatio-temporal scales. In particular, there is a hint of parallel lines across the whole frequency band within a diffuse background that indicates a possible inherent noisy content. On the other hand, the identification of structures in the DAH phase spectrum such as discussed in Section 3. 2 may be made complicated here due to uncertainties related to the shortness of MASIE observations.
For most of the frequencies, the predictions of HRCs are fairly consistent and are close to the HRCs obtained from observations (black circles in Figs 10-13). The frequencies for which the HRC predictions are robust tend to change from year to year, but typically lie in the lower-frequency range. However, for certain frequencies and depending on the month and the year, the predictions may deviate substantially from each other and/or from the observed values. This feature is visible for instance at f 0, 0. 039, 0. 079 = and 0. 118 in 2015 (see Fig. 12 ), f = 0. 105 in 2014 (see Fig. 11 ) and = f 0, 0. 118 in 2013 (see Fig. 10 ). Such forecast sensitivity may be due to highly transient anomalies that develop during the melt season, possibly caused by external (and non-stationary) factors that are not taken into account in the MASIE data set, and thus not included in our modelling.
METHODS Optimization involve multiple parts: in this section will be detailed the evaluation function used to affect a fitness value to each retrofit strategy, the algorithm that has been chosen to carry out the optimization and the decision space of the optimization. To evaluate the energy efficiency of a retrofit strategy, a test building has been modelled. This building is constituted of 4 floors and 8 apartments. Each apartment is 65m2 large and is 2,3m high. In the model, each of those apartments is a thermal zone. The building is modelled in Modelica language using models issued from the BuildSysPro library, developed by EDF  (Plessis et al. 2014). The model features an ideal heating generator and will be upgraded in future work with realistic systems. A TMY file is used to simulate the weather of the city of Nice (France).
Moreover, genetic algorithm are widely used in building physics, especially NSGA2  (Deb et al. 2000)  as it has proven to be both stable and efficient. In this paper, we used the implementation of NSGA2 provided in Python library DEAP  (Fortin et al. 2012). The parameters used are the following: Those parameters were selected after a literature study  (Rosenthal and Borschbach). They enable to have a good exploration of the decision space with mutation while the convergence speed towards Pareto front is not slowed down. The number of individuals has been selected to be 96 for a good diversity and to suit the 48 cores available for parallel computation. In this paper, the main focus is on the decision space. In our case, the decision space is constituted of a series of retrofit possibilities.
We reveal that the pattern observed is rather due to differences in the copy number of the plasmid carrying the guide RNA library that arise after DNA cleavage in different regions of the chromosome. The loss of some genomic regions leads to an interruption of plasmid replication, while the plasmid can still replicate and increase its copy number after cleavage in other regions. The scale and shape of the pattern is determined by the extent of chromosomal DNA being degraded after Cas9 cleavage. After taking this interesting phenomenon into account, one can still observe differences in the efficiency of guide RNAs targeting the chromosome of E. coli. We built a neural network model able to predict this efficiency based on the guide sequence. Model previously build on Eukaryotic datasets have a limited predictive power on our data, highlighting the need to develop specific models of guide RNA activity in bacteria.
Xu et al. 2015; Moreno-Mateos et al. 2015; Doench et al. 2014). Conversely, the presence of a guanine at the last position of the guide has a positive effect in our model, which was also consistently reported in previous studies. The ability of a simple linear model to obtain good predictions suggests that the determinants of guide efficiency in E. coli are quite simple and can for the most part be inferred from the specific bases present at each position. This model is however not able to encode more complex interactions between positions. The large amount of data collected in this experiments enables to train neural network models which have recently been successfully employed to model sequence data  (Kim et al. 2018; Alipanahi et al. 2015). The dataset was split into a training, validation and test set.
Comparison to other models We then compared our model to two of the most widely used models for on-target activity prediction. Doench and colleagues modeled the activity of guide RNAs expressed from a U6 promoter on a DNA vector in human cells  (Doench et al. 2016)  A previous report showed that these two models make very different predictions suggesting that the determinants of guide efficiency strongly depend on the exact experimental setup  (Haeussler et al. 2016). When applied to our data, the model of Moreno-Mateos provides some predictive power, and conversely our model shows some predictive power on the data from Moreno-Mateos. The model of Doench performs weak predictions on the E. coli and zebrafish data. In order to shed light on the specific sequence features that matter in these different datasets, we plotted the impact of having specific bases at each position along the guide RNA, i.
e. the difference between the average activity of guides that have this feature and the average activity of guides that do not have it. This analysis reveals that many of the features that impact the activity of guides in E. coli also impact the activity of guides in the zebrafish dataset. Note that because of experimental constrains, the dataset of Moreno-Mateos only includes guides whose first two bases are guanines. In our dataset these two bases are the most important features determining guide activity, but their effect is unknown in the experimental setup of Moreno-Mateos.
Discussion Over the last few years numerous studies have led to a detailed understanding of Cas9 target search mechanism, structure, and biochemical activity  (Gasiunas et al. 2012; Jinek et al. 2012 ; S. H. B, C ) Comparison of the impact of specific bases along the guide sequence between the different datasets, i. e. the difference between the average activity of guides that have this feature and the average activity of guides that do not have it. The following color code is used for bases (Adenine: blue, Thymine: yellow, Guanine: green, Cytosine: red). 12  Sternberg et al. 2014; Jinek et al. 2014; Szczelkun et al. 2014; Anders et al. 2014; Samuel H. Sternberg et al. 2015). Despite the impressive amount of knowledge gathered, little is known about how the guide RNA sequence affects the efficiency of Cas9 targeting.
Several groups have performed highthroughput screens in eukaryotic cells to measure the activity of thousands of guide RNAs, enabling the development of algorithms that can predict the efficiency with which a guide RNA can knockout a gene  (Doench et al. 2014; Moreno-Mateos et al. 2015; H. Xu et al. 2015; Doench et al. 2016). Strikingly, these algorithms tend to perform well on some datasets and very poorly on others  (Haeussler et al. 2016; Labuhn et al. 2018 ). Some of these differences have been attributed to the specificities of the experimental system and whether the guide RNA is transcribed in vitro from a T7 promoter followed by injection in cells, or in vivo from a U6 promoter on a DNA vector  (Haeussler et al. 2016). For instance, assays based on DNA vectors consistently observed a bias against uracil at the 3'end of the guide RNA sequence, which is due to the propensity of the RNA polymerase III to terminate transcription at uridine-rich sequences  (Doench et al.
2014; Wang et al. 2014; Wu et al. 2014; Moreno-Mateos et al. 2015). These results argue for the necessity to develop guide RNA design tools that are specific to each experimental setup. Here we measured guide RNA depletion after cleavage in the chromosome of E. coli, enabling the investigation of features that affect Cas9 targeting in a completely different experimental setup than previous reports, and with a lot more data points. In this assay efficient guides kill the bacteria and are rapidly depleted. The most striking feature that explained differences between guide RNAs was their position along the chromosome. Studies in Eukaryotic systems have shown the importance of chromatin structure and accessibility on Cas9 targeting efficiency  (Kuscu et al. 2014; Wu et al. 2014; Horlbeck et al.
Plasmids and E. coli strains construction Strain LC-E19 was constructed using the pOSIP system  (St-Pierre et al. 2013 ) and the backbones were removed using the pE-FLP plasmid  (St-Pierre et al. 2013). The Ptet-Cas9 expression cassette was integrated at the HK022 attB and a gfp (Green Fluorescent Protein) reporter gene under the control the sulA promoter was integrated at λ attB to monitor SOS response. Genes recD and sbcB were deleted from strain LC-E19 using the lambda red recombineering strategy  (Sharan et al. 2009). Plasmid pKD4 was used as a template to generate linear DNA fragments via polymerase chain reaction (PCR) followed by electroporation into strain LC-E19 carrying plasmid pKOBEG-A  (Chaveroche, Ghigo, and d' Enfert 2000). Colonies resistant to kanamycin were selected and the resistance gene was then removed using plasmid pE-FLP.
Machine learning For each guide of the library, we computed a sgRNA activity score as the difference between its log2FC and the mean log2FC of all the guides within 6 Kbp, normalized between 0 and 1. The activity score of guides was computed as the mean activity from 4 independent assays measured in strain LC-E19 using the cosmid transduction assay. After splitting the dataset into a training, validation and test sets, we implemented a neural network (Fig  3b ) using Keras and TensorFlow. We used a model architecture inspired from our previous work  (Cui et al. 2018 ) and consisting of 2 locally-connected layers of size 20 and 8, with kernel sizes of 4 and 7, followed by 2 dense layers of size 14 and 8. The layers sizes and kernel sizes were optimized to minimize the loss on the validation set using a grid search approach.
Acknowledgements We thank  Gizem Ozbaykal  for the help with the fluorescence and phase-contrast microscopy. DNA sequencing after Cas9 cleavage was performed by  Cédric Fund  from the  Institut Pasteur Genomics Platform , a member of " France Génomique " consortium ( ANR10-INBS-09-08 ). This work was supported by the  European Research Council (ERC)  under the Europe Union's  Horizon 2020 research and innovation program  (grant agreement No [ 677823 ]); the  French Government 's  Investissement d'Avenir program ;  Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases ' [ ANR-10-LABX-62-IBEID ]; the  Pasteur-Weizmann consortium , and a  Ramon Areces Fellowship  to B. G.
The EXPEL methodology allows the identification of protein biomarkers EXPEL-extruded fluids were collected from 7 colon cancer and 6 liver metastases patients with their corresponding normal tissue counterparts. They were processed for proteome analysis using UPLC-MS/MS methodology. Due to a difference in complexity, EXPEL fluids derived from colorectal samples were processed using 1D-LC-MS/MS analysis, while those originating from liver metastases were analysed using 2D-LC-MX/ MS. Over 2000 and 3000 proteins were identified for colon-and liver-extruded fluids respectively. To obtain an overview of the EXPEL-extruded fluid protein datasets, hierarchical clustering followed by Spearman Rank Correlation was performed using the LFQ (label free quantification, see material and methods) values for both colon and liver samples.
Interestingly, as shown in Supplementary Figure  2 , the general trend is that EXPEL fluids from the normal samples cluster separately from the tumoural ones. Exceptions to this trend were mainly found in the colon tissues datasets, a result that could be ascribable to the complex cellular composition of these tissues. To narrow down the list of potential biomarkers, we have further considered only proteins consistently identified in at least 4 out of 7 colon-and 3 out of 6 liver-EXPEL-extruded fluids (Figure  2A  upper panel, left for CRC and right for CRC-LM). To gain insight in the predicted subcellular localisation of the identified proteins, we have interrogated STRING database (http://string-db. org). Interestingly, we found that the subset of proteins located in the extracellular space was consistently more abundant in comparison with the number of proteins found in other cellular compartments (Figure  2A  upper panel pie-charts, left for CRC and right for CRC-LM).
This result highlighted the efficiency of the EXPEL method in collecting soluble and accessible proteins. Along with the subcellular localisation we further focused on investigating the pathways that could be altered by the modulated proteins identified in EXPEL fluids from colon and liver patients. To this end, we employed Ingenuity Pathway Analysis tool. The top modulated pathways are shown in Figure  2B  (left for CRC and right for CRC-LM) and can be grouped into 3 main classes i) inflammatory response, ii) cytoskeleton reorganisation and iii) metabolism. These 3 classes are well-recognised cancer hallmarks. Following the analysis of differentially expressed proteins, we have examined their expression level in normal healthy tissues. This was done at the mRNA level by interrogating BioGPS public database (Figure  2C , left for CRC and right for CRC-LM).
Proteomic data analysis Raw MS files were analysed by MaxQuant Software (version 1. 5. 2. 8, Max Planck Institute of Biochemistry, Martinsried, Germany). MS/MS spectra were searched against Uniprot human database by the Andromeda search engine. Normalisation of the data was done using LFQ algorithm  [22]. Carbamidomethyl (C) was set as a fixed modification, while oxidation of methionine and deammidation of Asn to Asp residues were used as variable modifications for all searches. Detection of minimum two peptides was required for protein identification. The false discovery rate (FDR) was set to 0. 01 on the protein and on the peptide levels. The main search tolerance was set at 5 ppm (10 ppm for MS/MS). Additional protein information, such as subcellular localisation, biological functions were determined using gene ontology annotation available on Uniprot (www.
uniprot. org), STRING version 10 (www. string-db. org) and ExoCarta (www. exocarta. org). Gene expression level of potential biomarker candidates in healthy human tissues was assessed through BioGPS public mRNA database (www. biogps. org). Pathway analysis of modulated proteins was performed using Ingenuity Pathways Analysis (IPA; Version 8. 5; www. ingenuity. com), aberrant functional networks and canonical pathways were recognised. The P value associated with a function or a pathway is a measure of the likelihood that the association between a set of focus genes in the experiment and a given process or pathway is the result of random chance; in general, a P value (calculated using the right-tailed Fisher exact test) <0. 05 indicates a statistically significant, non random association. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD005693 and PXD005709 for CRC and CRC-LM, respectively.