dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations of this study The availability of human material was the main limiting factor, as well as the small number of subjects included, which reduced our ability to detect other possible effects of BPA. For example, we were not able to acquire su cient material for the analysis of gene expression or protein levels in vivo. In addition, classi cation of subjects was performed on the basis of urinary BPA levels assessed in a single urine sample, which may lead to misclassi cation of exposure given the nonpersistent nature and short-term viability of this chemical compound. Moreover, BPA exposure was measured in samples collected before (when boys were between 9-10 yrs. ) and the genetic markers were assessed, when the boys were between 15 and 17 years old. Notwithstanding these limitations, this dataset was su cient to reveal some interrelated associations consistent with the hypothesized toxicological pathway.
ChIP-qPCR A total of 0. 4 ng of precipitated DNA or input sample was used for qPCR analysis (0. 1 ng/µL). Quantitative PCR was performed as described before. Normalized expression values were calculated with the CFX Manager program using a region located far from the promoter as a reference gene, and we used a region in RPLP0 for ChIP normalization. Enrichment of each target in the precipitated DNA was evaluated by calculating the ratio between the average of the normalized ChIP DNA copies and the average of the normalized DNA copies in the inputs.
Availability of data and materials All sequencing and ChIP-seq data from this study are publicly available and have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus. The datasets supporting the conclusions of this article are available in the [GEO] repository, [GSE212713] and hyperlink to datasets https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE212713.
4 ). Fig. 4. Pulse transformer block diagram with error pulse circuit  [1] ,  [11]  In order to reduce the circuit size, new gate driver integrated circuits (ICs) using coreless transformer technology have been developed  [12]. This design allows smaller parasitic capacitance and a greater operating limit because of the core saturation. This study focuses on planar transformers technology in order to improve their CMTI while reducing the parasitic capacitance as low as possible by adding electrostatic shielding. The targeted application is the transmission line of high-power gate driver for a 3300 V, 500 A MOSFET SiC module. The operating voltage chosen is 2. 5 kV. First, a design method for pulse planar transformers with Altium Designer is proposed. Then a simulation methodology of said pulse transformers using Ansys is exposed.
III. PULSE TRANSFORMER SIMULATION METHODOLOGY The employed simulation process in ANSYS included alternative software tools. The model being primarily implemented in Altium designer was imported into ANSYS SIWAVE using specific data exchange files. After being verified in terms of layer stack up, thickness, and material assignment, it was exported into ANSYS Q3D Extractor which can be dynamically linked with ANSYS Circuit Design for further analysis. Being the leading extraction parasitic tool, ANSYS Q3D Extractor was employed to simulate and analyze the planar pulse transformer model. The electromagnetic field simulations conducted by this software are vital for the extraction of resistance, conductance, partial inductance and capacitance, known as RLGC parameters  [17]. The central objective is to ensure compliant simulation and experimental results which will establish a reliable foundation for imminent experimentations.
A. Transformer Eligible Frequency Band As the pulse transformer was exported into ANSYS, a spectral analysis is need to estimate the frequency band used in short pulse transmission. The method used to pass data through the galvanic isolation can be described as follow: a positive pulse on the primary winding means a rising edge of the PWM input and a negative pulse means a falling edge. With WBG semiconductors and the rising in the switching frequency, short pulses were chosen to maximize the gate driver switching frequency. Based on the pulse generator (UCC27517DBV) used, 25 ns long short pulse was targeted. Pulse parameters can be seen in Fig. 10. The signal of Fig. 10  was implemented on Matlab and a frequency bandwidth analysis was performed using the obw function. Fig. 11  shows the function result.
C. dv/dt Simulated Immunity Test Setup ANSYS Circuit design software was used to simulate a dv/dt application on the modelled pulse transformer  [24]. To replicate a severe switching condition of power semiconductors, a steep dv/dt of 125 kV µs -1 was applied between the primary and secondary windings' disparate grounds as revealed in Fig. 15. Firstly, on the primary side short 25 ns pulses with a 15 V amplitude are connected to transmit turn on and turn off signals intended to switch a 1200V SiC MOSFET. At the secondary side, pulse selection is essential to segregate positive and negative pulses, and a filter stage necessary to curtail the signal noise for a 0-3. 3 V logic at the output. After setting up and verifying the design, a transient analysis from 0 till 2000 ns was applied to evaluate the results.
VI. CONCLUSION A planar pulse transformer using electrostatic shielding for 3. 3 kV applications with respect to European standard EN 50178 was proposed in this paper. The simulation analysis used alternative software tools, Altium Designer to design, and ANSYS Q3D with dynamic links to further analyze the transformer. After performing a spectral analysis on the applied pulse signal, an eligible frequency band was identified to extract RLGC parasitic parameters in ANSYS Q3D Extractor that revealed coherent results. The study contributed to construct an equivalent circuit model of the transformer at a specific frequency and investigate the latter under abrupt dv/dt conditions which simulate the rapid switching of power semiconductors. Convenient CM noise results reveal EMC conservancy and simulation analysis reliability.
CCD4PPI: docking conformations produced by MAXDo We compiled our primary database, which we call CCD4PPI, from two complete cross-docking experiments performed on the datasets P-262  [11, 23]  and PPDBv2  [31, 32]  using the rigid-body coarse-grained docking tool MAXDo  [42]. Both P-262 (262 proteins) and PPDBv2 (168 proteins) cover a large variety of functional classes, such as antibodyantigen, enzyme-regulator, and substrates-inhibitor  [11]. For P-262, we efficiently screened 27 millions docking conformations with INTBuilder  [10]  and the rigidRMSD library  [35] , and systematically evaluated their quality with respect to the experimentally resolved complex structures available in the Protein Data Bank  [3]. For PPDBv2, we obtained the list of acceptable and incorrect conformations from  [34].
Dockground: docking conformations produced by Gramm-X We downloaded the Dockground database 1. 0  [30, 22]  from http://dockground. compbio. ku. edu/downloads/ unbound/decoy/decoys1. 0. zip. This database has 61 target complexes with on average 108 candidate conformations per target generated by the Fast Fourier Transform-based method GRAMM-X  [45]. For comparison purpose we followed the experimental setups of GNN-DOVE  [48] : in summary, 59 target complexes were chosen and divided into 4 non-redundant groups with respect to the sequence identity (less than 30%) and TM-score  [52]  (less than 0. 5). On average each of these complexes has 9. 83 acceptable conformations (L-RMSD ≤ 5. 0 Å) and 98. 5 incorrect ones.
Training protocol We used CCD4PPI to optimize DLA-Ranker hyperparameters. In total, we explored about 10 different architectures by varying the number of convolutional layers, the number of neurons in the fully connected layers, and the dropout rates. We chose the best performing architecture and used it for producing our final results and performing the comparisons with the other methods. We trained several independent models of DLA-Ranker using each of the three considered databases. Using CCD4PPI, we trained 5 models over 20 epochs through a 5-fold cross-validation procedure on the 312 protein pairs (Fig. SI  3). For comparison purposes, we reproduced the same training protocols as those reported for DeepRank  [38]  and GNN-DOVE  [48]  on BM5 and Dockground, respectively. Specifically, to compare DLA-Ranker with DeepRank, we performed 10-fold cross-validation by splitting the set of 142 dimers selected from BM5 in 114 for training, 14 for validation, and 14 for testing.
In total 140 target complexes were used in the test sets (complexes BAAD and 3F1P were not included in the testing). In all three databases the incorrect conformations are much more abundant than the near-native ones. We should stress that, contrary to what was done in  [38] , we did not augment the input conformational ensemble by random rotations since DLA-Ranker is not sensitive to the orientation of the input conformation. To compare DLA-Ranker with GNN-DOVE, we trained 4 models following 4-fold cross validation. For each model we used 3 non-redundant groups defined from Dockground for training and validation (45 or 44 complexes) and the remaining one for testing (15 or 14 complexes). To compensate the effect of imbalanced training sets and elevate the importance of errors made on near-native poses compared to incorrect ones, we assigned higher weights to the loss of the acceptable class.
Combining DLA-Ranker with the pair potential CIPS  [34]  allowed enriching the top 200 subset for that pair in near-native conformations, and overall improved the results (Fig. 2B ). CIPS also improves the performance for the pair 2c9w_A:2jz3_C by surpassing MAXDo and enriching top5. Overall, DLA-Ranker performance do not depend on the extent of conformational change between the docked protein forms and the bound forms (Fig. 2A-B , label colors). For instance, one of the cases where it performs very well, the 1ku6_B homodimer, displays a substantial rearrangement (Fig. 2F ). We further investigated the behaviour of DLA-Ranker for the different sub-regions of the interface, namely the support, core, and rim on two pairs of the database, 1yy9_D:1ck4_B and 1ku6_B homodimer. For both pairs, we observed a wide range of predicted scores within each sub-region (Fig.
SI 5 ). [48]. DLA-Ranker and GNN-DOVE display comparable hit rates on Dockground (Fig. SI 6). While GNN-DOVE identifies a near-native conformation in the top 5 for more complexes than DLA-Ranker, DLA-Ranker covers more complexes when looking at the top 15 conformations. The results differ from one fold to another and this observation may be explained by the small size of the database. It contains about 5 000 conformations versus approximately 10 000 for CCD4PPI and 450 000 for BM5 (see Methods). In the second fold, we observe a lower performance for DLA-Ranker, due to the presence of an outlier complex, namely the ribonuclease inhibitor complex (1DFJ_E_I). The structure of this complex displays several loops on the interface (Fig. SI 7A ). By comparison, the other structures of the ribonuclease inhibitor complex available in the PDB have more structured interfaces (Fig.
SI 8). In addition, we assessed the partial aggregation schemes on BM5 (Fig. 3  and SI 5) and Dockground (Fig. SI 6 ) by the models trained using all interfacial residues. The results are consistent with those on CCD4PPI, with the combination of core and rim yielding a higher performance than the combination of support and core. We also checked whether we could exploit the topological information of the interface to aggregate the learned residue-based representations. We extracted the embeddings learned by DLA-Ranker on Dockground and used them as node features in a graph representation of the interface (Fig. 1C ). We observed that the graph-based aggregation does not improve over the global averaging scheme (Fig. SI 9C-F ). This result can be explained by the fact that the individual embeddings already encode global information about the interface since the labels used during training (acceptable or incorrect) are defined at the level of the interface (Fig.
Discussion We have shown that it is possible to evaluate complex candidate conformations by learning local 3D atomic arrangements at the interface. We have developed a deep learning-based approach explicitly accounting for the relative orientations of the protein residues while being insensitive to the global orientation of the protein. The method achieves performance better or similar to the state of the art. We obtained the best performance by averaging the per-residue scores predicted over the core and the rim of the interface. DLA-Ranker can be applied to conformational ensembles generated by    ) is colored according to the number of conformations (over a total of 3 000) where each residue was found at the interface and was assigned a score higher than 0. 5 by DLA-Ranker. Three red patches appear on the surface corresponding to: (i) interface 1 (partner in green, PDB codes: 1qrr, 1i24) (ii) interface 2 (partner in violet, PDB code: 1i24), and (iii) interface 3 (partner in gold, PDB code: 1wvg).
) is colored according to the number of conformations (over a total of 3 000) where each residue was found at the interface and was assigned a score higher than 0. 5 by DLA-Ranker. Three red patches appear on the surface corresponding to: (i) interface 1 (partner in green, PDB codes: 1qrr, 1i24) (ii) interface 2 (partner in violet, PDB code: 1i24), and (iii) interface 3 (partner in gold, PDB code: 1wvg). The inset is the Normalized Interface Propensity (NIP) showing the tendency of a residue to be part of an interaction site and computed by considering the fraction of docking poses where a residue is found at the interface  [11, 15]. It is plotted on 1qrr_A with a color scale going from red (high) to blue (low propensity), and highlights interface 1 but not interfaces 2 and 3, unlike DLA-Ranker.
CADGE improves phenotypic output of synthetic cell modules We previously proposed in vitro evolution as a route to build a synthetic minimal cell  (Abil and Danelon, 2020). Here, we seek to exploit CADGE for improving the expression of genes that are relevant for the construction of functional cellular modules. One candidate gene is pssA from the Kennedy phospholipid biosynthesis pathway  (Raetz, 1976; DeChavigny et al. , 1991). The E. coli pssA gene encodes an enzyme that conjugates cytidine diphosphate-diacylglycerol (CDP-DAG) with L-serine to produce cytidine monophosphate and phosphatydilserine (PS) (Fig. 5a ), a precursor of phosphatidylethanolamine. To assay the activity of in vesiculo synthesized PssA enzyme, we encapsulated PURE and the plasmid encoding the pssA gene in phospholipid liposomes containing 5 mol% CDP-DAG, and digested the extraliposomal DNA with DNaseI (Fig.
, 2022) , could in principle be utilized as well. Other types of microcompartments can also potentially be combined with CADGE: water-in-oil emulsion droplets (Fallah-Araghi et al. , 2012), microfabricated chambers  (Zhang et al. , 2019) , and peptide-based compartments  (Vogele et al. , 2018). Emulsion droplets are particularly appealing for their high monodispersity and because they have already been integrated in microfluidic-based screening platforms for directed evolution of water-soluble enzymes  (Obexer et al. , 2017; Holstein et al. , 2021). Application of gene expressing liposomes empowered with clonal amplification is also relevant to build a synthetic cell from the ground-up. When applied to essential genes, CADGE-assisted directed evolution might accelerate the optimization of individual cellular modules and their integration to achieve higher level functions  (Abil and Danelon, 2020).
The recorded LC-MS/MS data was analyzed with Skyline for fraction calculation between unlabeled and labeled peptides ( 14 N/ 15 N ratio) on both cell-free core/produced proteins and the initially added QconCATs. With these fraction values, and considering the regular concentration of core ribosomal peptides within PURE system (2 µM), we could estimate the concentration of the cell-free expressed proteins using the following equation: ( 𝑁 𝑜 14 𝑁 𝑜 15 ) PP × 4 ( 𝑁 𝑜 14 𝑁 𝑜 15 ) CPP1 + ( 𝑁 𝑜 14 𝑁 𝑜 15 ) CPP2 , (1) where PP refers to the detected peptide of produced protein, CPP1 refers to the detected peptide 1 of the core ribosomal protein (GVVVAIDK), and CPP2 refers to the detected peptide 2 of the core ribosomal protein (VVGQLGQVLGPR). MS/MS measurement details for each of the analyzed proteins can be found in Supplementary Table  3.
Liposomes were screened with the FACSCelesta flow cytometer (BD Biosciences) using the 488-nm laser and 530/30 filter for detection of DsGreen, GFP, YFP, or Acridine Orange, and the 561-nm laser and 610/20 filter for detection of PE-Texas Red or mCherry. The following acquisition parameters were used: photon multiplier tube voltages set at 375 V for forward scatter, 260 V for side scatter (SSC), DsGreen detection at 500 V, PE-Texas Red detection at 300-370 V, YFP detection at 550 V, GFP detection at 700 V, mCherry detection at 370 V, Acridine Orange detection at 400 V, threshold for SSC at 200 V, sample flow 1 (~1000 events/s), injection volume 50-200 μL, recording of 10-100,000 total events. Data were analyzed using Cytobank (https://community. cytobank. org/). Raw data was pre-processed as described in Fig.
Confocal microscopy and image analysis. A custom-made glass imaging chamber was functionalized with BSA-biotin:BSA and Neutravidin as previously described  (Blanken et al. , 2019). The liposome suspension (3-7 μL) was supplemented with PB buffer to a maximum volume of 7 μL and transferred into the functionalized chamber. The LactC2-GFP probe was used at a final concentration of ~260 nM. After 30 to 60 min incubation at room temperature to let the liposomes sediment, the sample was imaged with a Nikon A1R Laser scanning confocal microscope equipped with a ×100 objective and operated via the NIS Elements software (Nikon). The laser lines 488 nm (for MinC-eGFP), 514 nm (for YFP) and 561 nm (for DHPE-Texas Red and LactC2-mCherry) were used in combination with appropriate emission filters. The position of the focal plane was manually adjusted to image as many liposomes as possible across their equatorial plane.
Acknowledgments We are grateful to  Margarita Salas  and  Mario Mencía Caballero  ( Universidad Autónoma de Madrid ), and  Alicia del Prado  and  Miguel de Vega  ( Centro de Biología Molecular Severo Ochoa, Madrid ) for kindly providing us with the purified DNAP, TP, SSB, and DSB proteins. We thank  Yannick Rondelez  and  Thibault Di Meo  ( ESPCI, Paris ) for fruitful discussions. We also thank  Ilja Westerlaken  for the purification of LactC2-mCherry and LactC2-eGFP proteins,  Elisa Godino  for helping with the preparation and imaging of MinD-containing liposome samples. We are grateful to  Duco Blanken ,  Flora Yang  and  Anne Doerr  for their assistance with the mass spectrometry experimental set up. Finally, we are thankful to  Tess Bevers  for assistance with cloning of the ori-p3 plasmid and  Mats van Tongeren  for developing SMELDit.
During data acquisition, which is very fast, the instrument continuously analyzes the air flowing through a buffer tube (i. e. ambient air by default) and the patient is asked to expire a few times into the tube. Each data file (in the HDF5 open format;  Koziol, 2011)  contains the ion intensities stored as a numerical matrix whose dimensions are the TOF bins (which can be converted to m/z values) and the acquisition time. Two processing software are currently available for PTR-TOF-MS data, the commercial Ionicon Data Analyzer (IDA) released in 2020 based on the algorithms by  Mu ¨ller et al. (2013)  and the opensource PTRwid  (Holzinger, 2015). These software tools allow the analysis of high-resolution, TOF-MS data with the following characteristics: (i) single (or multiple for PTRwid) file analysis, (ii) internal m/z calibration, (iii) untargeted peak detection and deconvolution and (iv) quantification and suggestion of elemental composition.
In particular, the penalized regression on a B-spline basis (P-splines) was used for adaptive temporal modeling  (Eilers and Marx, 1996) , and the coefficients in both m/z and time dimensions were jointly estimated with a two-dimensional (2D) tensor product. This approach enables to estimate all temporal trends without any parametric hypothesis, and to precisely separate peaks in the m/z dimension at each time. The temporal profiles are then used to correct the external contamination, using linear ambient air baseline removal and statistical testing of mean intensity in ambient air versus exhaled breath. The whole workflow from the raw data files up to the table of peak intensities is implemented as the ptairMS package (doi: 10. 18129/B9. bioc. ptairMS) available on Bioconductor  (Gentleman et al.
Software implementation All algorithms were written in R (R Core Team, 2021), and implemented as the ptairMS package (https://doi. org/10. 18129/ B9. bioc. ptairMS), freely available on the Bioconductor platform  (Gentleman et al. , 2004). The companion ptairData experiment package (https://doi. org/10. 18129/B9. bioc. ptairData), also available on Bioconductor, contains the raw files from two datasets from exhaled breath and bacteria culture head space, respectively, as well as the simulated raw data file described in the following Section 3. The main ptairMS methods are described in Supplementary Figure  S2. Briefly, a ptrSet object is built by providing the name of the directory containing the HDF5 raw files. This object is then completed at each step of the processing. In addition, the ptrSet may be updated by adding new raw files to the directory, or by providing new sample metadata.
Results We developed a suite of algorithms for the preprocessing of PTR-TOF-MS data files and the untargeted analysis of exhaled breath from cohorts. Our workflow consists of the following main modules: peak detection, expiratory phases detection, temporal estimation, VOCs quantification and alignment between samples (Supplementary Fig. S2 ). It has been implemented in R as the ptairMS package, which is freely available on the Bioconductor repository. The package includes a Shiny graphical interface to facilitate data management and analysis by the end-user.
VOCs temporal profile classification and comparison to the state of the art on simulated data The performance of the present and previously described software  (Holzinger, 2015; Mu ¨ller et al. , 2013)  were compared using simulated data from PTR-TOF-MS exhaled breath analysis. First, temporal evolutions were extracted from a large in-house database of patient acquisitions (>10 000 expiration and ambient air profiles), after normalization and Savitzky-Golay smoothing. Second, peak clusters were generated around nominal masses 21 to 400, with an asymmetric sech2 peak shape distribution. Peaks parameters were randomly selected for each nominal mass: i. e. the asymmetry coefficient the peak width, the number of overlapping peaks (1 to 3), the peak proximity, the intensity of the highest peak, the ratio of neighboring peaks, and the class of temporal profile ('expiration', 'ambient air' or 'constant').
The exact m/z value of the first peak was selected from the formula library C x H y O z N t used by PTRwid  (Holzinger, 2015). Finally, background noise was added by using a Poisson stochastic process  (Gundlach-Graham et al. , 2018) , with a Gaussian distribution to model the single ion Pulse-Height. The random drawing of each parameters is detailed in the Supplementary Table  S2 , and the code used for the simulation, as well as a representative simulated data file in the HDF5 format, are included in the ptairData R/Bioconductor companion package. Ten simulated files, containing a total of 7028 peaks, were processed with ptairMS (version 0. 1), PTRwid (version 002 IDL) and IDA (version beta 0. 9. 4. 8). ptairMS, which is the only software allowing simultaneous multiple file processing, enabled to process the 10 files in <10 min.