dataesr/scientific-paragraphs-categorization huggingface dataset:
S2A  and B , respectively), thus we used domain structure identification to separate the different types and enzymes in the first round using the NCBI conserved domain database  (31). Then, with careful manual selection and annotation, all DGKδ and alternative transcripts (mainly isoform 2 of DGKη) were removed. DGKδ contains a class II PDZ binding motif that seems highly variable among species, and the second isoform of DGKη does not contain the C-terminal PBM sequence. After selection, 114 sequences were kept from 37 species in total (54 type II and 60 type IV DGKs, Table  S5 ). The classification was then validated by alignment and phylogram creation for all collected sequences, where the different types and enzymes were grouped together (Fig. S3 ). Note that the annotation of the enzymes is not complete and there might be species containing the whole set of enzymes, but we were unable to identify them in the database.
The ProfAff database include not only Boolean lists of interactions of the different DGK PBMs, but also precisely quantified site-specific affinity constants, which allow us to explore the binding preferences by ranking all potential target proteins across the proteome, as well as to precisely quantify the similarities or differences between the various members of the DGK family in multidimensional affinity spaces. Interestingly, both in the fragmentomic holdup (from the ProfAff database) and in nHU, the binding profiles of the two type IV DGKs, DGKζ and ι mediate highly similar affinities with PDZ domains. According to our results, type IV DGKs are highly promiscuous PDZ domain binders with many strong interactions, while type II DGKs do not have any strong binders in particular. We further validated our observations by evolutional conservation analysis, where we found that the PBMs of type II DGKs are not conserved across species, while type IV showed high conservation.
Peptide synthesis HPLC purified (>95% purity) biotinylated 10-mer peptides of the five DGK proteins (DGKδ, DGKη, DGKκ, DGKζ, DGKι, gene names: DGKD, DGKH, DGKK, DGKZ, DGKI, respectively, Uniprot accession codes: Q16760-1, Q86XP1-1, Q5KSL6, Q13574-2, O75912, respectively) were chemically synthesized by an ABI 443A synthesizer with standard Fmoc strategy with the biotin group attached to the N-terminus via a trioxatridecan-succinamicacid (ttds) linker and were purified with high-performance liquid chromatography (LC, >95%purity). Predicted peptide masses were confirmed by MS. Peptide concentrations were determined based on their dry weight.
The 20 most intense peaks from survey scans were selected for further fragmentation in the Linear Ion Trap with an isolation window of 2. 0 Da and fragmented by CID with normalized collision energy of 35% (TOP20CID method). Unassigned and single charged states were excluded from fragmentation. The Ion Target Value for the survey scans (in the Orbitrap) and the MS2 mode (in the Linear Ion Trap) were set to 1E6 and 5E3, respectively, and the maximum injection time was set to 100 ms for both scan modes. Dynamic exclusion was set to 20 s after one repeat count with mass width at ± 10 ppm. The raw LC tandem MS data have been deposited to the ProteomeXchange via the PRIDE database with identifier PXD042853.
Data extraction from ProfAff database and analysis For the comparative analysis of DGK PBM binding affinities we collected the data from the ProfAff database described previously (https://profaff. igbmc. science/) that contains equilibrium binding constants of PDZ-PBM interactions between purified PDZ domains and synthetic 10mer C-terminal peptides  (25). To determine the Jaccard similarities, we used the built-in 'compare' function of the website. For profile and affinity comparisons the retrieved data can be found in Table  S4. To determine correlation between the data sets we used linear regression fitting using only those affinity pairs where both motifs showed measurable affinity for the given PDZ domain in the database. The significance of the correlations was determined using twosided Student's t-test using the null hypothesis that the slope is 0 and the 95% confidence intervals were depicted using GraphPad Prism 7 software.
Collection of DGK sequences across the phylogenetic tree, classification and sequence logos For the evolutionary conservation analysis of PBMs, DGK sequences were manually collected from all major clades of the phylogenetic tree using the Lifemap tool (30) as a guide and the Uniprot database. In total, 176 sequences were collected in the first round from 40 species. Careful manual selection was performed by removing all DGKδ and alternative transcripts without PBM. After the selection, 114 sequences were kept from which 54 identified as type II and 60 as type IV (Table  S5 ). DGKs are classified into different types based on their domain structure, we used NCBI Conserved Domain Database Search  (31)  to identify the different types of DGKs and the different enzymes. For final validation, an alignment was performed using ClustalΩ multiple sequence alignment and a sequence phylogram was created using Jalview version 2.
Supplementary Materials: Fig. S1 : Correlation between the measured apparent affinities in native holdup (nHU-MS) and the calculated composite affinities using the ProfAff database for the DGKζ peptide. Table  S1 : The results of native holdup mass spectrometry measurements with the C-terminal PBMs of DGKs. Table  S2 : The results of affinity purification mass spectrometry measurements with the Cterminal PBMs of DGKζ and DGKκ. Table  S3 : Comparision of pK app (nHU-MS) and enrichment values (AP-MS) of significant partners for DGKζ. Table  S4 : Comparision of PDZ-binding profiles of different DGKs using the ProfAff database. Table  S5 : DGK sequences used in evolutional analyses. Figure 3 Figure 4.
Acknowledgement We are grateful for the help of the cell culture platform at IGBMC in this project. We thank to  Goran Bich  for his kind help in figure generation using the ProFeatMap. Funding BZ was supported by  Fondation pour la Recherche Médicale  (FRM,  SPF202005011975 ). GG was supported by the  Post-doctorants en   France program  of the  Fondation ARC  and by the collaborative post-doctoral grant of the  IGBMC. The project was supported by the  Ligue contre le cancer  ( équipe labellisée 2015  to GT), the  Agence Nationale de la Recherche  ( ANR-18-CE92-0017  to GT and  ANR-18-CE12-0002-01  to HM), and  Fondation Jérôme Lejeune  to HM. This work of the  Interdisciplinary Thematic Institute IMCBio , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS , and  Inserm , was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), and by  SFRI-STRAT ' US  project ( ANR 20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program.
SequestHT (Thermo Fisher Scientific) with Proteome Discoverer 2. 4 software (PD2. 4, Thermo Fisher Scientific) in the human fasta database downloaded from SwissProt. Precursor and fragment mass tolerances were set at 7 ppm and 0. 6 Da respectively, and up to 2 missed cleavages were allowed. Oxidation (M, +15. 995 Da) was set as a variable modification and Carbamidomethylation (C, + 57. 021 Da) as fixed modification. Peptides and proteins were filtered with a false discovery rate (FDR) at 1%. Label-free quantification was based on the extracted ion chromatography intensity of the peptides. All samples were measured in technical triplicate. The measured XIC intensities were normalized on median intensities of the entire dataset to correct for minor loading differences. For statistical tests and enrichment calculations, not detectable intensity values were treated with an imputation method, where missing values were replaced by random values similar to 10% of the lowest intensity values present in the entire dataset.
Unpaired two tailed t-test, assuming equal variance, were performed on obtained log 2 XIC intensities. The hyperbolic binding threshold for nHU-MS was calculated as follows where y is the p-value threshold at the fold change of x, c is an empirically fixed curvature parameter at 1 for nHU-MS and 4 for AP-MS experiments, y 0 is the minimal p-value threshold and x 0 is the minimal fold change value for any given dataset. The minimal p-value was defined at 1. 3-log 10 (p-value) and thus there is a >95% probability that all identified interaction partners are true interaction partners. The minimal fold-change value cutoff was set at 1 σ and was determined by measuring the width of the normal distribution of all measured fold changes in a given experiment. Note that this threshold can only be interpreted for interaction partners with fold change values of -x > x 0 in the case of nHU-MS experiments or x > x0 in the case of AP-MS experiments.
Statistical analysis All statistical analyses were performed with the software R (version 4. 2. 2)  [54]. All models for gene expression were repeated including or excluding the benchmark controls for immune induction (T. urticae inducer), immune suppression (T. evansi suppressor) and uninfested plants. This was mostly to ensure that induction of immune defences occurred as in previous experiments  [13, 46] , but also enabled us to test for differences among T. evansi populations or inbred lines, i. e. in models excluding benchmark controls. We fitted two independent generalized linear mixed models (GLMMs) (i. e. one for each host plant type) with a normal error structure (lmer, lme4 package  [55] ) to investigate whether fecundity (4 dpi) varied among the field and outbred populations on WT and def-1.
The models included population (T. evansi Outbred, VIT, 6M1, QG and PBS) as a fixed explanatory variable. To analyse the transcript accumulation of effector 84, we fitted a GLMM with a gamma distribution and a log link function (glmmTMB package  [56] ). The model included population (T. urticae inducer, T. evansi suppressor, Outbred, VIT, 6M1, QG and PBS) as a fixed explanatory variable. To investigate whether transcript accumulation changed in tomato plants infested with field or outbred populations (4 dpi) and if the levels changed when subsequently infested with the T. urticae inducer population (2 days after re-infestation), GLMMs with a gamma distribution and a log link function (glmmTMB package  [56] ) were fitted for each gene separately (WIPI-IIc, WIPI-IIf ). These models included population, time of gene expression (4 dpi or 2 days after re-infestation) and their interaction as fixed explanatory variables.
glmmTMB models were used instead of glmer to improve model convergence. To evaluate if the fecundity of T. urticae inducer changed following previous infestation with the T. evansi field and outbred populations, we fitted a GLMM assuming a Gamma distribution and a log-link function (lme4 package  [55] ) since normality was not met (Shapiro-Wilk test: p = 0. 008), and variances were not homogeneous. In these models, the pre-infestation population (T. evansi Outbred, VIT, 6M1, QG, PBS, and the inducer and manipulator benchmark populations) was included as a fixed explanatory variable. A GLMM assuming a Gamma distribution and a log-link function (lme4 package  [55] ) was used to investigate whether there was variation in fecundity of the inbred lines on the different host plant types since normality was not met, but here variances were homogeneous.
Inbred line, host plant (i. e. WT or def-1) and their interaction were included in the model as fixed explanatory variables. A similar model was repeated for the 19 inbred lines selected to investigate variation in the induction of plant defences. We calculated broad-sense heritability (H 2 )  [57]  for the fecundity of the inbred lines on WT and def-1 by performing separate generalized mixed linear models for each host type with a Gamma distribution and a log link function (lme4 package  [55] ) with inbred line included in the model as a random explanatory variable. From the summary of the models, we extracted the variance for inbred line, block and residual variance of the model and calculated H 2 as follows: var ðinbred lineÞ = ðvar ðinbred lineÞ þ var ðblockÞ þ var ðresidualsÞ Þ. To determine the significance of H 2 , we compared models, using ANOVA, including and excluding the inbred line random factor.
To analyse if there is variation in the induction of plant defences after infestation with the inbred lines, GLMMs with a gamma distribution and a log link function (glmmTMB package  [56] ) were fitted for each gene separately (WIPI-IIc or WIPI-IIf ). Inbred line was included in the model as a fixed explanatory variable. glmmTMB models were used instead of glmer to improve the convergence of the models. royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 10: 230525 To test how JA defences influence T. evansi fecundity, a genetic correlation between fecundity on WT and def-1 plants was performed using a generalized mixed linear model assuming a Gamma distribution and a log link function (lme4 package  [55] ) with host plant as a fixed variable and inbred line nested within plant (0 + host plant | inbred line).
Acknowledgements. The authors would like to thank  Sara Magalhães 's team at the  University of Lisbon , especially  Cátia Eira  and  Lucie de Sousa , for the help in the creation and maintenance of the populations and lines used in this study and for fruitful discussions throughout the project. Also, we would like to thank  Giacomo Zilio  for helpful discussion. Finally, we would like to thank  João Valeriano-Santos  for his help in creating large functions as a way to prepare the datasets for analysis. This is ISEM contribution number  ISEM 2023-095.
New target system for NUMEN The power released by the intense beam in the NUMEN targets is of the order of 1 W, two orders of magnitude higher than the typical power released in a target for nuclear experiments. Analytical and numerical thermal calculations have been performed with the MATLAB framework, which allows to conclude that a stand-alone target is not able to dissipate this amount of power  [6]. The heat dissipation can be enhanced by depositing the target material on a highly thermally conductive substrate, i. e. High Oriented Pyrolytic Graphite (HOPG), characterised by a thermal conductivity which could range between 1700 and 1950 W m -1 K -1 [7-8]. It has been estimated that a 450 μg/cm 2 -thick HOPG is able to dissipate up to 10 W  [9]. Moreover, a dedicated cooling system has also been designed to help the heat dissipation.
In this procedure, the main source of uncertainty is given by the stopping power values obtained with the SRIM code  [12]. According to the comparisons of SRIM calculations with measured stopping powers  [12] , the uncertainty of the calculations, for the materials studied in the present work, is in the order of 5%. The target non-uniformity can be deduced, as described in  [13] , by comparing the Full Width at Half Maximum (FWHM) of the experimental spectrum with the FWHM of a simulated spectrum, obtained by assuming a uniform target with the measured average thickness and convoluting it using the detector energy resolution as input. In the present analysis, the simulations were performed with the SRIM  [12]  program.
The data collected with the flipper on and off were corrected for the finite polarizing efficiency of the instrument using a procedure detailed in Ref. 8, and the results were combined to yield the coherent and incoherent dynamical structure factors. Background correction was performed by using the measurement on the empty cell. Three temperatures, 175, 220, and 300 K, were investigated. Neutron spin echo experiments  15  were performed using the WASP instrument  16  at the ILL. The WASP detector angle coverage in one position is 90 ○. It can be positioned to cover, for example,.
ARTICLE scitation. org/journal/jcp 5 ○ -95 ○ or 45 ○ -135 ○. The incoming wavelength λ can be chosen between 3 and 10 Å. In these experiments, λ = 4 Å corresponding to an incident energy of 5. 11 meV was used to study a protonated sample (hTHF, C 4 H 8 O, Scharlab TE02252500) at 175 K. The detector was positioned at 5 ○ -95 ○. The thickness of the sample between the walls of the hollow cylinder was 0. 1 mm. The data were deconvolved by division using a resolution measurement on a 1 mm TiZr null alloy, which gives no Bragg peaks but only incoherent scattering. It was in the same hollow cylindrical shape as the sample. Complementary x-ray experiments were performed with a RIGAKU SAXS instrument at the CFM in San Sebastian, Spain, in the temperature range between 170 and 340 K for both dTHF and hTHF samples.
ACKNOWLEDGMENTS We acknowledge the  Science and Technology Facilities Council  ( STFC ) for access to neutron beam time at  ISIS  (Grant No. 10. 5286/ISIS. E. RB2010388 ). We acknowledge the Grant No. PID2021-123438NB-I00  funded by  MCIN/AEI/10. 13039/501100011033  and by " ERDF A way of making Europe ," and the financial support of  Eusko Jaurlaritza , code:  IT1566-22 , and the  IKUR Strategy  under the collaboration agreement between the  Ikerbasque Foundation  and the  Materials Physics Center on behalf of the Department of Education of the Basque Government.
THE DAMAGE PARAMETER SF The expeditious assessment of the residual capacities of a building located in a seismic risk area requires the use of a parameter that is sufficiently reliable and quantifiable on the basis of data available from shared databases. A parameter that has these characteristics is the safety factor, SF, proposed by in  Borri, De Maria & Casaglia (2014). The safety factor is a parameter capable of describing the performance of the structure when subjected to a certain acceleration. This performance is the measure of the loss in safety of the structure after the event, loss quantified through appropriate parameters. In  Borri, De Maria & Casaglia (2014)  two types of safety parameters are distinguished: the global parameter, SF G, which describes the performance of the entire building and the local parameter, SF L , which describes the possible collapse mechanism that can locally affect some points of the building.
The global parameter SF G can be obtained from the geometry of the structure, and from the empirical evaluation of the masonry quality, data easily available from existing databases (Cadastre, municipal archive, vulnerability cards, etc. ) The local parameter, SF L , requires, instead, a little more detailed knowledge of the structure; in fact, its evaluation requires the estimate of the analysis of the loads and the definition of the framework of the decks (more details in section 4). The parameters SF G and SF L are deterministic and associated with a specific building. In Angjieliu, Cardani & Garavaglia (2022), such approach was the basis of the study for a probabilistic application that also showed the possibility of extending the method to the evaluation of the safety of the different types of masonry buildings as the expected acceleration values varied.
The overall behaviour of the building. As already explained, since the typological characteristics of Cascina Cuccagna are recurrent in Lombardy the evaluation of the factors SF G and SF L  (Borri, De Maria & Casaglia 2014) , is performed here for the acceleration interval (a g ) 0. 15g<a g ≤0. 25g, typical of the Italian seismic  zone 2. The design spectrum was constructed for this area and the characteristic acceleration parameters (PGA) were obtained as well as the spectral amplification factor, F 0 , (Table  1 ). On the basis of some parameters, all or in part, easily available from existing databases (the number of floors above ground and their height, the covered surface, the presence in the plant of resistant walls in the x and y direction, their length and thickness, the possible characteristics of the masonry and its specific weight, the presence or absence of cracks and a plausible analysis of the loads) it is possible to measure the loss of the global safety as the values of accelerations imposed vary and construct the consequent curves of fragility.
ACKNOWLEDGMENTS The following research is funded by the  CARIPLO Foundation, Milan, Italy , in the context of the 2017 Art and  Culture-Cultural Heritage at Risk Calls , Thanks to the  ACCC , Hydea s. p. a. and Lares s. r. l. for the collaboration undertaken in this project. Thanks also to the  RELUIS Group  and in particular to  Professor Giuliana Cardani  with whom I started developing this approach.
Acknowledgments We thank  S. Sethi  for discussions. The work of SC received funding under the  Framework Program for Research and  " Horizon 2020 " innovation under the  Marie Skłodowska-Curie  grant agreement n • 945298. The work of AG and DK is supported in part by the  BSF  (grant number  2018068 ). The work of AG is supported in part by the  ISF  (grant number  256/22 ). The work of DK is supported in part by  DOE  grant  DE-SC0009924. This work was supported in part by the  FACCTS Program  at the  University of Chicago. Open Access. This article is distributed under the terms of the Creative Commons Attribution License ( CC-BY 4. 0 ), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited. SCOAP 3 supports the goals of the  International Year of Basic Sciences for Sustainable Development.
Background There is a paucity of studies evaluating perioperative systemic chemotherapy in conjunction with cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) in patients with colorectal cancer peritoneal metastases (CRCPM). The aim was to evaluate neoadjuvant and/or adjuvant systemic therapy in CRCPM. Methods Patients with CRCPM from 39 treatment centres globally from January 1, 1991, to December 31, 2018, who underwent CRS+HIPEC were identified and stratified according to neoadjuvant/adjuvant use. Crude data analysis, propensity score matching (PSM) and Cox-proportional hazard modelling was performed. Findings Of 2093 patients, 1613 were included in neoadjuvant crude evaluation with 708 in the PSM cohort (354 patients/arm). In the adjuvant evaluation, 1176 patients were included in the crude cohort with 778 in the PSM cohort (389 patients/arm).
Implications of all the available evidence The vast usage of neoadjuvant therapy prior to cytoreductive surgery and HIPEC treatment of colorectal peritoneal metastases (even as part of some countries standard treatment algorithms) is brought into serious questioning. Reassessment of neoadjuvant therapy is warranted pending future randomized trials, while adjuvant therapy continues to hold a potential clinical benefit. are described in the Supplementary Methods section. Briefly, this PSOGI registry included all patients suffering from CRCPM from each respective centre's HIPEC registries. Only patients having undergone CRS+HIPEC treatment were included in the PSOGI compiled registry. The retrospective capture of data for this study was approved by each centres' national registries' respective ethical review boards.
All data was anonymised prior to registry synchronisation so informed consent was not required. Certain variables were deemed completely necessary and not possible to estimate in a reasonable manner. All patients with missing PCI (peritoneal cancer index), CC scores (completeness of cytoreduction), and specified HIPEC treatment drug regimen were removed from the 2093 base cohort. Demographics, clinicopathological variables, operating variables, postoperative morbidity, overall survival, and recurrence-free survival was retrieved from the PSOGI compiled registry. This study aimed to evaluate neoadjuvant therapy use and adjuvant therapy use through two separate propensity score matched analyses. For the neoadjuvant therapy evaluation, all patients with missing data on neoadjuvant therapy were removed (Fig.