dataesr/scientific-paragraphs-categorization huggingface dataset:
This process is studied by atomic scale simulations using the EAM potential for bcc Fe proposed in  [173]. Four symmetrical tilt grain boundaries were selected, two with an axis along  [110]  and two along  [100]. Segregation energy maps for single vacancies and activation energies for single and di-vacancies, as a function of the strain perpendicular to the interface, were calculated. Segregation is strong, but tends to decrease with increasing strain. Vacancies and di-   b ) highlights a significant defect size effect. Namely, the plastic strain ratio N irr /N 00dpa is significantly lower in presence of larger defects, regardless of the tested defect number densities. vacancies are mobile down to room temperature, with a tendency for one dimensional diffusion along the tilt axis. In order to go beyond divacancies and study clusters of larger sizes, the method call "Smart Darting" was adapted to an available Metropolis Monte Carlo (MMC) code for vacancies at grain boundaries  [174].
The code simulates the (N, V,T) ensemble using the same interatomic potential as in the above mapping calculations. The strain is initially applied to the system by elongating the box length in the direction perpendicular to the interface (one dimensional tension). The volume is decomposed into Voronoi cells, based on the elongated body centered lattice of Fe and the grain boundary structure of minimal energy. A microscopic configuration is defined by a vector containing the cell occupancies which are set to 0 when the cell is empty (vacancy) and to one when it contains a particle. In addition, a displacement vector is associared with every particle to define its position with respect to the lattice node inside its cell. Therefore, the configuration space, which is sampled by MMC, is a vector of length M (M3N) of occupancies, coupled to vectors of three dimensional displacements confined to the Voronoi cells.
Nevertheless, the extent to which negative selection affects human disease-causing genes, and the factors determining its strength, remain largely unknown, particularly because our knowledge of the severity, mode, and mechanism of inheritance of the corresponding human diseases remains incomplete  (3, (6) (7) (8). The strength of negative selection at a given gene has been traditionally approximated by comparing the coding sequence of the gene in a given species with that of one or several closely related species; it depends on the proportion of amino acid changes that have accumulated during evolution  (9) (10) (11). With the advent of high-throughput sequencing, intraspecies metrics have been developed, based on the comparison of the probability of predicted loss-of-function (pLOF) mutations for a gene under a random model with the frequency of pLOF mutations observed in population databases  (5, 12, 13) , which capture the speciesspecific evolution of genes.
in Man (hOMIM) database, a previous study elegantly showed that most human genes for which mutations cause highly penetrant diseases, including autosomal dominant (AD) diseases in particular, evolve under stronger negative selection than genes associated with complex disorders  (6). However, other studies based on OMIM genes have reported conflicting results  (3, (14) (15) (16) (17) , probably due to the incompleteness and heterogeneity of the datasets used. Moreover, no study has yet addressed this problem with intraspecies metrics, even though it has been suggested that the choice of the reference species for interspecies metrics contributes to discrepancies across studies  (6). We aimed to improve the identification of the drivers of negative selection acting on human disease-causing genes, by developing a negative selection score combining several informative intraspecies and interspecies statistics, focusing on inborn errors of immunity (IEI).
Mendelian Disease-Causing Genes Show Stronger Negative Selection Scores than Background Genes. We sought to replicate previous observations based on the hOMIM database  (6). We compared the CoNeS of 833 autosomal and 66 X-linked genes annotated by hOMIM as Mendelian disease causing, to that of 15,219 "autosomal background" (AB) and 650 "X background" (XB) genes, respectively, these background genes not being known to be essential [i. e. , indispensable for the survival  (26) ] or to underlie any severe genetic disorders (see Methods for details). The CoNeS was significantly lower for hOMIM autosomal genes than for the AB group (Wilcoxon one-tailed test, P = 5. 34 × 10 -16 ; resampling test accounting for the size of coding genes, P < 10 -5 ; Fig. 1B ), indicating that the hOMIM genes were subject to stronger selection constraints.
5% and 75. 0%, respectively), whereas AD diseases are typically associated with LOIP genes (80. 9%) (χ 2 test, P = 4. 56 × 10 -17 ; Fig. 3A ). This observation suggests that variants underlying AR IEI decrease fitness more than variants underlying AD IEI, consistent with the negative relationship observed between fitness and the dominance coefficient in Drosophila, yeast, and thale cress  (28) (29) (30). However, we caution this result, because the observed enrichment may also be due to a bias in the IEI database (e. g. , severe dominant diseases are more difficult to discover and study). Interestingly, we observed that the IEI AR genes of the EOHP group show significantly lower negative selection scores than those of the LOIP group (medians, -0. 0742 and 0. 413, respectively; Wilcoxon one-tailed test, P = 1.
S5 ), and DOMINO and LOEUF on IEND genes (SI Appendix, Table  S21 ). We improved the performance of CoNeS by removing some individual scores (SI Appendix, Table  S22 ), but its performance did not reach that of DOMINO on either hOMIM or IEND genes. We then trained a supervised model to distinguish between AR and AD genes on a dataset combining hOMIM, IEI, and IEND that included as features both DOMINO and the seven individual selection scores previously described. We evaluated its performance through leave-one-out cross-validation. Our supervised metric, which we denote as SCoNeS (supervised CoNeS), is the predicted probability of a given gene being AR. We chose a random forest classifier but also tested SVM (with Euclidian, polynomial, and radial kernels) and k-nearest neighbors (Methods and SI Appendix, Table  S23 ).
Importantly, we showed that the CoNeS score was significantly lower at loci underlying recessive IEI with a high degree of clinical severity than for other genes underlying recessive IEI. This result contrasts with the nonsignificant difference observed in a previous study based on highly penetrant OMIM genes  (6)  but confirms a more recent study based on the Bayesian estimation of the selection coefficient of heterozygotes for HI genes  (8). This discrepancy probably reflects differences in power and in the annotations of disease databases. Together, these results indicate that the effects of negative selection on genetic variation depend on both the mode of inheritance and the clinical outcome of human diseases. We found stronger evidence for negative selection at genes underlying inborn errors that are dominant by HI, relative to genes that have other mechanisms of dominance.
This information is usually leveraged to increase the power of linkage analyses. We suggest that candidate genes can be further prioritized based on their mode of inheritance predicted from negative selection scores. Our study shows that, although all scores are informative for this, some metrics do perform differently. For example, lofTool and evoTol are the only scores able to  distinguish hOMIM AR disease-causing genes from AB genes. One of the reasons could be their internal use of FATHMM  (42) , which includes known disease mutations as a training set  (43)  and may overfit well-known disease genes included in hOMIM. An overfitted gene score would increase error to predict the mode of inheritance of novel disease-causing genes, whose mutation patterns do not fit existing databases. CoNeS and SCoNeS show two paths to avoid overfitting: unsupervised classification for CoNeS and supervised classification for SCoNeS by leave-one-out cross-validation.
Both scores perform similarly to, or better than, individual scores on the independent datasets of genes underlying IEI and IEND, indicating good performance. Two related limitations of our study are the assumption that all mutations at a given locus cause diseases with the same severity and mode of inheritance, and that the strength of negative selection is equal all along a given gene. Several genes, such as C3 (44) and STAT1 (45), were found to be under strong negative selection (CoNeS of -1. 64 and -1. 84, respectively), but to be associated with several diseases of different severities, modes of inheritance, and/or incomplete penetrance. The additive effects of multiple small constraints on most of the sequence result in strong overall constraints on these genes. Conversely, a small number of genes are under weak negative selection, whereas their variants underlie severe diseases.
ACKNOWLEDGMENTS. This work was supported in part by the  Rockefeller University , the  St. Giles Foundation ,  Institut National de la Santé et de la Recherche Médicale ,  Paris Descartes University , the  National Center for Research Resources  and the  National Center for Advancing Sciences, NIH Clinical and Translational Science Award program  (Grant  UL1TR001866 ), the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  (Grant  ANR-10-LABX-62-IBEID ), and the  French National Research Agency (ANR)  (Grants  ANR-10-IAHU-01 ,  ANR-15-CE17-0014 ). The laboratory of L. Q. -M. and E. P. is supported by the  Institut Pasteur , the  Collège de France , the  Investissement d'Avenir program ,  Laboratoires d'Excellence "Milieu Intérieur" (Grant  ANR-10-LABX-69-01 ),  Fondation pour la Recherche Médicale  (Grant FRM  DEQ20180339214 ),  Fondation Allianz-Institut de France , and  Fondation de France.
Methods Gene and Disease Annotations. The lists of hOMIM and IEI genes and their modes of inheritance were obtained from previous publications  (6, 54). Each IEI gene was manually annotated for severity and (when AD) for mode of dominance (SI Appendix, supplementary file 1). The IEND gene list was assembled from the SysID reference database (39) (SI Appendix, supplementary file 1). The AB and XB gene groups included all human genes not listed in the OMIM, IEI, or IEND lists or in the list of essential mouse and human genes defined in a previous study  (26). Computation of the Scores. EvoTol  (22) , lofTool  (13) , and SIS (24) statistics were downloaded from the corresponding publications. Values for pLI  (12)  and LOEUF  (23)  were obtained from gnomAD, version 2. 1. For RVIS  (5) , we downloaded the values calculated with ExAC v2 from the RVIS website.
We used the knn function from the class package for k-nearest neighbors. When a gene was present in more than one dataset among IEI, IEND, and hOMIM, we annotated the gene as AD (respectively AR) if the gene was considered AD (respectively AR) in all the datasets in which it was present. Otherwise, we annotated the gene as AR/ AD. We adjusted the parameters of the SVMs using an internal crossvalidation loop (function tune. svm) and chose the parameters of the best model to test the performance. The performance is displayed with a precision-recall curve (function pr. curve from PRROC package). Data Availability. All study data are included in the article and SI Appendix.
Second, preoperative planning is not possible with the ROSA robot's software, which we think would save a lot of time. Third, there is no way to merge the data from CT and magnetic resonance imaging (as is possible for the brain version). Finally, the system lacks the ability to simulate the optimal sagittal balance, which we believe would be very valuable in giving the surgeon an idea of how much lordosis has to be restored or at least how much of the sagittal balance will be improved at the operation site. The initial challenges associated with the use of the robotic system include the application of the toothed cannula for drilling (to avoid the skiving effect). We, therefore, replaced it with a nontoothed cannula, with modification of the drilling maneuver (a very high-speed in-and-out movement, several times until penetration of the whole pedicle).
This work deals with the coupling between a periodic homogenization procedure, and a damage process occurring in a Representative Volume Element (RVE) of inclusion composite materials. We mainly seek, on the one hand to determine the effective mechanical properties according to the different volume fractions, and forms of inclusions for a composite with inclusions at the macroscopic level. On the other hand, to explore the rupture mechanisms that can take place at the microstructure level. To do this; the first step is to propose a periodic homogenization procedure, to predict the homogenized mechanical characteristics of an inclusion composite. This homogenization procedure is applied to the theory based on finite element analysis, by the Abaqus calculation code. The inclusions are modeled by a random object modeler, and the periodic homogenization method is implemented by python scripts.
This document is organized as follows. Section 2 discusses a method of solving a periodic homogenization problem using the principles of strain energy equivalence, in a coordinated way with finite element analysis. It gives the modifications required in the Abaqus calculation code by python srcipts, in order to introduce periodic boundary conditions at the micro scale. This procedure is performed by the introduction of additional degrees of freedom supporting the components of macroscopic strains. In section 3 are presented several damage models controlled by an equivalent strain tensor in the principal coordinate system. The inclusions are supposed to remain elastic, while the matrix is supposed to obey the actual behavior of the material constituting, it with damage at the micro level. Section 4 presents detailed results obtained by several tests of this selected periodic homogenization method, which will be compared with an estimated homogenization method (Mori-Tanaka model).
These results can be used to verify the implementation of the simplified approach proposed in the Abaqus calculation code. Finally, Section 5 provides applications regarding the introduction of damage into the homogenization problem. It is a question of using a local formulation for the evolution of the damage at the level of the microstructure, using the subroutine (Umat) implemented in the Fortran language on the Abaqus calculation code. This proposed method makes it possible to simulate the response of the matrix behavior, on several RVEs requested in uniaxial traction.
The regions on the boundaries of the RVE are selected and numbered as shown in Fig. 2. Additional equations should be introduced for periodic boundary conditions at the micro scale, where the macroscopic strain matrix has components  = ij with i, j = x, y, z. In order to include the macroscopic deformation of component , , , , , respectively as follows:       - - - - - - = = = = = = 1 1 1 1 1 1 x xy xz y yz z ( 1) ( 2 ) ( 3) ( 4 ) ( 5) ( 6) ; ; ; ; ; PR PR PR PR PR PR U U U U U U (17) Figure  2 : Representation and periodic boundary conditions of an RVE. We create the additional equations of all faces, edges and corners with the python command in order to apply the periodic boundary conditions in the Abaqus calculation code. And we apply these periodic boundary conditions at the micro scale.
GENERAL IMPLEMENTATION FLOWCHART ON THE ABAQUS CALCULATION CODE he proposed periodic homogenization method has been implemented numerically using a Python script, to invoke the solution in the Abaqus finite element computational code. The iterative algorithm is presented in Fig. 3. At each incremental step of the analysis, the material properties subroutine (Umat), where the constitutive law was used to carry out the communication between the Python code, and the Abaqus solver. The Python code modifies the boundary conditions to an Abaqus input file, applied to the RVE.
Introduction of damage into the problem of homogenization We implemented a Mazars damage model (  [29] ,  [30] ) for spheroidal and ellipsoidal shaped inclusions, and the model of  Bouafia et al. ([31] ,  [32] ) for cylindrical shaped inclusions in the Abaqus calculation code by the use of a subroutine (Umat). Calculations on a RVE containing 10 inclusions with type localization conditions (PBC), allowed us to study the influence of the variation of the volume fraction of inclusions on the mechanical characteristics. Then, once the effective mechanical properties of the composite were known, a damage model was introduced to study the response of the microstructure. The effective mechanical properties of the representative volume element ( )  , eff eff E are those obtained from the periodic homogenization method, corresponding to each volume fraction.
This paper explores the relationship between the industry collaborations of grant applicant teams and the outcomes of a multistage grant evaluation process. We studied this relationship by focusing on two possible channels of impact of industry engagement-team diversity (or the diversity effect) and prior collaboration experience (or the experience effect)-and examined their influence on the evaluators' decision by using the proxies of direct industry engagement (i. e. , the involvement of a companyaffiliated researcher in the grant applicant team) and indirect industry engagement (i. e. , joint publications with a company-affiliated researcher prior to the grant application), respectively. We analyzed data extracted from the application and reviewed materials of a multidisciplinary, pan-European research funding scheme-European Collaborative Research (EUROCORES)-for the period 2002-2010 and conducted an empirical investigation of its three consecutive grant evaluation stages at the team level.
1  Nonetheless, as  Banal-Estañol et al. (2019a)  argue, even in the absence of such an explicit aim, the evaluators may have been implicitly inclined to foster industry collaborations. The rest of the paper is structured as follows. The Grant Evaluation Process and Industry Engagement section presents a general framework in which the grant peer-review process is outlined, the diversity and experience effects and their respective proxies of direct and indirect industry engagements are described, and the EUROCORES funding scheme is explained. The Data and Methodology section describes the data, variables, and the methodology used. The Econometric Results section presents the empirical results for the three consecutive evaluation stages, and the Discussion and Conclusion section concludes by discussing these outcomes.
EUROCORES Grant Evaluation and Allocation Process European Collaborative Research (EUROCORES) was a broad, complex pan-European grant scheme aimed at supporting collaborative and interdisciplinary research by bringing together national research funding and performing organizations. The program facilitated multinational research collaboration, while funding remained at the national level. EUROCORES was initiated by the ESF in 2001 and included in the European Commission's Sixth Framework Programme (FP6) between 2003 and 2009. Networking and science support activities were later financed by the ESF's member organizations until the EUROCORES scheme was terminated in 2015. The evaluation of Collaborative Research Project (CRP) proposals in the EUROCORES scheme was a multistage process and included the establishment of an independent, international Expert Panel (see Figure  1 ).
Information concerning the applicants' direct engagement with industry, proxied here by an ongoing project partnership in a CRP with a company-affiliated researcher, can therefore be obtained directly from the EUROCORES database. The data also reported the decisions of and the scores given by the evaluators at the corresponding stage. Bibliometric data on research publications were extracted from the Web of Science (WoS) Core Collection, a comprehensive, international multidisciplinary bibliographic database. As of 2020, the WoS Core Collection contained over 21,100 international peer-reviewed journals (including open access journals) in 254 WoS journal subject categories, as well as books and conference proceedings. As with its similar counterparts, the WoS database presents a number of drawbacks, including the overrepresentation of Englishlanguage journals to the detriment of other languages and the underrepresentation of publications in the Social Sciences and Arts and Humanities  (Mongeon and Paul-Hus, 2016; Vera-Baceta et al.
, 2019). Nevertheless, the WoS database continues to be one of the main sources for bibliometric analyses. The bibliographic record of each publication contains, among other details, the authors' institutional affiliation, which enables us to identify joint publications with industry (or company) affiliated researchers. To identify coauthors affiliated to a company, we ran a computer code that was able to detect abbreviations or extensions related to a private entity, such as, "ltd. , Inc. , GmbH, SRL, public limited company, limited liability company". Through this coauthorship relation, we were able to detect the applicants' indirect engagement with industry prior to the grant application. In our database, 48,458 out of a total of 707,158 applicant publications include at least one coauthor with a private company affiliation (i.
Control Variables We included several control variables measured at the applicant team-level and evaluator-level and which may affect the outcome of the evaluation process (see Table  1  for variable descriptions). Bibliometric indicators are constructed through the individual data retrieved from the WoS database. We measured Productivity by the number of total publications of the team members normalized by the team size (in logs). Star scientist indicates whether the team includes at least one of the most cited researchers, corresponding to the top 1% of a specific discipline and the year of citation distribution. To measure Research diversity, we used the Blau index, given by 1s i 2 , where s i is the share of publications in a WoS subject category i, calculated with respect to other team members.
ECONOMETRIC RESULTS We investigated the impacts of team diversity and prior collaboration experience of EUROCORES applicants on the decisions of the evaluators at consecutive stages of the evaluation process. The results of the estimations are reported in Tables  8, 9 , 10. We used different model specifications by introducing variables related to the applicant team and the evaluators stepwise. All regressions were estimated with heteroscedasticity-consistent standard errors clustered at the program-level and included year, scientific field and program fixed effects. As a cautionary note, the following results allowed us to observe the correlation between the dependent and independent variables; they cannot be confidently interpreted as causal.
To the best of our knowledge, the only study to date that has attempted to examine the effects of industry collaboration is  Banal-Estañol et al. 's (2019a) , and here in the present study, only in part, as it does not constitute the primary focus of the authors' analysis. Our study, therefore, builds on and extends this earlier investigation by incorporating the dimension of indirect engagement in an effort to account for collaborations established in the past. Another important contribution made by the present study is the fact that our original and particularly rich database has allowed us to observe a multistage, multidisciplinary evaluation process at a more granular level within a pan-European grant scheme. Previous studies have tended to concentrate their research on a single country and a single discipline and have restricted themselves to a single stage in an evaluation process.