dataesr/scientific-paragraphs-categorization huggingface dataset:
Pathway-informed Deep Learning Model (PiDeeL) We introduce our model, PiDeeL, which is a pathway-informed multi-layer perceptron specially designed to exploit the biomarker metabolite level information on the input layer. We refer to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to parse the metabolite-to-pathway mapping and construct a 37 by 138 matrix (named P I matrix where the value in the i th row and j th column of matrix P I matrix is 1 if the metabolite i appears in the pathway j, and 0 otherwise. Our model architecture is as follows: P L (138) = F C (138) weights P I matrix (8) P A (1:N ) = ReLU (P L (138) ( M (1:N ) ) (9) X (1:N ) = ReLU (F C (64) (P A (1:N ) )) (10) h (1:N ) = F C (1) (X (1:N ) ) (11) where P L (138) represents the weights of the pathway-informed layer, P A (1:N ) represents the activations of the first hidden layer (we name it pathway-activation), X represents the output of hidden layers, h (i) represents the predicted risk score, F C (•) represents a fully-connected layer with (•) neurons and ReLU represents the rectified linear unit.
For each task and model type, we adopt a 5-fold cross-validation routine and repeat it 3 times with different seeds (i. e. 15 iterations). Before each iteration, we shuffle and divide our dataset to training, validation and test splits such that no sample or patient are shared across folds. The results discussed hereafter are with respect to the predictive capability of models on the test split. During iteration i of the cross-validation setup, Cox-PH and RSF models were tested on the fold i, validated on fold (i + 1)mod5, and trained on the remaining 3 folds. We re-trained the selected configuration on the union of training and validation splits to get stronger baseline models. For all deep learning models, re-training steps were not performed due to the presence of early-stopping dynamics.
The surgeon can be more conservative in this case compared to being informed with only a prediction of an aggressive tumor. Similarly, our model correctly predicts low survival for a patient with a benign tumor who lived only 43 days after surgery. We also show that using a multitask learning architecture that basically combines two architectures and predicts both labels simultaneously further increases the performance for both tasks and helps the model to generalize better. As the two tasks are related, the model requires a relatively smaller number of samples to generalize better to unseen samples compared to individual single task models. Our study is bottlenecked by the automated metabolite quantification model which has shown promising performance and generalizability. Yet, this component consists of individual multilayered perceptrons for 37 metabolites for which a training dataset exists.
Acknowledgements This work was supported by grants from  BPI France  ( ExtempoRMN Project ),  Hôpitaux Universitaires de Strasbourg, Bruker BioSpin ,  Univ. de Strasbourg  and the  Centre National de la Recherche Scientifique ; also by  TUBA GEBIP, Bilim Akademisi BAGEP and TUSEB Research Incentive  awards to AEC.
81E-07), antimicrobial response (P-Value = 5. 21E-04). The untargeted metabolomics approach detected 474 metabolites through positive (291 metabolites) and negative (183 metabolites) ionization modes. The proteomics statistical analysis was also applied to metabolomics (Fig. 2B ). In the supercentenarians, 62 metabolites were up-regulated; while 17 metabolites were down-regulated. Enrichment analysis was mainly focused on the up-regulated metabolites, with the lower p-value for the biosynthesis of unsaturated fatty acids (P-Value = 5. 96E-4). Other metabolic pathways such as linoleic acid, purine, and ether lipid metabolisms were also enriched. Downregulated metabolites presented an enrichment for the primary bile acid biosynthesis pathway. The proteinmetabolite network represents the omics dataset's integration (Fig.
Participants' recruitment and Sample Collection Three Brazilian unvaccinated supercentenarians who recovered from Covid-19 were contacted by our Human Genome and Stem Cell Research Center (HUG-CELL) research group following their report in national media: a 114-years-old woman (ID 01) and two men aged 111 (ID 02) and 110 (ID 03) years-old, respectively, at blood collection time. To our knowledge, these were the longest-lived people who recovered from Covid-19 in South America before the vaccination started and new SARS-CoV-2 variants emerged. RT-PCR tests confirmed the previous diagnosis of Covid-19, and all relevant clinical data related to the disease episode and comorbidities were collected from clinical reports and interviews. Baseline characteristics of the three supercentenarians are shown in Table  3.
Genomic assays Whole-exome sequencing (WES) was performed in peripheral blood DNA with the Illumina NovaSeq platform at HUG-CELL facilities. Sequencing data were analyzed following bwa-mem and GATK Best Practices workflow, quality control, and annotation were performed as previously described  [36]. HLA genes were realigned and called using hla-mapper  [78] , and the pipeline was described elsewhere  [79].
An accurate description of interactions between thermal neutrons (below 4 eV) and materials is key to simulate the transport of neutrons in a wide range of applications such as criticality-safety, reactor physics, compact accelerator-driven neutron sources, radiological shielding or nuclear instrumentation, just to name a few. While the Monte Carlo transport code Geant4 was initially developed to simulate particle physics experiments, its use has spread to neutronics applications, requiring evaluated cross-sections for neutrons and gammas between 0 and 20 MeV (the so-called neutron High Precision -HP-package), as well as a proper offline or on-the-flight treatment of these cross-sections. In this paper we will point out limitations affecting the Geant4 (version 10. 07. p01) thermal neutron treatment and associated nuclear data libraries, by using comparisons with the reference Monte Carlo neutron transport code Tripoli-4 ® , version 11, and we will present the results of various modifications of the Geant4 Neutron-HP package, required to overcome these limitations.
To this aim, the nuclear industry and nuclear engineering laboratories have been developing and using for decades dedicated Monte Carlo neutron transport codes, relying on evaluated nuclear data and subsequent nuclear data processing and treatment. Among these reference codes for neutronics, MNCP (version 5  [6]  and 6  [7] ), SCALE  [8] , SERPENT  [9] , MORET (version 5  [10]  and 6  [11] ) or Tripoli-4 ®  [12]  are validated and qualified using inter-code comparisons (see for instance  [13] ) and large qualification data bases (using either integral or differential experiments). Combined with a large user community, these codes hence present coherent results in their respective qualification domains. In a different context, the open-source Monte Carlo code Geant4  [14, 15]  was originally designed for high-energy physics and was extended a few years ago to transport low energy neutrons (below 20 MeV) using the Neutron High Precision -HPpackage.
Improvements are continuously made to this package to increase its precision such as described in  [16, 17, 18]. However, large discrepancies still remain when Geant4 is compared to neutronics reference codes and experiments. A quick review of the Neutron-HP package shows various shortcomings of the code, related either to the handling of resonances in general (in the unresolved and resolved regions) or to the handling of the free gas approximation itself  [19]  and of TSL data  [18]  with potential consequences in the epithermal and thermal energy domains for all materials. While being out of the scope of this paper, it might be good to know that using Geant4 in a context of reactor physics (with heavy structure materials and fissile media) might require ad-hoc developments for a proper handling of resonances, specifically in the unresolved resonance region (use of the so-called probability tables instead of averaged cross sections which are used at present) and in the resolved resonance region (use of the Doppler Broadening Rejection Correction (DBRC) method  [26]  which should lead to sensitive improvements of the elastic scattering kernel estimation close to neutron resonances).
This present works therefore focuses on the inconsistencies related to the free gas approximation and TSL data, and proposes developments within the Geant4 Neutron-HP package to correct them. It is organized as follows: in Section 2 the methodology is introduced, based on the use of Tripoli-4 ® as a reference code and on the definition and use of two simple neutron transport benchmarks (the homogeneous sphere and the thin cylinder). In Section 3, the Geant4 free gas approximation implementation is revised with the "Sampling of the Velocity of the Target nucleus" (SVT) algorithm  [20]  used in Tripoli-4 ®. Its description and impact on Geant4 predictions are evaluated and compared to Tripoli-4 ®. In Section 4 the corrections applied to the Geant4 TSL data treatment are presented along with their impact on computing time.
Methods: comparing Geant4 to Tripoli-4 ® on two benchmarks Tripoli-4 ® is a continuous-energy radiation transport Monte Carlo code developed since the mid-1990s at CEA-Saclay and devoted to shielding, reactor physics with depletion, criticality-safety and nuclear instrumentation for both fission and fusion systems. It is used as a reference code by the main French nuclear companies  [12]  and benefits from a very large verification and validation database gathering more than 1000 experimental benchmarks (ICSBEP benchmarks  [21] , reactor physics experiments  [22] ) as well as many comparisons to the US reference Monte Carlo code MCNP (see for instance  [23] ). It is qualified for various applications (criticality-safety, burn-up credit, reactor physics, etc. ), closely following the recommendations of the French nuclear safety authority for nuclear safety demonstrations  [24].
A coherent elastic process will lead to peaks in the cross-section if the Bragg rule is satisfied. TSL data are provided through evaluated nuclear data libraries in the ENDF-6 format, and are not directly usable by neutron transport codes. In fact, they are represented as tabulated scattering kernels S(α,β). Therefore the kernels S(α,β) need to be processed to get double differential cross-sections d 2 σ dΩdE with the NJOY code  [32]  for example. Often the coherent inelastic process is neglected during the construction of the S(α,β) , this is called the incoherent approximation (in certain conditions this could fail  [5] ). This approximation is used in the LEAPR module of NJOY. Therefore the neutron/medium interactions are split in coherent elastic, incoherent elastic and incoherent inelastic processes.
New thermal scattering kernels: processing tool and validation From 2011 and up to now, only ENDF/B-VII. 1 TSL data were available in Geant4, even if ENDF/B-VIII. 0 or JEFF-3. 3 have released updated and new TSL evaluations in the past years. In order to have access to the latest TSL evaluations and to study the impact of nuclear data processing parameters on Monte Carlo simulations and in particular in Geant4, a processing tool has been developed and validated against Tripoli-4 ®. In this work NJOY-2016  [32]  is used to process S(α,β). The code is available on the following GitLab repository  [34].
Processing tool A processing tool based on NJOY allowing to convert S(α,β) from ENDF file to Geant4 TSL files has been written. The first step is to generate the double differential cross-section and the second is to format the NJOY output file into Geant4 TSL files. With NJOY the nuclear cross-section is Doppler broadened with the BROADR module at the processed temperature. This ensures the continuity between nuclear cross-sections (above 4 eV) and thermal scattering cross-sections (below 4 eV) at the energy cut-off equal to 4 eV in Geant4 (Tripoli-4 ® 's cut-off is 4. 95 eV). Then the THERMR module is used to transform TSL data with user specifications related to the energy reconstruction tolerance (tol) and the number of equi-probable scattering cosine angle (N µ ). The crosssection binning is built by NJOY to allow linear interpolation between two points within the tolerance tol.
Hartling et al. [17]  have shown that the parameters tol=0. 001 and N µ ≥20 are required to accurately predict for example neutron transmission coefficients (see Fig. 7  and 8 in  [17] ). Following their recommendations, in this work tol=0. 001 and N µ =32 are chosen. The THERMR ouput file is in a PENDF format which is transformed to be processed by Geant4. Schematically the total cross-section given by the MF=3 file are placed in the CrossSection directories (Coherent, Incoherent, Inelastic), while final states from MF=6 file are placed in the FS directories (Incoherent, Inelastic). The exception is made for the coherent elastic final state which are directly taken from the MF=7 (MT=2) evaluated data file which is processed by NJOY, because it already represents final states. Tripoli-4 ® has been chosen as a reference code partly because it also uses the THERMR output file to deal with TSL data.
Flux [n/cm2/s] T4 @77K G4 @77K T4 @196K G4 @196K T4 @294K G4 @294K T4 @77K G4 @77K T4 @196K G4 @196K T4 @294K G4 @294K T4 @77K G4 @77K T4 @196K G4 @196K T4 @294K G4 @294K T4 @77K G4 @77K T4 @196K G4 @196K T4 @294K G4 @294K T4 @77K G4 @77K T4 @196K G4 @196K T4 @294K G4 @294K T4 @77K G4 @77K T4 @196K G4 @196K T4 @294K G4 @294K 10 -  With this processing tool ENDF/B-VIII. 0 TSL data have been integrated into Geant4 which allows having access to additional materials (e. g. YH 2 , ice or SiC), to more material temperatures (e. g. at 77 K, 196 K, etc for CH 2 with ENDF-BVIII. 0 instead of only 296 K with ENDF-BVII. 1) and to newer TSL data compare to ENDF/B-VII. 1 library. The same comparisons have been made between Geant4 and Tripoli-4 ® for ENDF/B-VIII. 0 and are presented in  Figures 7, 8 and 9.
The continuity between the two domains is ensured by NJOY and there is no problem in Tripoli-4 ®. This shows that TSL data in Geant4 is not properly handled in the G4ParticleHPThermalScattering class. In fact when a compound material (e. g. CH 2 ) is made of one element described by TSL data (e. g. H in CH 2 ) and the other by the free gas approximation (e. g. C in CH 2 ), if the neutron interacts with the nucleus described by the free gas approximation (e. g. C in CH 2 ), the G4ParticleHPElastic class is called. However in this class, the target nucleus (e. g. H or C) is sampled again even if the target is already known (e. g. C). This modifies the interaction probability in giving to the nucleus described by TSL data more chance to be the target (e. g. H is more sampled than C in CH 2 ).
Then the neutron final state at T mat is computed by a linear temperature interpolation. This is done even if T mat = T i. The main problem is that the temperature interpolation should be performed stochastically  [33] , i. e. the temperature T i or T i+1 should be randomly selected and then the final state should be computed at the sampled temperature. Physically this is the right method to use because the quantity that can only be interpolated is the phonon spectrum from which NJOY makes the convolution process to get TSL data, not the outgoing neutron characteristics. In addition to dealing with the TSL data in the right way, using a stochastic temperature interpolation speeds up the code by a factor of two since now only one final state is sampled instead of two at each simulation step.
In the future the DBRC treatment  [26]  of the neutron elastic scattering and the use of probability table to describe cross-sections in the unresolved resonance region should be taken into account in Geant4 to be fully competitive with "state-of-the-art" neutron transport codes. A TSL nuclear data processing tool has also been developed and validated to take into account in Geant4 new evaluated nuclear data libraries such as ENDF/B-VIII. 0 and JEFF-3. 3 instead of being limited only to ENDF/B-VII. 1 evaluation dating back to 2011. This allows the users to test a broader range of materials when designing experiments.
BPZE1 protects against mortality and colonization by SP1 in C57BL/6 mice Since the immune responses to B. pertussis infection differ between BALB/c and C57BL/6 mice  [30] , we investigated whether BPZE1-mediated protection against IPD was also observed in C57BL/6 mice. C57BL/6 mice were inoculated twice with live BPZE1 three weeks apart and then challenged with a lethal dose of SP1 one day following the second dose of BPZE1. 70% of the BPZE1-treated mice survived the challenge, while all control mice died within five days after challenge (figure 6A, p=0. 0006). These data show that, as for BALB/c mice, C57BL/6 mice were protected from death caused by lethal SP1 challenge when vaccinated with two doses of BPZE1. Lungs and blood were collected to measure SP1 load two days post-challenge. BPZE1 vaccination led to a significant reduction of SP1 CFU counts in both the lungs (figure 6B, p=0.
Alcohol consumption is linked to urinary sodium excretion and both of these traits are linked to hypertension and cardiovascular diseases (CVDs). The interplay between alcohol consumption and sodium on hypertension, and cardiovascular diseases (CVDs) is not well-described. Here, we used genetically predicted alcohol consumption and explored the relationships between alcohol consumption, urinary sodium, hypertension, and CVDs. Methods: We performed a comparative analysis among 295,189 participants from the prospective cohort of the UK Biobank (baseline data collected between 2006 and 2010). We created a genetic risk score (GRS) using 105 published genetic variants in Europeans that were associated with alcohol consumption. We explored the relationships between GRS, alcohol consumption, urinary sodium, blood pressure traits, and incident CVD.
This implies that there might be a synergic effect between alcohol consumption and sodium on the risk of hypertension. Advances in genetic data acquisition and analysis have improved our understanding of complex relationships and the biological mechanisms underpinning complex diseases. Recent genome-wide association studies (GWASs) among Europeans identified genetic variants in the form of single nucleotide polymorphisms (SNPs) associated with alcohol consumption  [12, 13]  and urinary sodium  [9]. To better understand the relationships between alcohol consumption, urinary sodium, hypertension, and CVDs, we constructed a genetic risk score (GRS) for alcohol consumption based on 105 SNPs associated with alcohol consumption in Europeans  [12, 13]. We explored the relationship between GRS and alcohol consumption and the effect of GRS on blood pressure, risk of hypertension, and CVDs in relation with urinary sodium excretion.
Study Population and Exclusion Criteria UKB is a large prospective cohort set up in 22 centers across the United Kingdom. It consists of over 500,000 participants aged 40 to 69 recruited between 2006 and 2010  [14]. Genetic data on 487,409 participants were available for analysis. We applied several exclusion criteria (Figure  1 ). We excluded participants who withdrew consent (N = 109), participants of non-European ancestry (N = 28,547), first-or second-degree relatives (N = 34,876), pregnant women or women unsure of pregnancy status at baseline (N = 232), prevalent CVD cases (N = 25,561), sex mismatch (N = 140), participants with health-related change in drinking habits (N = 65,579), non-alcoholic drinkers or participants with missing alcohol consumption data (N = 36,023), and participants with missing data regarding the main study variables (N = 1153).
Blood Pressure and Definition of Hypertension Supplementary Table  S1  lists the UKB data fields used in our analyses. Blood pressure was measured centrally by the UKB  [15]. We have described details about the blood pressure phenotype previously  [16]. In brief, for every participant, SBP and DBP was each measured twice at the UKB assessment center by an automated device (Omron HEM-7015IT digital BP monitor) or a manual device when automated readings could not be.
Blood Pressure and Definition of Hypertension Supplementary Table  S1  lists the UKB data fields used in our analyses. Blood pressure was measured centrally by the UKB  [15]. We have described details about the blood pressure phenotype previously  [16]. In brief, for every participant, SBP and DBP was each measured twice at the UKB assessment center by an automated device (Omron HEM-7015IT digital BP monitor) or a manual device when automated readings could not be taken. Where multiple values for blood pressure existed, we used the mean of all available measurements. We adjusted the blood pressure for participants taking blood-pressure-lowering medication by adding 15 mmHg to their SBP and 10 mmHg to their DBP measurements according to Tobin et al. [17]. We defined hypertension as stage 1 or stage 2 according to the American Heart Association (AHA) guideline  [18].
To avoid sample overlap with the UKB data, we specifically extracted the effect estimates from the summary statistics excluding UKB and 23andMe provided by Liu et al. [13]. We also checked that this summary statistics dataset used the same genome build assembly (GRCh37) as the UKB genotype data  [14]. To calculate the GRS, we multiplied the effect estimates by the number of risk alleles each UKB participant carries on the alcohol SNPs. The products were then summed across all SNPs to produce an overall weighted GRS for each participant. We standardized the weighted GRS for further analysis. [13]  (99 SNPs) and Evangelou et al. [12]  (46 SNPs). We first removed duplicates (n = 6). We then assessed LD (R 2 > 0. 1) among the SNPs and identified LD pairs using the LDmatrix function in LDlink  [22].
Within each SNP pair, SNPs with a stronger association with alcohol consumption were moved forward (32 SNPs removed). The remaining SNPs underwent extra LD pruning using individual-level data of the UKB (see methods; 2 SNPs removed). Eventually, 105 SNPs were selected to calculate the alcohol genetic risk score. SNP, single nucleotide polymorphism; LD, linkage disequilibrium; UKB, UK Biobank. To calculate the GRS for each UKB participant, we sought the effect estimates for the 105 alcohol SNPs from the alcohol GWAS meta-analysis study by Liu et al. [13]. To avoid sample overlap with the UKB data, we specifically extracted the effect estimates from the summary statistics excluding UKB and 23andMe provided by Liu et al. [13]. We also checked that this summary statistics dataset used the same genome build assembly (GRCh37) as the UKB genotype data  [14].
[9]. We performed SNP harmonization across the alcohol and urinary sodium datasets and checked the strand orientation prior to analysis. To derive the MR estimates, we used the inverse variance weighted (IVW) method, which calculates the MR causal estimate with the highest precision  [31]. However, these methods assume no horizontal pleiotropy. Horizontal pleiotropy occurs when the SNPs affect urinary sodium through traits other than alcohol consumption and these traits are not in the causal pathway from alcohol consumption to urinary sodium. When horizontal pleiotropy is left unbalanced, it can bias the MR causal estimate  [32]. To detect the presence of unbalanced horizontal pleiotropy and correct the MR estimate for any bias due to this, we performed additional sensitivity tests such as the MR-Egger  [32] , weighted median  [33] , and the weighted mode tests  [34].