dataesr/scientific-paragraphs-categorization huggingface dataset:
While  Combès et al. (2015)  argue that this results in a better statistical inference about the age (or palaeodose), it also comes with a downside: the user cannot visualise the data during the statistical analysis. In particular, the fact that the user must specify the list of grains to be included in the analysis implies that one should always pre-treat the samples in a standard way, by using for example "Analyst"  (Duller, 2015)  or the R "Luminescence" package  (Kreutzer et al. , 2012 (Kreutzer et al. , , 2020) )  to visually check the data but also to investigate the effect of various selection criteria on the datasets (see for example  Thomsen et al. , 2016 , on the effect of applying various selection criteria with frequentist statistical models; see  Heydari and Guérin, 2018 , for a similar study in a Bayesian framework).
A Cauchy distribution (sometimes also called Lorentz distribution) is a symmetric distribution which was chosen by  Combès et al. (2015)  because it has heavy tails; i. e. extreme values have a non-zero probability. Hence, the Cauchy distribution seemed to be well-suited for the analysis of widely dispersed datasets including outlier values such as single-grain D e distributions. Coming back to the samples from La Ferrassie, on top of saturation problems  Guérin et al. (2015b)  also identified dose rate variability as an important factor of dispersion in equivalent doses: the values of the CDM overdispersion parameter for the D e distributions of the samples were equal to 29 ± 3 % and 35 ± 3 %, respectively. If we assume that this overdispersion arises from dose rate variability to single grains of quartz, Heydari and Guérin (2018) using laboratory-controlled experiments showed that the Cauchy distribution and the CDM should both lead to ∼ 5 %-10 % age underestimation, because both models are biased.
Sect. 3 above) when running the function AgeS_Computation() applies to all samples. Then, for all j in {2,. , Nb_Sample+1} and all i in {j,. , Nb_Sample}, StratiConstraints[j,i] = 1 if the age of sample whose number ID is equal to j -1 is less than the age of sample whose number ID is equal to i. Otherwise, StratiConstraints[j,i] = 0. In practice, in our case StratiConstraints [1,] = (1, 1), StratiConstraints [2,] = (0, 1) (which means that sample FER 1 is not younger than itself but is younger than sample FER 3) and StratiConstraints [3,] = (0, 0) (which means that sample FER 3 is neither younger than sample FER 1 nor than itself). Note that in the markdown document provided in the Supplement, the corresponding code lines have comments to make this description easier to follow. Running the function AgeS_Computation() with this matrix of stratigraphic constraints only marginally affects the ages; in this case, the 95 % CIs become  [34.
General considerations In the previous calculations, all the variance is treated as random, whereas common, systematic errors affect all ages in the same direction, although to varying degrees (so systematic errors are unlikely to result in stratigraphic inversions). One of the main advantages of applying the models implemented in the BayLum package -contrary to other chronological modelling tools such as "OxCal" (Bronk Ramsey and Lee, 2013) or "Chronomodel" (Lanos and Philippe, 2018)lies in the possibility to include the structure of uncertainties specific to OSL dating. For instance, a radiocarbon age is derived only from the ratio of 14 C to 12 C (on top of which comes the more complex problem of calibration), whereas an OSL age involves a large number of measurements, each with its uncertainty  (Aitken, 1985 (Aitken, , 1998)).
Indeed, as can be seen, there is a positive correlation between the ages of samples FER 1 and FER 3: the greater the age of sample FER 1, the greater the mean age of sample FER 3. In other words, if the age of sample FER 1 were underestimated, then in all likelihood, the age of sample FER 3 would also be underestimated. Furthermore, the length of the CI for the age of each sample is slightly larger than without modelling the covariance (cf. Table 1); i. e. modelling the covariances slightly increases the age uncertainties. However, the positive correlation of ages has other, direct consequences. First, let us suppose that we have no knowledge of a stratigraphic link between the two investigated samples, and we wish to test the hypothesis that sample FER 1 is younger than sample FER 3. The credibility of such an assumption can be tested using the function MarginalProbability() of the "ArchaeoPhases" R package  (Philippe and Vibet, 2020)  Figure  6.
Sample FER 2 from this layer being poorly bleached, it is at present impossible to model with BayLum. However, an alternative to estimate the age of FER 2 consists of supposing that it has a uniform prior probability density between the ages of samples FER 1 and FER 3: P (A 2 |data) ∼ I [A 1 ;A 3 ] A 3 -A 1 π (A 1 , A 3 |data) dA 1 dA 3 , ( 11 ) where A i is the age of sample i, I [A 1 ;A 3 ] is the indicator function between A 1 and A 3 , and π (A 1 , A 3 |data) is the posterior joint density of A 1 and A 3 knowing the data (i. e. the density estimated with BayLum). Doing so (see the markdown file for the corresponding code lines), working from the output of BayLum one obtains a 95 % CI of  [36; 46]  ka, which can be compared with the confidence interval of [36; 48] ka obtained by  Guérin et al.
Integration of independent chronological data (radiocarbon) The BayLum package also offers the possibility to include radiocarbon ages in the chronological models  (Philippe et al. , 2019) ; more specifically, radiocarbon ages are calibrated within BayLum, using the function AgeC14_Computation() or Age_OSLC14() (in the latter case the function necessitates at least one OSL age calculation). Introducing covariance matrices to account for systematic errors on OSL data does not reduce the OSL age uncertainties; however, it becomes particularly useful to correct for estimation biases when more precise ages, unaffected by these systematic errors, are integrated into the models. To illustrate this, we decided to construct two models constraining the age of FER 3; for illustration purposes, in this section, we used the simplistic θ matrix described above in Sect.
Notes of caution As always when working with statistical models, one should first and foremost evaluate the measured data in light of the sampling context. We already mentioned the importance of grain selection (Sect. 2. 2); but, perhaps more importantly, and especially since users of BayLum have to make modelling choices (e. g. regarding the dose-response curves fitted to OSL measurements or the distribution of individual equivalent doses around the central dose), it is crucial to carefully examine data and assess their quality before building potentially sophisticated models. We would like to emphasise a few warnings regarding modelling samples in stratigraphic constraints, and the association of ages obtained by different methods. We would advise users, before combining for example radiocarbon and OSL ages, to first thoroughly examine the corresponding datasets independently: how were the data produced (with which experimental procedure)? Are the provided uncertainties reliable (or is there an unrecognised source of error that should be included in the evaluation of uncertainty)? Users are also encouraged to examine the consistency of results produced by each method, in light of the stratigraphy.
In a second stage, before modelling of independent ages, we would recommend assessing the consistency of these datasets -do they (at least broadly) agree? If not, can a parsimonious explanation be found? For example, it is rather common, when performing Bayesian modelling with tools such as OxCal, to observe a large fraction of ages considered as outliers; such observations should urge users to examine their data again and come up with likely explanations (note that to date, no outlier model has been developed for the OSL ages in BayLum). When it comes to imposing ordering constraints between ages as a result of stratigraphic observations, it is, of course, essential to leave no doubt about the validity of these stratigraphic constraints (the results of a model depend on the assumptions that are made, and the order in ages is a very strong constraint).
Perhaps more importantly, even when stratigraphic constraints are valid, it is possible that applying them will not improve the statistical inference. A simple example to illustrate this point is that of two superimposed, distinct layers (so that a stratigraphic order is clear) whose true ages are equal (or in practice, for which the age difference is negligible compared to the typical uncertainties of the implemented dating method). In such a case, modelling the ages with stratigraphic constraints is likely to result in a loss of accuracy (the age of the older layer will be overestimated, and that of the younger layer underestimated) compared to a model where no stratigraphic con-straints are imposed. Future developments of the BayLum package might include the possibility to test different modelling scenarios by comparing the agreement between the observations and the posterior probability densities, for example using the Bayes information criterion (BIC).
5B), so we know that the age of the sample FER 1 must be less than that of the sample FER 3 (i. e. sample FER 1 is younger than sample FER 3; for detailed stratigraphic information on the site of La Ferrassie, which is of paramount importance in this section, the reader is referred toGuérin et al. , 2015b). To encode this information, the function AgeS_Computation() takes as argument the object StratiConstraints, which is a matrix whose size depends on the number of analysed samples. First, the data in the DATA object (which is the output of the function Generate_DataFile()) must be ordered in stratigraphic order from top to bottom: thus, in our case the list of names used by the function Generate_DataFile()is FER 1, FER 3 (rather than FER 3, FER 1). Then, the stratigraphic matrix contains numbers equal to 0 or 1, indicating the applied bounds to the age of each sample.
Computational Analysis of miR-21-5p Interactors In light of the involvement of miR-21-5p in several pulmonary and extra-pulmonary fibrotic processes, we decided to analyze its interaction network to clarify the possible pathophysiological mechanisms at the base of BOS. DisGeNET v4. 0 (https://disgenet. org, accesed on 17 February 2020), a discovery platform integrating gene-disease associations from several public data sources and the literature, was used to computationally select the set of target genes associated with BOS development (disease identification in DisGeNET-UMLS CUI: umls:C0006272). The CyTransfinder software  [16]  was used to infer direct interactions between miR-21-5p and the identified set of genes. Indirect interactions between miR-21-5p and selected genes were also analyzed using CyTransfinder; miR-21-5p targets were identified in 5 public miRNA target repositories including miRTarBase (http://miRTarBase.
cuhk. edu. cn/ accesed on 21 February 2020), TargetScan (http://www. targetscan. org/vert_72/ accesed on 21 February 2020), PicTar 4 and 5 (https://pictar. mdc-berlin. de/ accesed on 24 February 2020), and miRanda (http://www. microrna. org/ accesed on 24 February 2020), and those that appeared in at least to 2 of these were investigated for their association with BOS-related genes listed in the DisGeNET database.
Computational Identification of miR-21-5p Targets and Immunohistochemistry In the DisGeNET database, 63 genes were found to be annotated as associated with BOS (http://www. disgenet. org/browser/0/1/0/C0006272/0/25/63/_a/_b. /-score/ accessed on 17 February 2020). By CyTransfinder, no direct interaction was found between miR-21-5p and DisGeNET listed genes. Therefore, we decided to first explore the interactors of miR-21-5p using five public miRNA databases, and selected those that appeared in at least three, four and five databases (Table  S1 ), to further check their association with BOS-related genes listed in DisGeNET database  [18]. STAT3 and PCBP1 showed more interactors in the DisGeNet database (Figure  3 ). Between these, only STAT3 showed a documented involvement in several fibrotic processes  [19] , with a role in bleomycin-induced lung fibrosis  [20]  and idiopathic pulmonary fibrosis (IPF)  [21].
Computational Identification of miR-21-5p Targets and Immunohistochemistry In the DisGeNET database, 63 genes were found to be annotated as associated with BOS (http://www. disgenet. org/browser/0/1/0/C0006272/0/25/63/_a/_b. /-score/ accessed on 17/02/2020). By CyTransfinder, no direct interaction was found between miR-21-5p and DisGeNET listed genes. Therefore, we decided to first explore the interactors of miR-21-5p using five public miRNA databases, and selected those that appeared in at least three, four and five databases (Table  S1 ), to further check their association with BOS-related genes listed in DisGeNET database  [18]. STAT3 and PCBP1 showed more interactors in the DisGeNet database (Figure  3 ). Between these, only STAT3 showed a documented involvement in several fibrotic processes  [19] , with a role in bleomycin-induced lung fibrosis  [20]  and idiopathic pulmonary fibrosis (IPF)  [21].
In this study, using DisGeNET together with CyTransfinder software, we observed that miR-21-5p does not directly interact with any of the BOS-related genes listed in Dis-GeNET. For this reason, we decided to consult different miRNA databases in order to find which proteins were more likely related to miR-21-5p. Subsequently, we investigated their association with the BOS-related genes listed in DisGeNET. After computational analysis, we identified eleven miR-21-5p-related genes that encode proteins that interact with various BOS-related genes. Some of these reported targets are cancer-related and are associated with carcinogenesis (such as SPRY2, SKY and SRSF3), cancer prognosis (such as CNOT6, RECK and GID4) or cancer cell plasticity (such as RMND5A)  [28] [29] [30] [31]. Interestingly, between these targets only STAT3 and PCBP1 showed more interactors.
At the same time, miR-21-5p can indirectly induce STAT3 by targeting PIAS3, a STAT3 inhibitor, forming a positive feedback loop  [41] [42] [43]. To further study the relationship between STAT3 and miR-21-5p, and more specifically, to confirm the presence of the STAT3/miR-21-5p positive feedback loop mediated by the down-regulation of STAT3-inhibitor by miR-21-5p in BOS, we evaluated STAT3 mRNA levels in cultured LFs derived from BAL and A549 cells after miR-21-5p inhibition. In our study we have demonstrated that miR-21-5p inhibition treatment induces a reduction in miR-21-5p levels after 48 h of treatment with anti-miR-21. However, this does not lead to the alteration of both STAT3 mRNA and protein expression levels, even though STAT3 is described in many databases as a target of miR-21-5p.
Figure3. miR-21-5p regulatory network. Interactions between miR-21-5p and genes that appeared to be associated with BOS in at least 3 different databases (Mirtarbase, Targetscan, Miranda, Pictar4, and Oictar5). miR-21-5p-related genes (green boxes) encoding for proteins that interact with BOS-related genes listed in DisGeNet database (light blue boxes).
Our work is the first to simultaneously address all these challenges. Contributions. We present Dynamic, Private and Secure GWAS (DyPS), a federated system where biocenters collaborate by dynamically and safely contributing data to a GWAS without losing control over the genomes they have sequenced. DyPS leverages a Trusted Execution Environment (TEE), i. e. , Intel SGX, to securely process encrypted genomes and compute GWAS statistics. In addition, DyPS ensures privacypreserving GWAS while accepting participation consent withdrawal. To do so, DyPS implements efficient algorithms that determine how to safely release and update GWAS statistics without noise addition, which guarantees no data utility loss. Moreover, DyPS tolerates up to all but one colluding biocenters without privacy leaks. We evaluated DyPS with a synthetic GWAS workload studying up to 300,000 SNPs and more than 6 million simulated genomes.
We also evaluated DyPS using two different genomic datasets consisting of 2,000 and 35,000 real genomes, respectively. Our results show that DyPS dynamically updates test statistics with a reason-able additional request processing delay (11% longer) compared to a naïve approach that would update them with minimal delay but would lead to 8% of the genomes not being protected. Moreover, DyPS eventually released the same amount of aggregate statistics than a static release method, which would release statistics after all genome requests have been processed, and punctually released up to 2. 6 times more statistics (44 instead of 17 for 350 considered SNPs). Finally, DyPS tolerates colluding biocenters. In the worst case scenario (only one biocenter is not colluding), DyPS updates the GWAS results with only 6.
TEE-based architecture DyPS uses Intel Software Guard Extensions (SGX)  [31] , which defines the concept of enclave as an isolated unit of data and code execution that cannot be accessed even by privileged code (e. g. , the operating system or hypervisor). Enclaves can be attested to prove that the code running in the enclave is the one intended, and that it is running on a genuine Intel SGX platform. Once attested, enclaves can be provisioned with secret data by using authenticated secure channels. Moreover, enclaves can persist secret data outside the trusted zone by using a sealing mechanism. Once DyPS' enclave has been initialized, each biocenter executes a remote attestation procedure to authenticate it and establish a secret symmetric key. The biocenters sign their data with their private key, encrypt it with the shared symmetric key and send it over the network to the enclave's host.
Workflow diagram GWAS can produce both test statistics and aggregate statistics. DyPS follows the workflow depicted in Figure  4  to ensure safe releases in both cases. This workflow is executed in the enclave and contains multiple modules: (1) the pending requests queues, (2) the request selection to produce safe test statistics, (3) the GWAS processing, and (4) the test to produce safe aggregate statistics. (1) FIFO pending requests. DyPS maintains FIFO queues of genome additions or removals for each biocenter. DyPS tries to execute the received requests according to their initial ordering by the biocenter through the use of their sequence number. However, DyPS might not always treat requests across the FIFO queues (e. g. , because a genome A cannot be immediately removed, while a genome B can be added).
More specifically, DyPS uses equation (3) as it implies equations (  4 ) and  (5). DyPS assembles batches of genome operations according to the FIFO ordering of requests, and so that a batch contains more additions than removals for every subset of (B -f ) biocenters, and an overall number of genome operations either equal to 0 or larger than LtoN (L) for every subset of (B -f ) non-colluding biocenters. We provide the pseudocode of DyPS' requests selection algorithm, and prove by induction that the adversary is never able to isolate test statistics where less than LtoN (L) genomes participate in Appendix A. From a high-level perspective, this algorithm works as follows. First, all pending addition requests of biocenters are selected, and for each of them, at most an equal number of pending removal requests.
Experimental setup We used both Windows 10 Enterprise and an Ubuntu 18. 04 LTS in a 64-bit machine, equipped with 16 GB of RAM and an Intel i7-8650U @ 2. 11. GHz, which supports Intel SGX. We evaluate the performance of DyPS under several scenarios using simulated and real genomes. We run DyPS' code both in Java using Java JDK 12. 0. 1 and Eclipse IDE (4. 11. 0), and inside an Intel SGX enclave using Graphene  [32]  to implement DyPS in C++, so that it can run inside the SGX enclave. We use AES 256 to encrypt messages, and ECDSA for signatures. When it executes the remote attestation procedure, a biocenter agrees on a key with the enclave, which it uses to encrypt and sign the data it sends to the enclave, while the enclave can verify it upon reception. During the experiments, we use rounds where biocenters generate requests that are sent to the enclave, and the enclave tries to generate a GWAS statistics release.
To do so, we compare DyPS with a static approach that would rely on the LR metric  [11]  to release singlewise allele frequencies at the end of the experiment. For this experiment, we used the default LR parameter values defined in  [11] : a MAF cut-off of 0. 05, a LD between SNPs cut-off of 10 -5 , a false positive rate of 0. 1, and a true positive rate of 0. 9. We stress DyPS by simulating the generation of up to 6 million genomes studied over up to 300K SNPs to evaluate its performance with test statistics. In addition, we use two real genome datasets to evaluate DyPS' performance on both test and aggregate statistics: the idash2017 dataset  [33] , which consists of 2,000 genomes, and the phs001039. v1. p1 dataset from dbGAP  [6] , which consists of more than 35,000 genomes.
Bandwidth, CPU and memory consumption We use 64-bits integers to encode the ID fields in a request, except for the pop and op fields that only require one bit. Overall the size of a request is 258 bits, which represents approximately 48 Bytes after encryption. A genome is encoded using 2 bits per SNP, which would represent only 75 KB for a GWAS studying 300,000 SNPs. Given those numbers, bandwidth is not a bottleneck for DyPS since communications only occur when biocenters asynchronously send requests to the enclave. To measure DyPS' CPU and memory consumption, we first consider a GWAS scenario that involves 4 noncolluding biocenters (B = 4, f = 0). Figure  7  shows the CPU running time of the brute force (BF ) and of DyPS' request selection algorithms during a synthetic GWAS that involves 5,000 SNPs and 20,000 rounds.
SNP selection for aggregate statistics DyPS' releases of aggregate statistics depend on the SNP distribution among the set of added genomes. We first evaluate DyPS using the idash2017 dataset  [33] , which consists of 2,000 real genomes (1,000 case and 1,000 control). We used all the genomes in the control set as the adversary knowledge. For aggregate statistics, DyPS' release mechanisms require more extensive computations when the number of previous releases and when the number of genomes used per release increase (cf. Section 4. 4). We therefore primarily consider scenarios where f = 0, because it maximizes the number of releases and their size. In this section, we consider a GWAS that studies a small set of L SNPs (i. e. , the top 10 most significant SNPs) so that releases are more frequent.
DyPS vs. static release of aggregate statistics Next, we compare DyPS with a state-of-the-art static release algorithm over a larger set (the top 1,000 SNPs) of the idash2017 dataset in Figure  14. More precisely, we measure over how many SNPs both approaches are able to release aggregate statistics only, which enables frequent updates. We use the LR-metric  [11]  to decide whether singlewise frequencies can be released. The static release method identified that singlewise frequencies could be safely released over 45 SNPs out of the 1,000 studied, using the full set of genomes remaining at the end of the experiment (i. e. , in only one releasethe dashed bar). On the other hand, the number of selected SNPs for DyPS varied according to the rounds, as expected, that is, in each different round, a different distribution of genomes participated in a release and, therefore, the set of SNPs over which statistics can be released evolves.