dataesr/scientific-paragraphs-categorization huggingface dataset:
miR transfection experiment datasets Fourteen series of miR transfection experiments on five different platforms by Seki et al. [24-38], for a total of 157 gene expression datasets involving 59 miRs transfected in different human cancer cell lines were downloaded from GEO. Gene expression data were obtained using Agilent whole-genome microarrays. An experiment series from Misiewicz-Krzeminska et al. [39] , containing data from miR-214 expression in H929 myeloma cell lines was also downloaded to support our experimental data. The data were LOWESS normalized and background subtracted from log10 of processed Red signal/processed Green signal by the author through Agilent software (Supplementary File 2). Some of the gene names annotated to the probes were obsolete and we updated them, exploiting the bioconductoR (Bioconductor 3.
2, R 3. 2. 3) package biomaRt (v. 2. 34. 2)  [40]. Then, we selected the probes with the highest fold change across each platform, we selected a unique set of genes conform to all the experiments considered to use as universe gene set (containing 17,025 genes) and applied an arbitrary, the literature-approved, threshold of t = 0. 5 on the log10 (fold change) of the expression data to select the up-/downregulated genes for each experiment in the Seki panel and in the GSE35948-miR-214 panel.
Gene expression profiling Protein-coding expression profiling was carried out using the Whole Human Genome Oligo Microarray (41,000, 60-mer oligonucleotide probes) from Agilent Technologies. Total RNA (800 ng) was labeled by the Agilent One-Color Microarray-Based Gene Expression protocol, according to the manufacturer's instructions. Slides were scanned on an Agilent microarray scanner. Analysis was performed as described in  [43]. Gene expression data are available in the U. S. Center for Biotechnology Information Gene Expression Omnibus (GEO) database (GSE124965).
Clearly, this is a simplified view. Undirect interactions and more complex regulatory networks and cascades can, in principle, lead to similar expression patterns. Therefore, the regulatory circuits in Figure  1  should be seen as the most basic interactions between miR and epi-miR compatible with the experimental expression patterns. For example, cases in which several genes are both upregulated after miR overexpression and downregulated after epi-R ablation cannot be explained by these simple circuitries and require additional regulatory players. More in details, the analysis was organized in three steps as depicted in Figure  2. First, we identified a panel of gene sets which could be considered strongly associated to epigenetic regulation. To this end, we selected the gene sets available from the gene set enrichment analysis (GSEA) database (software.
broadinstitute. org/gsea/)  [22, 23] oming from epi-R ablation experiments. The main assumption in our protocol is that the epigenetic regulation is so strong and widespread that several of the target genes do not depend on the cell lines used in the experiment and can be used as a benchmark of the activation of the epigenetic pathway of interest. We chose gene sets associated to each of the epigenetic pathways discussed above, identifying 43 gene sets (reported in Supplementary File 1). The representation of the different epi-Rs in the identified datasets is summarized in Figure  3A. Briefly, 20 of them are specifically devoted to the PRC2 and contained both manually curated lists and experiments looking for the histone mark H3K27me3 associated to the PRC2 activity; while 23 were devoted to the other three epigenetic pathways discussed above.
Identification of candidate epi-miRs In order to test our procedure, 14 experiment series (Supplementary File 2) for a total of 157 miR transfection experiments (Supplementary File 3) involving 59 different miRs on 26 different cell lines were downloaded from Gene Expression Omnibus (GEO) database (https://www. ncbi. nlm. nih. gov/geo/). This particular panel of transfections was chosen among the hundreds of miR transfection experiments available in the GEO database, miR miR epi-R epi-R epi-R miR A B D miR overexpression ablation epi-R C miR epi-R E epi-R miR loops Theoretical Figure 1. Schematic view of considered circuitries between miRNAs and epigenetic regulators. miR overexpression or epi-R ablation or chromatin immunoprecipitation experiments were used to identify the pattern of miR and epi-R interactions.
The putative circuitries between miR and epi-R are depicted. In (A), miR and epi-R do not interact and regulate distinct sets of genes; in (B), they repress an overlapping set of genes; in (C), the epi-R represses the miR; in (D), the miR represses the epi-R; in (E), both (C) and (D) situations are present for the same miR and the same epigenetic pathway. The up arrows indicate the sets of genes upregulated in the depicted experiments, while the down arrows indicate the sets of downregulated genes. Cases in which the genes were upregulated after miR overexpression and downregulated after epi-R ablation were not took into consideration because it was not possible to group them under a single common theoretical circuit. epi-R: Epigenetic regulator; miR: miRNA. because they are characterized by a high level of homogeneity and consistency both of the experimental protocol and the data curation.
Thirty-four out of 59 miRs resulted as candidate epi-miRs: 19 are known as epi-miRs, while 15 are identified as new, a pie chart showing the percentage of the different classes of miR/epi-miR is shown in Figure  3B. The results of the best intersections obtained for each candidate epi-miR and the associated epigenetic pathway are summarized in Table  1 , where in the second column the association of each miR to the putative targeted epigenetic pathway is shown. In third, fourth, fifth and sixth columns, the potential loops between the candidate miR and epi-R belonging to the categories described in Figure  1  are indicated. Finally, in the last column, the results obtained using the miRTarBase database of experimentally validated miR-target interactions  [41]  are indicated (Supplementary File 4).
Pathways displaying very strong signatures (FDR < 10 × 10 -8 ) are reported in bold face (complete list of intersections in Supplementary File 5). More than half (34). Schematic pipeline of our analysis. Each miRNA (miR) transfection experiment gene set was divided in up-/downregulated gene sets, and for each of them was computed the intersection with the mSigDb gene sets. The significance of these intersections has been evaluated through a hypergeometric test and corrected for multiple testing by the Benjamini-Hochberg procedure. All of the miRs involved in intersections with false discovery rate lower than 0. 001 were selected as candidate experimentally validated miR. Results were further refined through gene set enrichment analysis, Database for Annotation, Visualization and Integrated Discovery, miRTarBase or TargetScan analysis, and each miR-epigenetic regulator interaction was labeled on a specific theoretical circuit based on the sign of the miR gene set and the type of epigenetic gene set involved in the intersection.
DAVID: Database for annotation, visualization and integrated discovery; epi-miR: Epigenetic miRNA; GSEA: Gene set enrichment analysis. can be influenced by the fact that most of the miRs included in the panel are known to be involved in cancer or in differentiation processes and thus more likely to be involved in epigenetic pathways. In most of the cases (182 intersections corresponding to 55 different transfection experiments), we found very low FDR for the intersections of the upregulated genes with the GSEA sets, which could be compatible with a direct interaction of the miR with an epi-R. Interestingly, we also found quite a few examples (93 intersections corresponding to 38 different transfection experiments) of significant intersections for the downregulated genes. In a few cases, when transfection experiments of the same miR in different cell lines were available, we found that the same miR may act as an epi-miR in one particular cell line and show no epigenetic effect in the other cell lines.
Examples of candidate epi-miRs identified with our pipeline miR-31 In the panel of the analyzed transfection experiments, miR-31, a typical miR deregulated in cancer, was present in a single transfection experiment in the human highly metastatic prostate cancer cell line, PC3. Looking at the intersections with our gene sets, four entries were observed (FDR <0. 001, see Supplementary File 7). All of them were for the set of genes upregulated after transfection (NUYTTEN EZH2 TARGETS UP, SENESE HDAC1 TARGETS UP, SEN ESE HDAC3 TARGETS UP and GSE27434WT VS DNMT1 KO TREG DN). Three sets contained genes upregulated after knockout of EZH2, HDAC1 and HDAC3, suggesting a direct inhibition of these three genes by miR-31 or more generally on the corresponding epigenetic machinery as shown in the third column of Table  1.
miR-138 Differently from the previously described miRs, miR-138 is a well-known epi-miR  [7] , in this case only one transfection experiment in the A498 kidney carcinoma cell line was present in the considered datasets. Eight intersections (FDR <0. 001) were observed and all of them in the set of genes downregulated after transfection. Noteworthy, in all these cases, the gene sets are incoherent with respect to the expected epi-miR interaction. For instance, we found among the downregulated genes a large number (FDR = 10 × 10 -19 ) of genes upregulated after knockout of EZH2. This means that these genes are simultaneously targets of miR-138 and EZH2. We report as an example the intersection corresponding to the file NUYTTEN EZH2 TARGETS UP mentioned above. This intersection is composed by 95 genes which are listed in the Supplementary File 7 (FDR = 10 × 10 -19 ).
miR-214 modulates multiple genes in melanoma cells The procedure we propose can be used given a miR of interest, to identify its candidate epigenetic interactors. Due to the relevance of miR-214 in tumor progression, we are continuously investigating unknown mechanisms through which miR-214 could coordinate tumor metastatization. In the attempt to identify new genes directly or indirectly modulated by miR-214, MA-2  [19]  aggressive melanoma cells were transiently transfected with a precursor for miR-214 (pre-miR-214) or negative controls (precontrol). RNA was extracted 72 h following transient transfection and miR-214 expression levels verified by real-time PCR (data not shown). RNA was used to perform whole human genome gene expression analysis (GSE124965). A total of 493 differentially expressed genes (342 upregulated; 88 downregulated) were found, considering a 14.
2% FDR. Surprisingly, 342/493 of the differentially expressed genes resulted to be upregulated, suggesting a possible indirect regulation of these genes by miR-214 via a potential crosstalk with an epi-R. Similar observation came from the analysis of another miR-214 dataset (GSE35948)  [39]. The first hypothesis we made was that miR-214 could influence the expression of this huge amount of genes via an epigenetic mechanism. For this reason, the list of the differentially expressed genes was subjected to the analysis described above to identify candidate epigenetic interactors of miR-214. The lists of the differentially expressed genes used for the analysis is available in Supplementary File 9. The heatmaps for both datasets are shown in Figure  6A  and B, where it is qualitatively appreciable the increased number of upregulated genes compared with downregulated ones following miR-214 overexpression.
Eight GSEA sets were identified, across up-and downregulated gene sets using both our (GSE124965) and GSE35948  [39]  datasets (Supplementary File 10), showing evidence of a direct regulation of the miR on the considered epigenetic pathways. Five of them correspond to the PRC2 pathway and two correspond to HDACs. In particular, the PRC2 sets are related to the silencing of the enhancer of zeste 2 polycomb-repressive complex 2 subunit (EZH2) and to the silencing of the EZH2 interactor NIPP1 (Supplementary File 10), suggesting EZH2 as the candidate gene directly targeted by miR-214 and responsible for the vast upregulation observed in the experimental dataset. GSEA analysis of the genes belonging to the best PRC2 intersection, described above, shows a significant enrichment with sets related to epithelial-to-mesenchymal transition and TGFb pathway in agreement with the prometastatic role of miR-214 (Figure  7 ).
Discussion We selected a panel of gene sets which can be considered bona-fide targets of the above-discussed main epigenetic pathways and intersected them with the set of genes, respectively, upregulated or downregulated in a series of gene expression datasets coming from publicly available transfection experiments of various miRs. Intuitively, a large intersection could be considered as a signature of an epigenetic amplification. The p-value of the intersection was assessed using the hypergeometric distribution corrected for multiple testing. Our analysis shows that the interaction between miRs and epi-Rs is widespread. Epi-miRs are typically involved in differentiation processes and are likely to play an important role in cancer, where accumulating evidence shows that epigenetic alterations exert pivotal roles in tumor progression.
The DNMT pathway is indeed one of the pathways that we found as direct targets of miR-145 in our analysis (see Table  1 ). Finally, a very interesting pattern is found in the case of the well-known miR-7  [7] , for which GO categories show an enrichment in immune response and cell cycle inhibition categories and our findings suggest that it exerts these functions by targeting the PRC2 pathway. Our pipeline can be used to identify putative epigenetic interactors of any given miR. The prometastatic miR-214 has been used as an example of the potentiality of our methods. Based both on a dataset from our transfection experiments and from a publicly available dataset, we identified EZH2 as an epigenetic interactor of miR-214. We demonstrated that miR-214 regulates EZH2 in melanoma cells and EZH2 is, in turn, at least partially responsible of the downstream pathway activated by miR-214 involving ALCAM, ITGA5 and miR-148b.
These results were in agreement with previous data regarding miR-214 regulation of EZH2 during muscle differentiation  [58]. Moreover, the GSEA analysis performed for the best intersection of miR-214-regulated genes with the selected GSEA sets used in our analysis shows an enrichment in EMT and TGF-β-related genes, perfectly in agreement with the prometastatic role of miR-214 during tumor progression. The identification of miR-214 as an epi-miR in a tumor cell context highlights new layers of regulation of this miR in the control of tumor progression.
Acknowledgments The authors are grateful to  Lei Xu  and  Richard Hynes  for the MA-2 cell line. Financial & competing interests disclosure M Caselle received the funds from Departments of Excellence 2018-2022  Italian Ministry of University and Research (MIUR ;  L. 232/2016 ). F Orso funded by  FIRB giovani 2008 project  ( RBFR08F2FS-002 FO ). D Taverna received the funds  Compagnia di San Paolo , Torino, 2008. 1054,AIRC 2010, 2013, 2017 ( IG2010-10104DT ;  IG2013-14201DT ;  IG2017-20258DT ),  Fondazione Cassa di Risparmio Torino CRT  ( 2014. 1085DT ); Progetto di Ricerca di Ateneo 2017/San Paolo Torino   CST0165134. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
Summary points • miRNAs (miRs) are small noncoding RNAs able to post-transcriptionally regulate gene expression. • miRs can cross-talk with other regulatory genes such as the epigenetic factors, amplifying their regulatory potential. • miRs-regulating epigenetic factors are denoted as epi-miRNAs (epi-miRs). • Starting from pre-existing miR transfection experiments, we identified 34 candidate epi-miRs: 19 of them are known as epi-miRs, while 15 are new. • We found that the same miR may act as an epi-miR in one particular cell line and show no epigenetic effect in the other cell lines. • Using an in-house generated gene expression dataset, we experimentally proved that, a component of the polycomb-repressive complex 2, histone methyltransferase enhancer of zeste homolog 2 (EZH2), is an interactor of miR-214, a well-known prometastatic miR in melanoma and breast cancer.
2 1 4 # 3 Figure 6. Figure 7. 4367 Figure 6. Heatmaps of the expression levels of miR-214-regulated genes. (A) Heatmap representing the results of the microarray analysis performed on MA-2 melanoma cells 72 h following miR-214 transient overexpression (GSE124965). (B) Heatmap representing the results of the microarray analysis of GSE35948 dataset.
Data are sampled in 2. 5 µs Gate Time Units (GTUs). The GTU corresponds to the time that light from the atmosphere takes to pass through a pixel of a detector in space at 400 km altitude, and represents the time resolution of the detectors within the JEM-EUSO program. The readout is performed by one 64-channel SPACIROC1 ASIC chip  [10]  per MAPMT, with a dead time at beginning of each GTU of 0. 2 µs and 30 ns double pulse resolution. Typical of fluorescence detectors, EUSO-TA works at nighttime and in the best cases in clear sky conditions, to reduce effects of atmospheric and cloud attenuation. The elevation of the telescope axis can be manually changed from 0 • to 30 • with respect to the horizon, whereas its azimuth is fixed at 53 • from North counterclockwise, pointing to the CLF. Data discussed in this proceedings have been acquired with the external trigger from the BRM-FDs: in case of trigger, a packet of 128 GTUs centered around the time of the trigger is saved, which otherwise would be overwritten.
Observation of UHECR events Four data acquisition campaigns in the year 2015 and one in 2016 were done with the external trigger provided by BRM-FDs, for a total of about 140 hours. The analysis reported in this proceedings refers to data taken in 2015, equivalent to about 120 hours. Energy and distance of the EASs crossing the field of view of EUSO-TA, whether detected or not, can be used to estimate the detection limit of the detector. As the field of view of BRM-FDs includes the EUSO-TA one, they detect all the EASs in the EUSO-TA field of view. The Telescope Array Collaboration provides the list of such events and also the reconstruction parameters of the UHECRs generating the EASs, reconstructed in monocular mode (using just data collected with the BRM-FDs). The reconstruction parameters used in this analysis are the impact parameter R p , i.
In particular for inclined showers approaching the detector, the distance along the telescope axis is much longer than the impact parameter. To estimate E eq of the EASs that crossed the EUSO-TA field of view, an energy conversion factor is calculated and applied to E recTA. Using the zenith, azimuth and energy of the EASs provided by the Telescope Array Collaboration, EAS simulations were performed with CONEX  [14]. These simulations provide the distribution of the energy loss (proportional to the number of photons emitted) with respect to the altitude. From the direction and impact point on ground of an EAS and the elevation angle of EUSO-TA during the data acquisition, event by event, it is possible to calculate the altitude at which EUSO-TA observed (or did not) such EAS. The energy conversion factor is the ratio between the energy loss of the EAS at the altitude where EUSO-TA observed with the actual elevation (calculated as the point where the distance between the telescope's optical axis and the shower axis is minimum), and the energy loss in the case the detector was pointing to the EAS maximum, with known altitude in the simulations.
Acknowledgments The support received by the  Telescope Array Collaboration  is deeply acknowledged. This work was partially supported by  Basic Science Interdisciplinary Research Projects of RIKEN  and  JSPS KAKENHI  Grant ( JP17H02905 ,  JP16H02426  and  JP16H16737 ), by the  Italian Ministry of Foreign Affairs and International Cooperation , by the  Italian Space Agency  through the  ASI INFN  agreement n. 2017-8-H. 0and contract n. 2016-1-U. 0,by  NASA  award  11-APRA-0058  in the USA, by the  Deutsches Zentrum für Luft-und Raumfahrt , by the  French space agency CNES , the  Helmholtz Alliance for Astroparticle Physics  funded by the  Initiative and Networking Fund of the Helmholtz Association (Germany) , by  Slovak Academy of Sciences  MVTS JEM-EUSO as well as  VEGA  grant agency project  2/0132/17 , by  National Science Centre in Poland  grant ( 2015/19/N/ST9/03708  and  2017/27/B/ST9/02162 ), by Mexican funding agencies  PAPIIT-UNAM ,  CONACyT  and the  Mexican Space Agency (AEM).
Acknowledgements and role of the funding source Medical writing support for this manuscript was provided by  Michael Healy of Sudler , fully funded by  Bayer Pharma AG, Leverkusen, Germany. All authors contributed to conceptualisation and review of the draft manuscript. We thank  Jason Taylor  for discussions around his work on FVIII in bone health, and  Snejana Krassova  and  Prasad Mathew of Bayer  for their critical review of drafts of this manuscript.
The aim of this work is to provide a fast and accurate behavior law for Ti-6Al-4V titanium alloy, useful for all work requiring knowledge of the behavior of the alloy within the range of strain rate between ̅ ̇= 0. 01 -1 and ̅ ̇= 1 -1 , the range of strain between ̅ = 0 and ̅ = 15 and a range of temperature between 1000°C and 1100°C. This work is part of an approach to simplify access to these types of behavior laws which often require very expensive material characterization equipment not always available in the laboratory. The behavior laws are necessary to carry out many simulations. For instance, when using a finite element software such as Abaqus, Forge Zset,. etc. , the researcher usually implements the behavior law point by point, to fit the hot behavior of the material as well as possible during loading.
Identification of parameters The model parameters have been identified to fit the flow curves of the current experiments. This minimization problem was solved using the Matlab® R2016a  [26]  fmincon function. fmincon resolves optimization problems by determining the minimum of a constrained linear or nonlinear multivariable function. The input values of the solver are the function to be minimized, the constraints, the initial solution and the algorithm to be used. The function to be minimized is the residual  between the predicted stress and the experimental data expressed in relation Eq. (17). { 𝛿 = ∑ (𝜎 𝑖 -𝐹 𝑖 ) 2 𝑖 𝐹 𝑖 = 𝑚 1 𝜀̅ 𝑖 𝑎 (1 + 𝜀̅ 𝑖 ) -𝑎 (𝟏 -𝒎 𝟐 𝒕𝒂𝒏𝒉 ( 𝜺 ̅ 𝒊 𝜺 𝒊𝒏𝒇 ⁄ )) (17)   and   represent respectively the experimental stress and the corresponding predicted stress for the experiment i.
Acknowledgements Authors acknowledge the  Vietnam International Education Development program  for a Ph. D. grant for T. P. T. Hoang. We are grateful to  Dr. Laurence Meslet - Cladière -University of Brest UBO  for the sequencing of the fungal strain,  Prof. Soizic Prado  - Natural History National Museum, and Dr Arnaud Bondon -University of Rennes  1 for NMR analyses and the  Corsaire-ThalassOMICS Metabolomics Facility (Biogenouest, University of Nantes, France)  for the help in HRMS/MS analyses and data interpretation.