Ania Alik

Chafiaa Bouguechtouli

Manon Julien

Dr Wolfgang Bermel

Dr Rania Ghouil

Dr Sophie Zinn-Justin

Francois-Xavier X Theillet
email: francois-xavier.theillet@cnrs.fr

Sensitivity-Enhanced 13C-NMR for Monitoring Multisite Phosphorylation at Physiological Temperature and pH

Keywords: Cell signaling, Phosphorylation, NMR spectroscopy, Mdm2, DNA-damage response

come   L'archive ouverte pluridisciplinaire

In eukaryotes, intracellular signal transduction largely relies on phosphorylation of intrinsically disordered regions of proteins (IDRs) [1] . More than 200,000 phosphosites have been detected on the ~13,000 human phosphoproteins [2,3] . Many of these phosphosites are clustered in unfolded regions of proteins, which conveniently permits to establish crosstalks or robustness in signaling [4,5] . Most of them have been detected by massspectrometry (MS) or western-blotting (WB). These popular methods are sensitive, but hardly provide a comprehensive characterization in common cases of multiple neighboring or degenerate phosphosites. High-resolution NMR spectroscopy has emerged as a complementary approach to decipher complex phosphorylation schemes: using the classical detection of 1 H- 15 N amide resonances, residue-specific information is obtained in 2D 1 H- 15 N correlation spectra, even on multiple phosphorylation sites that were not solved by MS/WB [6][7][8] . In frequent cases where 2D NMR peaks of IDRs overlap, 3D BT-HNCO combined with nonuniform sampling (NUS) shows superior capacities [9,10] . However, these approaches rely on amide protons detection, which is efficient for unfolded peptides at pH<7 and T<293K: because water-amide proton exchange rate is proportional to [OH -] at pH~7 and gets faster at high temperature [11] , 1 H- 15 N signals become weaker in physiological conditions where phosphorylation reactions are carried out, especially for fast-exchanging residues like Ser/Thr (Figure1a; Figure S1). Moreover, pH changes of ~0.1 can generate ~25% variations in the H-N crosspeak intensities, making them delicate to use when a quantitative analysis of phosphorylation reaction advancement is needed. Indeed, to obtain individual phosphorylation rates, time-series of 1 H- 15 N NMR spectra are recorded during the reaction in conditions where kinases are active, i.e. at pH=7 and 298K. Residue-specific peak intensities are then quantified in every spectrum to monitor disappearance or appearance of unphosphoor phospho-species, respectively [6][7][8] . The non-phosphopeaks (either from the phosphosites themselves or from neighboring residues) provide the most reliable information: reference intensities at 0% phosphorylation are known and permit a straightforward normalization. However, at physiological pH and temperature, disordered proteins generate overlapping and weak 1 H- 15 N signals, as mentioned earlier. The counterpart phosphopeaks display generally more favorable intensities because phosphate groups on the side chain slow down water-Scheme 1. Figure 1. (a) 1 H- 15 N 2D HSQC and (b) 13 Ca- 13 CO 2D ( 1 Hflip*) 13 Ca 13 CO-LB spectra of Mdm2(aa284-434) at pH7/283K and pH7.5/303K. Spectral areas of Gly, Ser, Thr resonances are highlighted in blue, those of Pro in black. The same contour levels are applied in all conditions. amide proton exchange, but exploiting them in a quantitative manner is often problematic: it requires normalization by peak intensities at 100% phosphorylation, a stoichiometry that is often impossible to reach using in vitro phosphorylation by purified kinases, especially when these are purchased commerciallycommonly at high cost. (Schemes illustrating these normalization issues are shown in Figure S2). To circumvent these drawbacks, we thought to switch to the observation of proton-less, 13 C-detected NMR correlations. This approach became appealing over the last years to characterize unfolded regions of proteins [12][13][14] . Still, notably because of the lower 13 C gyromagnetic ratio, it suffers from lower S/N than conventional 1 H-NMR. The 13 CO- 15 N correlation experiments are used to study IDRs because they offer the best crosspeaks dispersion, but necessitate high protein quantities to provide quality spectra (~500 µM in ~300 µL), which is not very adapted to phosphorylation studies: a whole batch of commercial kinase is usually necessary to phosphorylate ~100-1000 nmol of highpreference phosphosites. To reach a more favorable NMR sensitivity, we decided to build on the ( 1 H-flip) 13 Ca- 13 CO correlation experiment, the most sensitive 2D 13 C-detected pulse sequence [12] . This type of experiment is not spoiled by wateramide proton exchange, at the opposite of the 13 CO- 15 N experiments (Figure S3). 13 Ca- 13 CO experiments are usually thought to generate unresolved spectra showing abundant crosspeaks overlaps for unfolded peptides. However, using prolonged constant-time 13 Ca-evolutions, 13 Ca- 13 CO spectra can provide residue-specific information with high coverage along the peptide sequence, as we will see below. Hence, we thought to improve the ( 1 H-flip) 13 Ca- 13 CO sensitivity and adapt the sequence to our application. We first tuned the magnetization transfer delays (we named this first step 1 H-flip*, see Figure S4), sacrificing glycine resonances but generating a 15% increase for the rest of the amino acids. On average, this pays off in terms of phospho-and unphosphopeak intensities monitoring. Then, we replaced the final IPAP scheme by a homonuclear decoupling scheme LOW-BASHD applied during the acquisition (Figure S4) [15] . We also implemented a 120 µs triply compensated pulse G5 [16] to invert both carbonyl and alkyl 13 C nuclei in the final INEPT block. This yielded on average a 40% increase in S/N without any side-effects on peak line-width (Figure 2a, 2b), as demonstrated by Bax and colleagues [15] . It left the door open for implementing the preservation of equivalent pathways (PEP) routinely applied to 1 H- 15 N HSQC [17] . Despite multiple trials using various pulses and transfer delays (Figure S3), the best combination provided only a ~40% enhancement in signal, while also increasing the noise by Ö2 because the PEP protocol requires the acquisition of two independent datasets later recombined before processing. Finally, we thought to use chelated transition metals like Ni-DO2A for paramagnetic T1 relaxation enhancement, which is a potent way for NMR sensitivity improvement [18,19] . However, to preserve kinase activity, phosphorylation reactions require i) the presence of Mg 2+ , which can compete with DO2A-chelated divalent metal ions, and ii) reducing agents, which react with Ni-DO2A and most of the transition metals complexes that we tried. Among the commercial DOTA derivates available, NOTA is attractive because it is tailored to chelate trivalent transition metals, and thus not Mg 2+ . We found that the vanadium ion complex [V 3+ (NOTA 3-)] (Figure 2c) was the only one to remain stable in presence of Mg 2+ and DTT, as well as at high pH and temperature. 13 Ca 13 CO-LB with 0mM (blue) or 5mM (red) V-NOTA compared to ( 1 H-flip) 13 Ca 13 CO-IPAP (0mM V-NOTA); (b) Linewidths of isolated peaks in 2D ( 1 H-flip*) 13 Ca 13 CO-LB with 0mM (blue) or 5mM (red) V-NOTA compared to ( 1 Hflip) 13 Ca 13 CO-IPAP (0mM V-NOTA). (c) Chemical structure of V 3+ chelated by NOTA 3-; (d) Sensitivity and time efficiency: average S/N per Ötscan as measured in 1D ( 1 H-flip) 13 Ca 13 CO-IPAP (black) and ( 1 H-flip*) 13 Ca 13 CO-LB with 0mM (blue), 1.5mM (violet), 3mM (purple) and 5mM (ping) V-NOTA.

It provided up to a 25% increase in S/N for the ( 1 H-flip*) 13 Calinewidths (Figure 2). Fast pulsing with low interscan delays (~0.1s) provides the best S/N per experimental time (Figure 2d). Altogether, using a classical cryoprobe optimized for "indirect" 1 Hdetection, a 13 Ca constant-time evolution of 27 ms and 50% NUS [20,21] , we can record in 30 minutes high-resolution 2D 13 Ca- 13 CO correlation spectra providing residue-specific information on IDRs at 50 µM and at any pH or temperature. This can be translated into 25 µM for 13 C-direct detection probes. The resulting resolution is often sufficient to get residue-specific information along most of the disordered peptide sequences. To cancel CaCb coupling in 13 Ca dimension, a 13 Ca constant-time evolution of 27, 54 or 81 ms must be used. In case of important peak overlaps, an increased resolution is achievable using a 13 Ca evolution time of 54 ms, which provides an average 12 Hz linewidth in 13 Cadimension but provokes about 30% losses in S/N (8 Hz linewidth using an evolution time of 81 ms, Figure S5). With this dedicated analysis tool in hands, we then attempted to characterize challenging phosphorylation reactions that we failed to monitor accurately using only 1 H- 15 N experiments. This was the case for human Mdm2, an E3-ubiquitin ligase controlling the abundance of the tumor suppressor p53 [22,23] . Mdm2 activity is regulated by DNA-damage response (DDR) kinases DNA-PK, ATM or ATR [24,25] . According to the literature, they phosphorylate Mdm2 on 6 sites of its C-terminal region (aa334-434) [24][25][26] , among which mouse S394 (human S395) phosphorylation has consequences on p53 stabilization and DNA-damage signaling. [26][27][28] To investigate the structural mechanism behind this functional switch, we had first to map comprehensively the phosphorylation scheme established by the DDR kinases. We used the segment Mdm2(aa284-434) comprising the region Mdm2(aa334-434) that we characterized as intrinsically disordered (Figure S6). After a prolonged reaction at 298K in the presence of purified DNA-PK, 2D 1 H-15 N spectra of Mdm2(aa284-434) recorded at 283K revealed the phosphorylation endpoint (Figure 3): we identified 8 phosphosites T351, S386, S388, S395, S402, S407, T419 and S429. Initially, we could determine T351, S395 and S429 phosphorylation rates by recording time series of 1 H-15 N SOFAST-HMQC NMR experiments during the reaction at 298K [29] . In the cases of S386, S388, S402, S407 and T419, we were unable to quantify accurately their kinetics because of 1 H-15 N non-phosphopeaks overlapping or being even non-detectable for S402 and S407. Together with their uncomplete phosphorylation, these prevented the determination of reference 1 H-detected peak intensities at 0% and 100% phosphorylation. Here, previously published 1 H-detection methods [6][7][8][9][10] found their limits. Then, we recorded times series of alternated 1 H-15 N SOFAST-HMQC and ( 1 H-flip*) 13 Ca 13 CO-LB experiments during the reaction at 298K. These provided complementary information: 1 H-15 N SOFAST-HMQC spectra reported phosphopeaks build-up curves at high S/N, which we normalized using ( 1 Hflip*) 13 Ca 13 CO-LB non-phosphopeaks intensities and their decay curves. We found exploitable non-phosphopeak reporters for S386, S388, S402, S407 and T419 in 13 Ca- 13 CO spectra at 298K (Figure 3, see the Material & Methods for a detailed list, a scheme of the rationale is presented in Figure S2). Using these peaks, we quantified and ranked DNA-PK's site preferences on Mdm2 (Figure 4a). Then, we tested our ability to monitor phosphorylation in cell extracts. Our approach mixing 1 H-15 N SOFAST-HMQC and ( 1 H-flip*) 13 Ca 13 CO-LB spectra worked in cell extracts containing from 0.2 to 5 mg/mL of cellular proteins: the phosphorylation order observed with purified DNA-PK was very similar in HeLa cells Nuclear Extract that were DNA-damaged by short sonication pulses. Thus, our site-specific absolute quantification revealed i) that S388 is a high-preference novel phosphosite, and T351 and S402 are two new lower preference sites, ii) that S429 and S402, which do not correspond to the DDR kinases S-T/Q consensus motif, were steadily modified, iii) that T383 and T419, two S-T/Q consensus sites, were not or slowly phosphorylated, respectively. Similarly, we monitored site-specific phosphorylation on two other disordered segments of nuclear proteins involved in genome maintenance and expression, namely the Breast Cancer type 2 susceptibility protein (BRCA2 aa190-284, see [30] ) and the pluripotency transcription factor Oct4 (aa1-145) (Figure S7). Our characterization of BRCA2 phosphorylation by Plk1 (pH7.8, T=303K), an important mitotic event, permitted to quantify properly two novel phosphosites T219 and T226, which were modified with rates comparable to the already known S193 and T207 (Figure 5a,c). We also wanted to examine Oct4 phosphorylation by MAP kinases, which regulates Oct4 transcriptional activity [31] . In presence of p38a (pH7.4, T298K), we noticed that the classical MAPK target motifs Ser/Thr-Pro S12, S111 and T118 were modified slower than the non-Ser/Thr-Pro T92 (Figure 5b,d). Here, we bring attention to the fact that 13 Ca 13 CO correlation are very adapted to the study of proline-rich peptides, the residues in N-terminal of Pro showing strong upfield shifts and interesting chemical shift dispersion (see also Figure S8). This is of interest, since the abundant Ser/Thr-Pro motifs are targets of the essential activities of MAP-Kinases and Cyclindependent kinases [32] . Because this method is not limited by temperature conditions, it may also help in studies focusing on temperature-sensitive activities of kinases involved in circadian rhythm or hyperthermia consequences [33,34] . Hence, our improved 13 C-detected NMR approach expands the range of peptides and conditions accessible to NMR phospho-mapping and -monitoring. NMR methods previously published for PTM characterization are based on amide 1 H-detection, which often provides the best sensitivity and possibly higher dimensionality/resolution, using fast acquisition and time-resolved NUS [6][7][8][9][10] . The proposed 13 Cdetection scheme makes it possible to obtain residue-specific information with high sequence coverage on disordered proteins by NMR in all conditions of pH and temperature. Overlays of ( 1 H-flip*) 13 Ca 13 CO-LB spectra of (a) BRCA2(aa190-284) and (b) Oct4(aa1-145) before (black) and after (red) phosphorylation by Plk1 and p38a, respectively. Spectral area corresponding to crosspeaks of Ser/Thr/Gly-Pro motifs are in blue. Kinetics were derived from time-series of ( 1 H-flip*) 13 Ca 13 CO-LB spectra of (c) BRCA2(aa190-284) at 100 µM, pH7.8, T=303K (d) Oct4(aa1-145) at 100 µM, pH7.4, T=298K at 700MHz using a Bruker-TCI probe.

Experimental section

The Supporting Information contains detailed Material and Methods, HN/CaCO/CON NMR spectra of a-Synuclein, Elk1 and BRCA2 fragments at various pH, temperature and resolution, pulse sequence schemes, proteins' primary sequences presentations.

Entry for the Table of Contents

We present an NMR method to monitor site-specific phosphorylation at high pH/T, which are necessary for kinases activity but prevented thorough characterization of disordered proteins. Our approach uses sensitivity-enhanced 13 Ca- 13 CO experiments. We detail their improvements and efficiency on Mdm2, BRCA2, Oct4. Much more phosphorylation cases are now investigable by NMR.

HeLa Nuclear Extract production

HeLa S3 cells were grown in DMEM (4.5 g/L glucose, Sigma) supplemented with 10 % FBS. 100 million cells were harvested by trypsinization and lysed using the Qproteome Mitochondria isolation kit (Qiagen). We kept the nuclear fraction and resuspended it in 500 µL of Hepes 50 mM, NaCl 400 mM, glycerol 10%, protease inhibitors 1x (EDTA-free cOmplete, Roche), 2 mM DTT and phosphatase inhibitors 1x (PhoSTOP, Roche), pH7. We submitted it to three 5s pulses of sonication using a microtip and a 30% output power (VibraCell, Sonics & Material In. Danbury, Connecticut, USA). The extract was aliquoted and stored at -80°C. This protocol yields extracts at 5 to 10 mg/mL protein concentration. The final working concentration used for the presented figures is 0.5 mg/mL.

NMR spectroscopy

All NMR spectra were recorded on a 700 MHz Bruker Avance Neo spectrometer, equipped with a cryogenically cooled triple resonance 1 H[ 13 C/ 15 N] probe optimized for 1 H detection (TCI Bruker from 2006: 13 C ASTM S/N=850, 1 H 0.1% ethylbenzene S/N=7260 when delivered; actual 13 C-optimized probes ASTM S/N specifications are about 2500). All spectra were processed in Topspin 4.0.4. 3D spectra analysis was carried out using CccpNmr 2.4.2, and 2D spectra were analyzed using NMRFAM-SPARKY. [3] DSS at 100 µM and 3% D2O were added in all samples.

NMR assignments of backbone amide resonances of uniformly-labeled peptides ( 13 C/ 15 N) was achieved via BEST-HNCO, -HN(CA)CO, -HNCACB, [4] (H)N(CA)NH and HNCA 3D experiments, at pH 6.8 and 283K, and at peptide concentrations ranging from 200 to 500 µM in 5 mm diameter Shigemi tubes. HNCO N). Non-uniform sampling was applied (35 to 50%) and processing was achieved using RMDD algorithm implemented in Topspin4.0.4. [5] [6] Spectra were processed with linear prediction of 16 complex points in both 13 C, and 15 N dimensions, cosine apodization in every dimensions, and zero filling to 2048, 512 and 256 complex points in 1 H, 13 C, and 15 N dimensions, respectively. Assignment 2D 1 H-15 N HSQC spectra were recorded using 1536 ( 1 H) x 256 ( 15 N) complex points and sweep widths of 16.23 ppm ( 1 H) and 30 ppm ( 15 N), and processed with zero filling to 4K and 1K in the proton and nitrogen dimensions, respectively. We used this assignment strategy for backbone amide resonances (BEST-HNCO, -HN(CA)CO, -HNCACB, (H)N(CA)NH and HNCA 3D experiments, at 283K) both for non-modified and phosphorylated species. The assignments at 303K in 2D spectra were transferred from those at 283K by recording spectra at 283K, 293K and 303K: this is sufficient to characterize peak-specific temperature shifts. The spectra and the assignment of "DNA-PK intermediate" phosphorylated Mdm2(aa284-434) were obtained using samples whose phosphorylation was stopped by heating at 323K for 10 minutes (provoking DNA-PK inactivation). Assignments of unmodified and phosphorylated human Mdm2(aa284-434, C362A-C374A), as well as details of NMR experiments used to derive them have been deposited in the Biological Magnetic Resonance Data Bank (BMRB, accession numbers 28011, 28019, respectively). As reported in the literature by Bycroft et al. [7] , we observed two stable conformations of the Zn-finger that correspond to trans-and cis-prolines (PDB: 2C6A and 2C6B). The relative NMR peak intensities of the two species were the same in the isolated Zn-finger and in Mdm2(aa284-434) (trans:cis~2:1). Secondary structure propensities of unmodified and phosphorylated Mdm2(aa284-434, C362A-C374A) were obtained with the neighbor-corrected structural propensity calculator ncSCP [8,9] (http://www.protein-nmr.org/ , https://st-protein02.chem.au.dk/ncSPC/) using experimentally determined, DSS referenced Ca and Cb chemical shifts as input. We set up 2D spectra during phosphorylation kinetics as follows: 2D 1 H-15 N SOFAST-HMQC experiments were recorded using 1536 ( 1 H) x 92 ( 15 N) complex points and sweep widths of 16.23 ppm ( 1 H) and 30 ppm ( 15 N), 16 scans (128 dummy scans) and interscan delays of 0.12 s, resulting in 7 minutes long spectra; 2D ( 1 H-flip*) 13 Ca 13 CO-LB experiments were recorded using 1024 ( 13 CO) x 128 ( 13 Ca) complex points and sweep widths of 20.28 ppm ( 13 CO) and 14 ppm ( 13 Ca), 80 scans (96 dummy scans) and interscan delays of 0.15 s, and 50% non-uniform sampling optimized for a 35 Hz J-coupling (i.e. low sampling around 14 ms Ca evolution time) and 60 ms relaxation, resulting in 31 minutes long spectra. All spectra were processed zero filling to 4K and 1K in the direct and indirect dimensions, respectively. Cosine apodization was applied in 13 CO dimension for ( 1 H-flip*) 13 Ca 13 CO-LB spectra, no apodization was applied for 1 H-15 N SOFAST-HMQC spectra. 2D ( 1 H-flip*) 13 Ca 13 CO-LB experiments processing was achieved using the CS algorithm implemented in Topspin4.0.4 [10] , parameters were set as follows: Mdd_mode=cs, MddCEXP=false, MddCT_SP=true, Mdd_CsALG=IST, Mdd_CsVE=false, Mdd_CsNITER=0. Two 2D 1 H-15 N SOFAST-HMQC spectra were initially acquired, before recording alternately 2D ( 1 H-flip*) 13 Ca 13 CO-LB and 2D 1 H-15 N SOFAST-HMQC spectra. We used 5 mm diameter Shigemi advanced tubes for phosphorylation kinetics, which increases NMR active volume to 330 µL, and yields ~13 % higher 13 C-direct detection sensitivity, and no 1 H-dectection gains or losses.

Phosphorylation reactions

Phosphorylation reactions were carried out using 15 N-labeled target peptides concentrations between 12.5 and 100 µM, in Hepes 40 mM, NaCl 75 mM, DTT 2 mM, ATP 5 mM, MgCl2 20 mM, protease inhibitors (Roche), 3% D2O, pH 7.2 to pH 7.8 at 25°C or 30°C in 330 µL. The spectra and kinetics shown in all figures are from kinetics run with 100 µM Mdm2, Oct4, Elk1 or BRCA2 fragments. No changes in phosphorylation preferences were observed with concentration changes. Mdm2 phosphorylation by purified DNA-PK was achieved using commercial DNA-PK (ThermoFisher Scientific / Invitrogen): 2.5 µg of DNA-PK and 35 µg of DNA-PK activator (ThermoFisher Scientific / Invitrogen), i.e. calf thymus DNA, were spiked in the sample immediately before inserting it in the NMR spectrometer. Regular calf thymus DNA concentrations of 2.5 µg/mL were not reliable enough: initial phosphorylation rates were decreasing with Mdm2 concentration increasing, which goes against kinetics theory and probably reveals an interaction between DNA and Mdm2. We saturated the system with 100 µg/mL of Calf thymus DNA to have constant DNA-PK activation. Moreover, we observed that satisfying reproducibility of DNA-PK phosphokinetics is achievable at 298K, but that this kinase complex appears less stable at 303K, resulting in variable phosphorylation results. BRCA2 kinetics were run at 50 and 100 µM, in hepes 50 mM, NaCl 50mM, pH 7.8 in presence of 250 nM of recombinant Plk1, 2 mM ATP, 1 mM EDTA, 20 mM MgCl2, 2 mM DTT, 1x protease inhibitors cocktail cOmplete (Roche), and 3 mM V-NOTA (Plk1 aliquots gently provided by the protein production platform of the Institut Curie, Paris, France). Oct4 kinetics were run at 50 and 100 µM, in hepes 20 mM, NaCl 50mM, pH 7.4 in presence of 500 nM of recombinant p38a, 2 mM ATP, 5 mM MgCl2, 2 mM DTT 1x protease inhibitors cocktail cOmplete (Roche) using recombinant p38a produced and activated in-house (see the paragraph "Peptide constructs"). Elk1 phosphorylation was performed using recombinant human Erk2 produced and activated in-house (see the paragraph "Peptide constructs").

Phosphorylation kinetics analysis

After processing spectra as detailed in the paragraph "NMR spectroscopy", we measured peak intensities in NMRFAM-SPARKY. [3] Peaks were centered in every spectra to follow peak shifting because of pH drifts. Progress curves were plotted and fitted in Kaleidagraph 4.5. We used only decay curves to normalize phosphorylation build-up curves. We describe the set of disappearing peaks that we used in the following paragraph. About Mdm2(aa284-434), we could determine pT351, pS395 and pS429 phosphorylation rates using peaks from 2D 1 H-15 N SOFAST-HMQC NMR spectra. Indeed, we monitored peak intensities of neighboring residues A345+L347 for pT351, Y394+S395 for pS395, L430 for pS429, which all disappear proportionally to the phosphorylation advancement of their closest phosphosite. The other phosphosites (S386, S388, S402, S407, T419) were monitored using 2D ( 1 H-flip*) 13 Ca 13 CO-LB spectra as follows. D380, K381, I382 and T383 peaks are good reporters of the concomitant disappearance of non-modified S386 and S388. Even more valuable, a second set of intermediate I382 and T383 peaks appear corresponding to S386/pS388, while a third one report the progressive buildup of pS386/S388+pS386/pS388. In the case of I382 and T383, pS388 does not provoke any further peaks shifting after S386 phosphorylation. At the opposite, ( 1 H-flip*) 13 Ca 13 CO-LB peaks of Q384, Q387 and Q387 split in 4 different forms corresponding to the 4 combinations of S386-pS386-S388-pS388, but their interpretation is more tedious because of peak overlaps (Figure 3). Peaks of S402, I403, I404, Y405, S406 and Q408 are good reporters of S407 disappearance, i.e. pS407 appearance, and S398, T399, S402 and I404 of the later appearance of pS402. Finally, E415, R416 and T419 are good reporters of the pT419 reaction advancement. Intensities of peaks that are reporter of a phosphorylation reaction were added to increase the experimental S/N. Decay curves were fitted according to the following equations: y(t)=I0*exp(-k*t)+Ifinal with I0 and k the fitted values. Normalization was performed using (I0+ Ifinal), and phosphorylation build-up curves are thus %phospho(t)=1-y(t)/ (I0+ Ifinal). We also used phosphopeaks of pS386, pS388, pS402, pS407 and pT419 observed in 2D 1 H-15 N SOFAST-HMQC NMR spectra, which are well-separated and identifiable in 2D 1 H- 15 N spectra. We normalized them according to the final levels reached at the end of the kinetics, as determined from ( 1 Hflip*) 13 Ca 13 CO-LB decay curves. Proton-detected SOFAST-HMQC experiments still provide higher S/N and permit to generate higher quality build-up curves. BRCA2(aa190-284) phosphokinetics were derived from ( 1 H-flip*) 13 Ca 13 CO-LB peak intensities of M192, M192(pS193), S193, W194 and S196 for pS193, T207, pT207, V208, V208(pT207), I210, I210(pT207) and V211 for pT207, A216, T219, pT219 and V220 for pT219, and from H223, A226 and V229 for pT226. Oct4(aa1-145) phosphokinetics were derived from ( 1 H-flip*) 13 Ca 13 CO-LB peak intensities of D8, D8(pS12), F9, F9(pS12), A10, A10(pS12), and S12 for pS12, L90, T92 and S93 for pS93, D108, A110, S111 and E113 for pS111, T116, T116(pT118), V117, T118, A121 and V122 for pT118.

Pulse program

We provide two versions of the ( 1 H-flip*) 13 Ca 13 CO-LB Bruker pulse program: one for Neo consoles and Topspin4 (c_hcaco_ctre.lowbash.top4), one for earlier systems and Topspin 3 (c_hcaco_ctre.lowbash.top3). They are straightforward to parameterized: only the proton pulse length (p3) has to be determined for each sample, while carbon pulse lengths can be set up only once. The carbon homo-decoupling LOW-BASH pulse is the only pulse that is delicate to parameterize: one should follow carefully the indications provided by Bax and coworkers [11] . The Shapetool in Topspin4 permits to generate the necessary shape pulse: a center lobe of a sinc function of duration 22,600/nc in µs (nc is the 13C frequency in MHz) should be amplitude modulated by a cosine function at a frequency of 118 x nc. Hence, it applies pulses of equivalent power on both sides of the 13 CO carrier frequency, centered at 55 and 191 ppm, which efficiently vanishes Bloch-Siegert shift effects [11] . This decoupling pulse is automatically applied every 5 ms by the pulse program, as well as the MLEV16 RF phases. We provide the shape pulse Bruker file for a 700 MHz spectrometer as an example in Supplementary data. Phosphorylated residues generally provide lower peak intensities than their non-phospho counterparts, due to the difficulties to excite and decouple selectively Ca and Cb of pSer and pThr. It is important to use a low D2O concentration to avoid high amounts of deuterated amides, which generate significant changes in chemical environment for the neighboring Ca and CO, hence provoking peak splitting or measurable exchange relaxation. We use 3% D2O.

Results and Discussion

Figure S1. 1 H-15 N 2D HSQC and 13 Ca- 13 CO 2D ( 1 H-flip*) 13 Ca 13 CO-LB spectra of alpha-synuclein, Elk1(aa308-428) and Brca2(aa48-218) in low pH and low temperature or high pH and high temperature conditions. The same acquisition parameters and contour levels were applied in both conditions. Positive and negative crosspeaks are in black and red, respectively. Negative peaks in red correspond to Gly residues. Analysis pipeline for data fitting using non-phosphopeaks or phosphopeaks from NMR spectra of Mdm2(aa284-434). a. In the case where nonphosphopeaks (from a phosphosite or its neighboring residues) are well-resolved and provide sufficient S/N, intensities measured in every spectrum of the time series can be plotted. They are then fitted using an exponential function I(t)=I(t=0)*exp(-t/k)+I(tfinal), the fitted parameters being I(t=0), k and I(tfinal). Curves are then normalized by I(t=0)+I(tfinal) and %phosphorylation(t)=1-Inormalized(t). b. In the case where only phosphopeaks are well-resolved and provide sufficient S/N, intensities from phosphopeaks can be plotted, but not normalized, unless 100% phosphorylation is reached. c. Intensities from non-phosphopeaks can be measured in ( 1 Hflip*) 13 Ca 13 CO-LB spectra and the resulting curves can be fitted and used for normalization of both phosphopeaks and non-phosphopeaks build-up curves.

Figure S3

. 15 N- 13 CO 2D ( 1 H-flip*)CON-IPAP [12] spectra of Mdm2(aa284-434) and alpha-synuclein at low pH and low temperature or high pH and high temperature conditions. The same acquisition parameters and contour levels were applied in both conditions. Negative peaks in red correspond to Gly residues. 13 Ca 13 CO-LB_2 and (c) ( 1 H-flip*) 13 Ca 13 CO-LB_3 pulse sequences. Pulse lengths are adapted to a spectrometer field of 700 MHz. Delays are: d=2.9 ms, D=9 ms, D'= 8.4 ms, D 1=27*n ms with n=1, 2 or 3. Phase cycle: f1=x,x,x,x,-x,-x,-x,-x; f 2=x, -x; f 3=x, x, -x, -x; f rec=x,-x,-x,x; fn=x,x,-x,-x,-x,x,x,-x,-x,-x,x,x,x,-x,-x,x. Filled and open rectangles represent hard 90° and 180° pulses, respectively. Narrow and wide black semi-ellipses are Q5-sebop/Q5tr-sebop 90° (231 µs) and Q3-surbop 180° (300 µs) pulses, respectively. 13 CO and 13 Ca/b and 13 Cali carrier frequencies are 173, 55.5 and 41 ppm, respectively. White semi-ellipses are 13 Ca centered Q3-surbop 180° (634 µs) pulses applied at 41 ppm. The striped semi-ellipse is a 120 µs triply compensated pulse G5 [13,14] applied at 114.25 ppm. The shaded semi-ellipse is a Q3-surbop 180° (900 µs) pulse applied at 114.25 ppm, amplitude modulated by a cosine function at a frequency of 58.75*nc (nc is the 13 C frequency in MHz), which inverts selectively both 13 CO and 13 Ca/b at the same time. The grey semi-ellipse is a center lobe of a sinc function of duration 22,600/nc in µs (nc is the 13 C frequency in MHz), amplitude modulated by a cosine function at a frequency of 118*nc applied n times every 5 ms, with n=tacq/5ms. Quadrature detection is obtained by incrementing phase f2 in a States-TPPI manner. The original ( 1 H-flip*) 13 Ca 13 CO-IPAP sequence uses a pulse field gradient just before the carrier frequency change to 13 CO before the final INEPT transfer. We chose not to apply any pulsed field gradients (Gz), because we did not observe any improvement related to it.

Figure S5.

( 1 H-flip*) 13 Ca 13 CO-LB spectra of alpha-synuclein using growing constant-time (CT) evolution periods of 27, 54 or 81 ms. These correspond to n/ 1 J(CaCb) with n=1, 2 or 3. This allows the removal of 1 J(CaCb) =35 Hz coupling effects during Ca chemical shift evolution. Increasing the constant time by 27 ms provokes on average a 30% loss in S/N for IDPs. It generates at the same time an improved resolution: we measured an average 12 Hz linewidth in Ca-dimension at 54 ms and 8 Hz at 81 ms. In these spectra, the signs of NMR signals oscillate according to J-coupling evolution: they are negative for cos(pJ*CT)=-1, i.e. CT=27 or 81 ms, and positive for cos(pJ*CT)=1, i.e. CT=54 ms. Gly residues are an exception, because they do not contain any Ca and their signal is always positive. We have changed the phase by 180° so that most of the peaks appear to be positive (black), and those of Gly residues appear negative at CT=27 or 81 ms. alpha-synuclein pH7/283K t evol ( 13 C α )=27ms t evol ( 13 C α )=54ms t evol ( 13 C α )=81ms 13 Cα(ppm) 13 Cα(ppm) 13 C O (ppm) 13 C O (ppm) motifs not yet detected to be DDR kinases targets are in light red circles. [15] (b) Secondary structure propensity as calculated from 13 Ca/ 13 Cb chemical shifts using ncSCP. [8,9] (c) Low 1 H-15 N heteronuclear nOes (700 MHz) reveal ps-ns timescale motion typical of unfolded peptides outside of the Zn-fg domain.

p53-BD RING 13 Ca/ 13 Cb chemical shifts using ncSCP. [8,9] Gly and Pro positions along the primary sequence are indicated by red and blue sticks, respectively.

Figure 2 .

 2 Figure 2. Performances of the ( 1 H-flip*) 13 Ca 13 CO-LB sequence, as evaluated on the model protein a-synuclein. (a) S/N of well-separated peaks in 2D ( 1 H-flip*)13 Ca 13 CO-LB with 0mM (blue) or 5mM (red) V-NOTA compared to ( 1 H-flip)13 Ca 13 CO-IPAP (0mM V-NOTA); (b) Linewidths of isolated peaks in 2D ( 1 H-flip*)13 Ca 13 CO-LB with 0mM (blue) or 5mM (red) V-NOTA compared to ( 1 Hflip)13 Ca 13 CO-IPAP (0mM V-NOTA). (c) Chemical structure of V 3+ chelated by NOTA 3-; (d) Sensitivity and time efficiency: average S/N per Ötscan as measured in 1D ( 1 H-flip)13 Ca 13 CO-IPAP (black) and ( 1 H-flip*)13 Ca 13 CO-LB with 0mM (blue), 1.5mM (violet), 3mM (purple) and 5mM (ping) V-NOTA.

Figure 3 .

 3 Figure 3. Phosphorylation of human Mdm2(aa284-434) by DNA-PK observed by NMR. (a) Overlay of 2D 1 H-15 N HSQC spectra at 283K before (black) and after (red) the phosphorylation reaction. (b) Overlay of 2D ( 1 H-flip*)13 Ca13 CO-LB at 303K before (black), in the course (light blue) and at the end (red) of the phosphorylation reaction. At the right: Close-up views of Gln, Ile, and Thr regions. Arrows show peak displacements upon 2 neighbor phosphorylation events: certain sets of peaks report for the 4 phosphostates, e.g. Q384(S386-S388)/Q384(pS386-S388)/Q384(S386-pS388)/Q384(pS386-pS388); in contrast I382(pS388) and T383(pS388) report the appearance of Mdm2(S386-pS388), while I382(pS386) and T383(pS386) both report Mdm2(pS386-S388)+Mdm2(pS386-pS388) (see Experimental Procedures in Supp. Info. for the assignment strategy).

Figure 4 .

 4 Figure 4. Site-specific phosphorylation kinetics of Mdm2(aa284-434). Build-up curves of phosphorylation reactions as executed (a) by purified DNA-PK or (b) in HeLa cells Nuclear Extract; The phosphosite labels are in line with their corresponding curves. Non-conserved sites are in black, conserved S/T-Q sites in red, non-S/T-Q sites in orange.

Figure 5 .

 5 Figure 5. Phosphorylation as observed by 2D ( 1 H-flip*) 13 Ca 13 CO-LB NMR.Overlays of ( 1 H-flip*)13 Ca 13 CO-LB spectra of (a) BRCA2(aa190-284) and (b) Oct4(aa1-145) before (black) and after (red) phosphorylation by Plk1 and p38a, respectively. Spectral area corresponding to crosspeaks of Ser/Thr/Gly-Pro motifs are in blue. Kinetics were derived from time-series of ( 1 H-flip*)13 Ca 13 CO-LB spectra of (c) BRCA2(aa190-284) at 100 µM, pH7.8, T=303K (d) Oct4(aa1-145) at 100 µM, pH7.4, T=298K at 700MHz using a Bruker-TCI probe.

Figure S2.

  Figure S2.Analysis pipeline for data fitting using non-phosphopeaks or phosphopeaks from NMR spectra of Mdm2(aa284-434). a. In the case where nonphosphopeaks (from a phosphosite or its neighboring residues) are well-resolved and provide sufficient S/N, intensities measured in every spectrum of the time series can be plotted. They are then fitted using an exponential function I(t)=I(t=0)*exp(-t/k)+I(tfinal), the fitted parameters being I(t=0), k and I(tfinal). Curves are then normalized by I(t=0)+I(tfinal) and %phosphorylation(t)=1-Inormalized(t). b. In the case where only phosphopeaks are well-resolved and provide sufficient S/N, intensities from phosphopeaks can be plotted, but not normalized, unless 100% phosphorylation is reached. c. Intensities from non-phosphopeaks can be measured in ( 1 Hflip*)13 Ca 13 CO-LB spectra and the resulting curves can be fitted and used for normalization of both phosphopeaks and non-phosphopeaks build-up curves.

Figure S4 .

 S4 Figure S4. Schemes of the (a)( 1 H-flip*)13 Ca13 CO-LB_1, (b) ( 1 H-flip*)13 Ca13 CO-LB_2 and (c) ( 1 H-flip*)13 Ca13 CO-LB_3 pulse sequences. Pulse lengths are adapted to a spectrometer field of 700 MHz. Delays are: d=2.9 ms, D=9 ms, D'= 8.4 ms, D 1=27*n ms with n=1, 2 or 3. Phase cycle: f1=x,x,x,x,-x,-x,-x,-x; f 2=x, -x; f 3=x, x, -x, -x; f rec=x,-x,-x,x; fn=x,x,-x,-x,-x,x,x,-x,-x,-x,x,x,x,-x,-x,x. Filled and open rectangles represent hard 90° and 180° pulses, respectively. Narrow and wide black semi-ellipses are Q5-sebop/Q5tr-sebop 90° (231 µs) and Q3-surbop 180° (300 µs) pulses, respectively.13 CO and13 Ca/b and13 Cali carrier frequencies are 173, 55.5 and 41 ppm, respectively. White semi-ellipses are13 Ca centered Q3-surbop 180° (634 µs) pulses applied at 41 ppm. The striped semi-ellipse is a 120 µs triply compensated pulse G5[13,14] applied at 114.25 ppm. The shaded semi-ellipse is a Q3-surbop 180° (900 µs) pulse applied at 114.25 ppm, amplitude modulated by a cosine function at a frequency of 58.75*nc (nc is the13 C frequency in MHz), which inverts selectively both13 CO and13 Ca/b at the same time. The grey semi-ellipse is a center lobe of a sinc function of duration 22,600/nc in µs (nc is the13 C frequency in MHz), amplitude modulated by a cosine function at a frequency of 118*nc applied n times every 5 ms, with n=tacq/5ms. Quadrature detection is obtained by incrementing phase f2 in a States-TPPI manner. The original ( 1 H-flip*)13 Ca 13 CO-IPAP sequence uses a pulse field gradient just before the carrier frequency change to13 CO before the final INEPT transfer. We chose not to apply any pulsed field gradients (Gz), because we did not observe any improvement related to it.

Figure S6 .

 S6 Figure S6. (a)Primary structure of human Mdm2 and Mdm2(aa284-434). Folded domains (p53-BindingDomain: p53-BD; Zinc-finger: Zn-fg; RING-ubiquitin ligase domain: RING) are in deep blue and domains predicted to be disordered are in light-blue. DNA-PK/ATM/ATR (DDR kinases) were reported to phosphorylate sites depicted in solid red or orange circles, representing consensus S/T-Q motifs or non-S/T-Q motifs, respectively. S/T-Q motifs not yet detected to be DDR kinases targets are in light red circles.[15] (b) Secondary structure propensity as calculated from13 Ca/13 Cb chemical shifts using ncSCP.[8,9] (c) Low 1 H-15 N heteronuclear nOes (700 MHz) reveal ps-ns timescale motion typical of unfolded peptides outside of the Zn-fg domain.

Figure

  Figure S7. (a) Primary structure of human Oct4 and Oct4(aa1-145). Folded domains (DNA-BindingDomain: DNA-BD or DBD) are in deep blue and domains predicted to be disordered are in light-blue. (b) 2D 1 H-15 N HSQC spectrum of Oct4(aa1-145) showing that most crosspeaks are in the region (in grey) where resonances of unfolded amino acids are found. (c) Secondary structure propensity as calculated from13 Ca/13 Cb chemical shifts using ncSCP.[8,9] Gly and Pro positions along the primary sequence are indicated by red and blue sticks, respectively.

  and HN(CA)CO were carried out with 1536 ( 1 H) x 92 ( 13 C) x 80 ( 15 N) complex points and sweep widths of 16.23 ppm ( 1 H), 10 ppm ( 13 C) and 30 ppm ( 15 N), HNCACB 1536 ( 1 H) x 92 ( 13 C) x 80 ( 15 N) complex points and sweep widths of 16.23 ppm ( 1 H), 70 ppm ( 13 C) and 30 ppm ( 15 N), (H)N(CA)NH with 1536 ( 1 H) x 64 ( 13 C) x 64 ( 15 N) complex points and sweep widths of 16.23 ppm ( 1 H), and 30 ppm ( 15 N), and HNCA 1536 ( 1 H) x 192 ( 13 C) x 80 ( 15 N) complex points and sweep widths of 16.23 ppm ( 1 H), 26 ppm ( 13 C) and 30 ppm ( 15

Acknowledgements

This work was supported by CNRS and CEA-Saclay, by the French Infrastructure for Integrated Structural Biology (https://www.structuralbiology.eu/networks/FRISBI, grant number ANR-10-INSB-05-01, Acronym FRISBI) and by the French National Research Agency (ANR; research grant ANR-14-ACHN-0015). We thank the protein production facility of the Institut Curie for providing the purified kinase Plk1.

Ania Alik [a]# , Chafiaa Bouguechtouli [a]# , Manon Julien [a]# , Wolfgang Bermel [b] , Rania Ghouil [a] , Sophie Zinn-Justin [a] , Francois-Xavier Theillet* [a] Abstract: Abundant phosphorylation events rule the activity of nuclear proteins involved in gene regulation and DNA repair. These occur mostly on disordered regions of proteins, which often contain multiple phosphosites. Comprehensive and quantitative monitoring of phosphorylation reactions is theoretically achievable at a residue-specific level using 1 H-15 N NMR spectroscopy, but is often limited by low signal-to-noise at pH>7 and T>293K. We developed an improved 13 Ca- 13 CO correlation NMR experiment that works equally at any pH or temperature, i.e. also at those where genuine kinase activities occur. This permits to obtain atomic-resolution information in physiological conditions down to 25 µM. We exemplified the interest of this approach by monitoring phosphorylation reactions, in presence of purified kinases or in cell extracts, on a range of previously problematic targets, namely Mdm2, BRCA2 and Oct4. Virus Protease (Tev) cleavage site ENLYFQG on the N-terminal side of the peptide of interest. Oct4(aa1-145) was also followed on the C-terminal side by a Tev cleavage site and a GB1 tag to stabilize the construct during recombinant expression. The Tev construct contained a hexahistidine tag (His6), and was produced in-house recombinantly in Escherichia coli.

Recombinant protein production

All peptides were produced in BL21(DE3)Star E. coli transformed with the plasmids presented above. BRCA2(aa190-284) was produced as previously described [1] . Alpha-synuclein was produced in cells co-transformed with the plasmid encoding NatB following protocols published earlier. [2] Cells were grown in M9 medium containing 13 C-glucose (2 g/L) and 15 NH4 + (0.5 g/L) as sole sources of carbon and nitrogen, or in LB for kinases. Media were supplemented with 100 µg/mL ampicillin or 50 µg/mL kanamycine depending on the plasmids, and in 30 µg/mL ampicillin and 15 µg/mL chloramphenicol for alpha-synuclein. Overexpression was induced at an optical density OD600=0.8 by supplementing the medium with 1 mM ITPG at 37°C for all peptides except kinases, which were expressed at 30°C. Cells were harvested by centrifugation 4 hours later and cell pellets stored at -80°C. Cell lysis was performed using sonication in Tris 20 mM, NaCl 150 mM, pH 7.5 (TBS), in presence of benzonase (Sigma-Aldrich), lysozyme, protease inhibitors 1x (EDTA-free cOmplete, Roche) and 10 mM DTT. Soluble and insoluble fractions were separated by 15 min centrifugation at 15,000g. All peptides were purified from the soluble fraction except Elk1(aa309-429), which was resolubilized in TBS supplemented with 8 M urea. Alpha-synuclein was purified using protocols published earlier. [2] For the other peptides, the cell extracts were loaded on a His-Trap FF column (5 mL, GE Healthcare) and eluted using a gradient of imidazole. The eluted fractions were later concentrated, submitted to Tev treatment for 1 hour (after transfer in TBS for Elk1) in TBS supplemented with 10 mM DTT, and then re-loaded on His-Trap column. Fractions containing the peptide of interest were finally submitted to a final size-exclusion chromatography using a Superdex 16/60 75 pg (GE healthcare) previously equilibrated with Hepes 40 mM, NaCl 75 mM or 150 mM, pH 7.0 or 7.4 or 7.8.

Vanadium-NOTA production

VCl3 was purchased from AlfaAesar-Fisher Scientific. NOTA was purchased from Chematech (Dijon, France). NOTA and VCl3 were solubilized in water at 0.1 M and 1 M, respectively. They were mixed in a 1:1.1 ratio, and pH was progressively shifted from acidic values (about 2-3) to 7. When isolated, NOTA saturation concentration is lower than 0.1 M; it becomes soluble upon V 3+ complexation, which requires to elevate pH and reach the anion form NOTA 3-. V(OH)3 precipitate was sedimented using 14,000 g centrifugation during 10 minutes. 2 mL of the supernatant was applied on a Source 15 RPC column (GE-Healthcare) previously equilibrated with Hepes 1 mM and NaCl 5 mM, pH 7. Salts are eluted first, before the purple-brownish V-NOTA fractions (about 5 mL). These were lyophilized over night before being re-solubilized in water and re-injected on the same Source 15 RPC column and re-lyophilized. The obtained powder was solubilized in ~100-200 µL water. The most accurate method for weighting the final amount of V-NOTA is the following one: we weight the final microtube filled with the chosen volume of water before and after V-NOTA solubilization. Hepes and NaCl weights from the lyophilized 5 mL must not be forbidden. About 20-30 mg V-NOTA are typically obtained from a starting amount of 30 mg NOTA. Elk1(aa308-428) +Erk2 60 13 C O (ppm)

SUPPORTING INFORMATION


Zimo Wang
email: zimo.zmw@gmail.com

Pawan Dixit

Faissal Chegdani

Behrouz Takabi

Bruce L Tai

Mohamed El Mansori

Satish Bukkapatnam

Bidirectional Gated Recurrent Deep Learning Neural Networks for Smart Acoustic Emission Sensing of Natural Fiber-Reinforced Polymer Composite Machining Process Reference

Keywords: smart sensing, deep learning approaches, natural fiber-reinforced composites

Natural fiber-reinforced polymer (NFRP) composites are increasingly considered in the industry for creating environmentally benign product alternatives. The complex structure of the fibers and their random distribution within the matrix basis impede the machinability of NFRP composites as well as the resulting product quality. This article investigates a smart process monitoring approach that employs acoustic emission (AE)-elastic waves sourced from various plastic deformation and fracture mechanisms-to characterize the variations in the NFRP machining process. The state-of-the-art analytic tools are incapable of handling the transient dynamic patterns with long-term correlations and bursts in AE and how process conditions and the underlying material removal mechanisms affect these patterns. To address this gap, we investigated two types of the bidirectional gated recurrent deep learning neural network (BD-GRNN) models, viz., bidirectional long short-term memory and bidirectional gated recurrent unit to predict the process conditions based on dynamic AE patterns. The models are tested on the AE signals gathered from orthogonal cutting experiments on NFRP samples performed at six different cutting speeds and three fiber orientations. The results from the experimental study suggest that BD-GRNNs can correctly predict (around 87 % accuracy) the cutting conditions based on the extracted temporal-spectral features of AE signals.

Introduction

Because of recent moves toward sustainable manufacturing, natural fiber-reinforced polymer (NFRP) composites are becoming increasingly attractive for various industrial applications, such as for panels, window frames, and decking for industries such as automotive, construction, and aerospace companies. [START_REF] Peças | Natural Fibre Composites and Their Applications: A Review[END_REF] NFRPs offer an attractive balance between cost, carbon footprint, and performance. The applications of NFRP have widely expanded through the last few years, and the NFRP industry sector has reached $2.1 billion (USD) in the 2010s with a 10% growing rate worldwide. [START_REF] Uddin | Developments in Fiber-Reinforced Polymer (FRP) Composites for Civil Engineering[END_REF] Most of the emerging industrial applications necessitate machining of these NFRP composite panels to dimensional tolerances of ±1 mm and surface roughness within 1 μm. Currently, machining process is crucial for realizing NFRPs' functionality as the machined surface quality is highly related to the bearing of mechanical property of the NFRP products. However, NFRPs possess complex material characteristics, such as viscoelastic behaviors and thermal effect. [START_REF] Hollaway | Composite Materials and Structures in Civil Engineering[END_REF] In particular, their complex multiscale structure poses significant challenges for machining. Because of the natural fibers' cellulose structure and their orientations in the composites, these randomly distributed elementary/ bundle fibers within the matrix basis introduce distinct material removal mechanisms, create surface conformity issues, and consequently influence the overall machinability of such a material for industrial applications.

Current investigations into characterizations of the NFRP machining processes heavily rely on offline approaches (e.g., mechanical property testing and imaging instruments [START_REF] George | A Review on Interface Modification and Characterization of Natural Fiber Reinforced Plastic Composites[END_REF][START_REF] Valadez-Gonzalez | Effect of Fiber Surface Treatment on the Fiber-Matrix Bond Strength of Natural Fiber Reinforced Composites[END_REF] ). Though these methods are powerful in measuring the finished surface morphology and characterizing various modes of material removal, they cannot capture the effects of the heterogeneity and the fibers' distribution over the multiple scales of the matrix on material removal mechanisms. In addition, such offline tools are impractical for in-process monitoring of the NFRP machining processes. Therefore, a real-time sensing approach is desirable to characterize machining processes, evaluate machined surface quality, and provide timely intervention for in-process quality assurance. [START_REF] Lee | Precision Manufacturing Process Monitoring with Acoustic Emission[END_REF] Among various sensor candidates, the acoustic emission (AE) sensor shows potential for in-process monitoring of the cutting processes at the precision level (depth of cut within 10s of micrometers). The AE waves gathered during the machining process are sourced from the (elasto)plastic deformation, fracture, and friction at interfaces between the cutting tool, workpiece, and chips from machining processes. [START_REF] Dornfeld | Acoustic Emission During Orthogonal Metal Cutting[END_REF][START_REF] Barry | Observations on Chip Formation and Acoustic Emission in Machining Ti-6Al-4V Alloy[END_REF][START_REF] Bukkapatnam | Analysis of Acoustic Emission Signals in Machining[END_REF] In addition, AE waveforms are also related to the crack formation and chip breakage, entanglement, or both during the material removal process. However, such elastic transient waves during machining processes may be highly affected by the environmental noises, and consequently, they have a low signal-to-noise ratio. The challenge remains as to how to analyze such nonstationary signals with strong transient behaviors.

Most representative time portraits of the AE's transient patterns are listed in figure 1-figure 1A depicts a burst-patterned AE waveform, which oftentimes contains higher frequency responses with sharp rising and decaying times, and figure 1B shows a near-stationary (also referred to as "continuous") AE signal with low energy and long decaying time. [START_REF]Comprehensive Composite Materials[END_REF] During an NFRP cutting process, fast-streamed AE data may exhibit both types of behavior: continuous waveforms combined with (irregular) intermittent bursts. Each of the waveform patterns may be due to the energy released from a distinct material removal mechanism. Therefore, the prominent moves toward analyzing the AE waveforms may be related to how to connect the short time-dependent waveform patterns (intermittent bursts and resultant time-varying frequency responses) with different cutting conditions that result in changes of the fundamental cutting mechanisms.

From the perspective of the dynamic system, this transient behavior is referred to as dynamic intermittency. [START_REF] Wang | Change Detection in Precision Manufacturing Processes Under Transient Conditions[END_REF] The transitions between intermittent bursts and laminar phases from such a system create nonstationarity with time-variant dependency. Thus far, analyzing the intermittent AE signals is still challenging. Chang and Bukkapatnam [START_REF] Chang | Towards Characterizing the Microdynamics of AE Generation in Machining[END_REF] and Bukkapatnam and Chang [START_REF] Bukkapatnam | A Statistical Mechanistic Model of Acoustic Emission Generation in Shear Zone of Machining[END_REF] investigated the AE sensor signal's connection to microdynamics for machining metallic materials. Results suggest that the energy of AE signals sourced from the plastic deformation is related to the shear strain and shear strain rate. The stress releases that are due to the grain dislocation during the material removal processes may result in such time-varying intermittency patterns in AE waveforms. [START_REF] Csikor | Dislocation Avalanches, Strain Bursts, and the Problem of Plastic Forming at the Micrometer Scale[END_REF] Intuitively speaking, how to treat transient intermittent AE events is akin to how a human brain recognizes/processes the information in speeches or music clips. Currently, deep learning approaches and hidden Markov models [START_REF] Hinton | Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups[END_REF] have been selected as powerful tools for processing acoustic signals. Such approaches apply windows with fixed sizes to investigate contextual information from neighboring windows/frames to process the time-varying signals and improve training accuracy. However, the optimal length for preserving the contextual information in AE signals may be varied case by case because of its time-varying nature, suggesting that conventional deep learning algorithms or hidden Markov models may not be sufficient to explore the time-variant nature of the AE signal. Researchers have also presented recurrent neural networks (RNNs) for investigating the time-varying nature of streaming/sequential data, such as acoustic recognition problems. [START_REF] Trianto | Fast-LSTM Acoustic Model for Distant Speech Recognition[END_REF] Kamarthi, Kumara, and Cohen, [START_REF] Kamarthi | Wavelet Representation of Acoustic Emission in Turning Process[END_REF] and Pittner Kamarthi, and Gao, [START_REF] Pittner | Wavelet Networks for Sensor Signal Classification in Flank Wear Assessment[END_REF] and Pittner and Kamarthi [START_REF] Pittner | Feature Extraction from Wavelet Coefficients for Pattern Recognition Tasks[END_REF] presented a wavelet-based neural network that extracts the spectral features in a multisensor setup for detecting cutting tool wears. Comparatively, RNNs containing cyclic connections are regarded as more powerful tools to model the nonstationary transient data, especially time-varying acoustic signals. [START_REF] Mikolov | Extensions of Recurrent Neural Network Language Model[END_REF][START_REF] Sak | Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling[END_REF] The RNN encodes the sequential data in the internal states and allows an estimation of the following steps based on all previous information up to the current time point. Hence, RNNs have demonstrated the success in sequence labeling and prediction tasks in nonstationary real-world processes, such as handwriting recognition and language modeling. [START_REF] Graves | Speech Recognition with Deep Recurrent Neural Networks[END_REF] However, conventional RNNs may be difficult to train and can merely model short time-dependent effects (the intermittent bursts in AE signals are both long and short time dependent). Toward overcoming this drawback, an RNN framework [START_REF] Bukkapatnam | Fractal Estimation of Flank Wear in Turning[END_REF] that uses fractal properties of the attractor of the underlying dynamic system along with other machining parameters as the training inputs was developed to predict flank wear in machining. Furthermore, a recurrent predictor neural network [START_REF] Rao | Real-Time Identification of Incipient Surface Morphology Variations in Ultraprecision Machining Process[END_REF] was applied to capture the evolution of complex nonlinear and nonstationary processes (e.g., real-time sensor signals) and detect the incipient surface deterioration in ultraprecision machining (UPM) processes. Cheng et al. [START_REF] Cheng | Ultra-Precision Machining Process Dynamics and Surface Quality Monitoring[END_REF] applied a Gaussian process with extracted statistical features as well as features extracted based on nonlinear recurrent analysis for real-time prediction of the achieved surface characteristics during UPM. Kannatey-Asibu and Dornfeld [START_REF] Kannatey-Asibu | A Study of Tool Wear Using Statistical Analysis of Metal-Cutting Acoustic Emission[END_REF][START_REF] Kannatey-Asibu | Quantitative Relationships for Acoustic Emission from Orthogonal Metal Cutting[END_REF] presented statistical analytic approaches to analyze AE signals for monitoring the cutting tool condition and related the AE features to the metal-cutting process parameters. A nonparametric approach from our earlier effort [START_REF] Wang | A Dirichlet Process Gaussian State Machine Model for Change Detection in Transient Processes[END_REF] was presented to model the transient behaviors of nonstationary sensor signals during machining processes.

In this article, we present a sensor-based monitoring approach for smart sensing and AE characterizations for machining NFRPs. To address this gap, we investigate the gated recurrent neural network (GRNN) models to relate the AE waveform features to different process conditions and the underlying material removal mechanisms of machining NFRPs. During the orthogonal cutting experiments, AE signals were gathered from the testbed equipped with a multisensor data acquisition system. To connect the AE signal patterns to the machining mechanisms of NFRP cutting, we employ GRNN models for characterizing the temporal-spectral features of AE signals. The rest of this article is organized as follows: the framework of the presented approach is introduced in "Overview of Recurrent Deep Learning Approaches." The experiment details about the orthogonal cutting experimental setup, implementation of GRNN models, and results for AE characterizations are presented in "Temporal-Spectral Features-based GRNN for AE Characterizations." Concluding remarks are provided in "Summary and Concluding Remarks."

Overview of Recurrent Deep Learning Approaches

We present the framework of GRNN for characterizations of AE signals gathered from orthogonal cutting experiments. Figure 2 summarizes the schematic diagram for implementing GRNNs. During the orthogonal cutting experiment, sensor signals were recorded with various cutting conditions (varying cutting speeds (ν) and fiber orientation angles (FOA)). The temporal-spectral features were extracted using a sliding-windowed timefrequency analysis over specified microseconds-long time windows. To handle the time-varying frequency response of the AE signal and relate it to different machining conditions during NFRP cutting, the temporalspectral features are directly treated as the input series in the GRNN for characterizing the cutting processes. Therefore, ad-hoc feature selection and extraction are avoided. The GRNNs were then developed for training and prediction/estimations on characterizing the time-spectral features of AE waveforms.

GENERAL STRUCTURE OF LONG SHORT-TERM MEMORY AND GATED RECURRENT UNIT

The general RNN can be presented as follows:

h t = HðW xh x t + W hh h t-1 + b h Þ (1)
FIG. 2 Schematic diagram of the presented research approach.

y t = W hy h t + b y (2)
where x t , y t , and h t are the input, output, and hidden state, respectively. The matrix Ws are weight matrices (e.g., W xh is the weight matrix for the layer connecting the inputs and the hidden nodes, W hh is the weight matrix between hidden layers, and W hy is the weight matrix between the hidden layer and output layer). H is the hidden layer function, and the b's are the bias vectors (b h is the hidden bias vector and b y is the output bias vector). To extend the formula to the GRNN, two models, namely long short-term memory (LSTM) cell and gated recurrent unit (GRU), are introduced. The LSTM uses the input, forget, and output gates, and the GRU model uses the reset and update gated units. Both LSTM and GRU apply these gated units to track timedependent recurrence relations while mitigating the issues of vanishing or exploding gradient problems or both. [START_REF] Chung | Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[END_REF] In this article, we developed both LSTM and GRU-based approaches for constructing the GRNN to capture the transient nature of AE, and based on the comparisons between two presented GRNN approaches using numerical and experimental case studies, we further applied the optimal model for characterizations of AE signals during the machining processes on the NFRPs. The details of these two models are described in the following subsections.

LSTM Cell

As mentioned, LSTM considers the dependencies on previous states at different timescales by applying input, forget, and output gates. This feature may help capture the intermittent transient data with time-variant longshort time dependencies. One can formulate the structure of the LSTM cell as follows:

i t = σðW xi x t + W hi h t-1 + W ci c t-1 + b i Þ (3) f t = σðW xf x t + W hf h t-1 + W cf c t-1 + b f Þ (4) c t = f t c t-1 + i t tanhðW xc x t + W hc h t-1 + b c Þ (5) o t = σðW xo x t + W ho h t-1 + W co c t + b o Þ (6) h t = o t tanhðc t Þ (7)
where σ is the activation function (which is often selected as the logistic sigmoid function) and i, f , o, and c are, respectively, the input gate, forget gate, output gate, and cell activation vectors (with the same size as the hidden vector h). The matrix Ws are the weight parameters for different memory cells: W xi is the weight matrix for the input gate, W hi is the matrix connecting hidden layer and the input gate, and W ci connects the cell activation and the input gate. W xf , W hf , and W cf are the matrices connecting the inputs, hidden layers, and activation vector to the forget gate, and similarly, W xo , W ho , and W co are the matrices connecting the inputs, hidden layers, and activation vector to the output gate. The b's are the bias vectors (e.g., b i is the input gate bias vector and b f is the forget gate bias vector).

GRU

Compared with the LSTM cell, the general GRU unit does not possess a separated memory (c t ) or the output gate. Instead, it applies recurrent reset and update units for capturing the time-dependent features at different time scales. [START_REF] Chung | Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[END_REF] A general GRU can be formulated as follows:

z t = σðW xz x t + W hz h t-1 + b z Þ (8) r t = σðW xr x t + W hr h t-1 + b r Þ (9) ht = tanhðW x hx t + W r hðr t ∘ h t-1 Þ + bh Þ (10) h t = ð1 -z t Þh t-1 + z t ht (11)
where z t and r t are, respectively, the update gate and reset gate vectors; the activation h t at time t is a linear interpolation between the previous activation h t-1 and the candidate activation e h t ; the b's are the bias vectors for different cells (b z , b z , and bh are the bias terms for the update gate, the reset gate, and the candidate activation, h, respectively); Ws are the weight matrices (e.g., W xz is the matrix between the inputs x and the update gate z); and the activation function σ is chosen with the sigmoid kernel. The operator ∘ denotes a Hadamard product.

Bidirectional LSTM and GRU

As one drawback of the RNNs, they only consider the previous information for inferencing the system outputs; the bidirectional (BD)-GRNN is applied to characterize the AE signal with the transient nature by considering the forward and backward states for inferencing the transient burst and jumps between sojourns/stationary segments. In general, the BD-GRNN contains two sets of layers (one for forward information and the other for the backward direction as shown fig. 3). One can extend the formulas to a BD-GRNN as follows:

h → t = HðW xh → x t + W h → h → h → t-1 + b h → Þ (12) h ↼ t = HðW xh ↼ x t + W h ↼ h ↼ h ↼ t-1 + b h ↼ Þ (13) y t = W h → y h → t + W h ↼ y h ↼ t + b y (14)
The BD-GRNN contains the forward hidden sequence h As suggested by the schematic diagrams of LSTM and GRU in figures 4 and 5, it may be noted that, compared with the conventional RNN, the gate units in LSTM and GRU (e.g., the memory cell of the LSTM and the update/reset gate of the GRU) keep the present states and add the new content on top, which in turn constructs the recurrent relations (similar to the iterative map for modeling the nonstationary dynamic systems). Such a scheme provides a prominent feature toward capturing the transient behaviors of the input data.

CAPTURING TRANSIENT BEHAVIORS USING LSTM AND GRU

To demonstrate the capability of the presented approach for characterizations of transient processes, an illustrative example based on the chord progression of the major pentatonic scale is presented. As noted earlier, such a chord progression follows intermittent dynamics [START_REF] Wang | A Dirichlet Process Gaussian State Machine Model for Change Detection in Transient Processes[END_REF] and is therefore well suited to illustrate the performance of our approach. Figure 6 shows a representative snippet of the symbolic sequence of chord progressions.

FIG. 4

Schematic diagram

showing the structure of an LSTM cell. [START_REF] Hochreiter | Long Short-Term Memory[END_REF] Here, the vertical axis represents different chords, and the horizontal axis is the time index referring to the number of the music note. Each chord is tokenized with a unique number, viz., number 1 mapping to the note B, 2 mapping to the note E, 3 to B b , 4 to F, 5 to A, 6 to D, 7 to G, and 8 to C. There are four types of chords considered here: C-G, D-A, E-B, and F-B b . Each segment (referred to as a pentatonic note progression) lasts for 3 bars under the 4/4 time signature (i.e., 12 data points). After this duration, the note is transitioned as determined by a Markov transition matrix given by the following: Here, each row corresponds to the original pentatonic chord progression (e.g., first element for the pentatonic chord of C-G, the second for D-A, the third to E-B, and the fourth to F-B b ), and each column contains the destination pentatonic chords.

A =
The implemented model has two layers of the gated units for both forward and backward directions with a detailed structure as plotted in figure 7B. Each layer is set with neuron nodes as 100, the batch size is set as 40, and the training epoch is selected around 30 to ensure that the loss function value during training converges to its minimal. The prediction performance on the trained BD-LSTM as well as BD-GRU is tested on simulated chord progressions.

The results of BD-LSTM for one-step and five-step look-ahead predictions (i.e., forecasts) are correspondingly shown in figure 8A and 8C. In comparison, figure 8B and 8D includes the prediction results from BD-GRU for one and five-step forecasting. The observations (symbolic sequence) are plotted using solid lines, while the lines with triangles represent the estimated sequences. Remarkably, as illustrated in the magnified block (dashed outline) in figure 8, the BD-GRNN model is able to capture the transitions between scales as it accurately predicts the jumps among multiple sojourns (in-between segments of the chord progressions). It is evident that both onestep and five-step predictions for both BD-LSTM and BD-GRU models possess high accuracies demonstrated by nearly 100 % for the R 2 values as shown in Table 1.

From the numerical case study, one can conclude that the developed BD-GRNNs can properly capture the complicated recurrence relations between states. The transient AE signals may exhibit similar recurrent patterns, which combine irregular bursts and stationary segments caused by the material fracture and intermittent dislocations during machining. Hence, the presented approaches have prominent application merits toward FIG. 7 (A capturing the intermittent AE waveform signals for monitoring machining process: as the model trains the gates to construct the recurrence relations using the sequential data, it captures the transient behaviors of the AE signals under different machining conditions. To test the presented approach toward smart sensing for AE characterizations, an experimental case study on machining NFRP processes was further investigated. Details on the experiment setup as well as the case study results are presented in the following sections. The AE signal captures the transients, i.e., time-varying frequency patterns associated with the material deformations, fractures, or both that occur at irregular intervals during machining. [START_REF] Hase | The Relationship between Acoustic Emission Signals and Cutting Phenomena in Turning Process[END_REF] To further process the AE signals, the temporal-spectral features are then extracted. Such time-frequency features could be visually represented using a spectrogram, as shown in figure 10B, where the x axis represents the time index, the y axis is the frequency range, and a colormap is applied to represent the strength of the frequency component. Such a spectrogram records the frequency components of the signal and their variations over time. The following procedures summarize the generation of the temporal-spectral features:

1. A sliding window with window width L = 125 is applied to collect a set of AE signals at the time index t, i.e., {x t-L+1 , x t-L+2 , : : : , x t }. 2. Then, the fast Fourier transformation (FFT) is applied to compute the frequency component:

X ðtÞ k = X t n=t-L+1
x n e -i2πkn L k = 0, 1, : : : , L -1 (15) where fx n g (for n = t -L + 1, t -L + 2, : : : , t) is the time series of the AE signal within the sliding window L, and the FFT generates the frequency components X ðtÞ = ½X 11 were generated by the FFT with a sliding window of 0.125 ms and a 50 % time step (i.e., ϑ = 0.5 L). This translates to a temporal resolution of 0.125 ms (x axis), and we select 200 Hz as the spectral resolution (y axis). The edge effect that is due to the sliding window may be ignored, as the window size (0.125 ms duration) is smaller than the durations FIG. 10 (A Note that the multisensor setup segments the cutting and noncutting phases for the time-frequency features. Let τ 1 and τ 2 be the start and end time stamps of the segment for the cutting phase (e.g., the start and end points of the rectangular frame shown in fig. 11A). The response vector Y, during the cutting phase, contains the control parameters for the designed experiments, i.e., Y ðtÞ = ½FOA ðtÞ , v ðtÞ ⊤ for t = τ 1 + L, τ 1 + L + ϑ, τ 1 + L+ 2ϑ : : : , minðT, τ 2 Þ, where FOA ∈ f0 °, 45 °, 90 °g and cutting speed v ∈ f2, 4, 6, 8, 10, 12g (m= min ). Given the time duration T 0 (in seconds) of the recording, let T = T 0 × Fs, where Fs is the sampling rate (Fs = 1 MHz for all experiment recordings). Then, the extracted features X as well as the process responses Y for a single recording Ω could be represented in the following format:

Ω = ½X ðtÞ⊤ , Y ðtÞ⊤ ⊤ =
X ðLÞ X ðL+ϑÞ X ðL+2ϑÞ : : : X ðTÞ Y ðLÞ Y ðL+2ϑÞ Y ðL+2ϑÞ : : : Y ðTÞ

The GRNN model is applied to capture the empirical relationships of the AE spectral features fX ðtÞ g with fy ðtÞ g, where y ðtÞ is a subset of the response fY ðtÞ g (e.g., y ðtÞ = FOA ðtÞ for the classification of fiber orientations). To create the GRNN models, [START_REF] François | Keras: The Python Deep Learning Library[END_REF] data augmentation is needed to ensure that all the input sequences for both training and testing data have the same lengths. One straightforward method is to repeat the temporal-spectral feature data fX ðtÞ g and responses fy ðtÞ g in the testing data set until the length of fX ðtÞ g in the testing set is equal to fX ðtÞ g in the training set. In total, 38 experiments (90°with 1 single repetition for each cutting speed) were conducted with different fiber orientations and cutting speeds.

To ensure the training and testing datasets for each set of parameters have the same data length (as required by the GRNN models), we apply the data augmentation [START_REF] Um | Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring Using Convolutional Neural Networks[END_REF] to keep the duration as 250 ms for each cutting experiment. Because of the variations in the cutting speeds, the durations of the cut and the associated lengths of the signal recordings vary from ∼30 to ∼260 ms. Hence, for the conditions with higher cutting speeds (shorter time durations), the signals are sequentially padded (as illustrated in fig. 12A) to maintain the data length as 250 ms. Recordings with lower speeds (longer durations), on the other hand, are truncated (see fig. 12C) to keep the recording length as 250 ms. For those conditions with one repetition, samples have been randomly permutated (as shown in fig. 12B) followed by sequential padding to avoid the exact same data/signals utilized in both training and testing datasets, hence reducing the chance of overfitting.

BD-LSTM AND BD-GRU FOR MONITORING CUTTING CONDITION VARIATIONS WITH RESPECT TO FIBER ORIENTATIONS

Given the extracted time-frequency features fX ðtÞ g and the response fy ðtÞ g = fFOA ðtÞ g, the BD-LSTM and GRU models are applied for classifying the cutting conditions. The details of the structure of the BD-LSTM and BD-GRU are described as follows.

The input for the extracted temporal-spectral features is with the dimension as T × 2,499, where T is the length/duration of the temporal-spectral data with 2,499 rows of the frequency component ranging from 0 to 500 kHz (with 200 Hz resolution). Here, the number of hidden nodes is selected as 40 to predict the outputs (different fiber orientations). The batch size is selected as 36. The results from an 18-fold cross validation (which ensures that the test data contain every condition from 6 cutting speeds × 3 FOAs) in terms of the confusion matrix are listed in Table 2. In total, the classification accuracy is around 88 % for different fiber orientation conditions using BD-GRU. Both BD-LSTM and BD-GRU were then applied to test the prediction accuracy on the sequence of each recording. In total, 36 experiment recordings (with various cutting speeds and fiber orientations) were selected in training sets, and 18 recordings were included in each testing set. The classification results are listed in Table 3.

We then compare the results with an earlier investigation on analyzing AE characteristics using random forest (RF) model [START_REF] Wang | Acoustic Emission (AE) Characterization of Natural Fiber Reinforced Plastic (NFRP) Composite Machining Using a Random Forest Machine Learning Model[END_REF] listed in Table 4. Though the result from the RF model suggests a statistically significant difference between the study cases with FOA = 0 °and all others, it could only recognize the difference for FOA = 45°and 90°with around 60 % accuracy. Comparatively, both BD-LSTM and BD-GRU outperform the RF model with consistent accuracy over 87 % toward characterizations of AE signals related to different cutting conditions (FOAs), while BD-GRU consistently achieves higher accuracy for both training and testing.

AE CHARACTERIZATIONS UNDER DIFFERENT CUTTING CONDITIONS

The presented approach was then applied to classify cutting conditions considering both FOAs and cutting speeds for machining NFRP, i.e., fyg = fðv, FOAÞjv ∈ f2, 4, 6, 8, 10, 12g, FOA ∈ f0°, 45°, 90°gg. As for implementation details, we select the GRU to construct the BD-GRNN model since it achieves higher classification accuracy (compared with LSTM) toward characterizations of AE temporal-frequency features under different cutting conditions (for the previous case study). The number of nodes N h is selected as follows:

N h =
#total samples η • ð#output neurons + #input neuronsÞ (17) where the scaling factor η is set as 2. [START_REF] Stathakis | How Many Hidden Layers and Nodes?[END_REF] The parameters were selected based on numerical case studies toward multiple settings of the neural network for achieving the highest accuracy. With the actual length given as 140,000 data points, we set the input neurons as 2,499 (frequency bins with 200 Hz resolution for the spectral features at each time step) and the output size as 18 (for 3 FOAs × 6 cutting speeds). Hence, by selecting a proper scaling number η = 2 in equation ( 17), the number of nodes N h = #total samples η•ð#output neurons+#input neuronsÞ = 140,000 2×2,517 ≈ 28, and we select the number as 30 in the model. In addition, we also investigate the performance [START_REF] Keskar | On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima[END_REF] we selected the batch size as 36, which is the minimal fraction value of the training data size. Classification results in terms of recall/true positive rate (TPR) values for both cutting speeds and fiber orientations (18 conditions in total) are shown in Table 5. Each row represents an FOA, and columns are the collection of different cutting speeds. It may be noted that for the higher speed conditions (v = 10 and 12 m= min ), most cases achieve high TPR (nearly 100 %) for connecting the AE temporal-frequency features to the cutting conditions. However, the classification errors increase when discerning conditions with minor changes in cutting speeds under the same FOAs. Details about TPR/recall values given these 18 conditions are listed in Appendix Table A.2, where the first column lists the FOAs, second column enumerates different cutting speeds (m/min), the diagonal elements are the TPRs, and all the nondiagonal ones represent the false negative rates.

Next, we investigate the classification errors under each condition. Figure 13 visualizes the detailed classification results (precisions and false positive rate) using pie charts for all 18 conditions. The x axis represents the fiber orientations, and the y axis lists the cutting speeds. The sectors in the pie chart present the portions for true positives as well as false positives under every cutting condition. Each condition ðv, FOAÞ is listed in the legend in sourced from the condition FOA = 0°and v = 10 m= min (colored in blue). Similarly, as for the condition of FOA = 0°and v = 2 m= min (the first pie chart in the first row), its false negatives are mostly classified as the conditions with the same orientation (FOA = 0°) but the different cutting speed as ν = 4 m/min (the condition colored in turquoise).

Results from this collection of pie charts may suggest that most false negatives are misclassified in adjacent groups with different cutting speeds but the same FOA. The same cutting mechanism may result in similar temporal-frequency patterns in the AE signals. However, the minor variations (e.g., from 2 to 4 m/min) in cutting speeds may not be significant for changing the AE characteristics. More evidence could also be found in the conditions where FOA = 90°. As for the cases under FOA = 90°, the relatively low values of both recall and precision (∼77%) could be found in the groups in which cutting speeds v = 2 and 4 m= min , indicating a large portion (∼20 % for each condition) was misclassified between these two conditions with a slight change in cutting speeds.

The reason why AE signatures are more distinguishable under different FOAs is because different FOAs lead to distinct combinations of cutting mechanisms [START_REF] Keskar | On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima[END_REF] : under all FOA = 0°conditions, the cutting mechanisms are predominantly the PP matrix removal, with some fibers sliding/shearing along interfacial areas during the material removal; as for the 45°orientation position, the cutting mechanism consists of compression-induced interfacial shearing along the fiber orientation direction, which causes fiber-matrix debonding; for FOA = 90°, the fractures that contribute predominantly to fiber pull-out emerge intermittently. Different failure modes during material removal may attribute to different frequency responses of the AE sensor signals. However, classification results also suggest that minor changes in the cutting speed are still less influential on the variations of the resultant temporal-spectral AE features compared with the fiber orientations (FOAs).

Despite those groups with comparatively higher false positives (conditions misclassified in adjacent cutting speeds), the presented BD-GRU could achieve an overall ∼87 % accuracy, while the previously investigated RF could not correctly classify the AE characteristics with respect to the FOAs.

Summary and Concluding Remarks

This article presented a framework using a recurrent deep learning approach for real-time monitoring and characterization of sensor signals under transient phase. The temporal-spectral features of AE signals during the NFRP machining processes were selected to test the performance of the presented BD-GRNNs. Admittedly, the feature extraction and selection for the temporal-spectral components remain a major challenge, and investigations into an advanced machine learning approach may be needed to efficiently select energy components within specific frequency band(s). However, the developed GRNNs allow a selection of useful information from the time-frequency features (by training the weights of each neuron) and use gated units to capture the underlying process dynamics on the time-varying spectral information. Thus, ad-hoc feature selection and extraction are avoided. The contributions of this article are listed as follows:

• An illustrative case study is presented to show the performance of the BD-RNN for capturing the transient nature of signals. The results of one (multi)-step forecasting indicate that the GRNN can capture transients (intermittent jumps) between stationary segments and could be used for characterizations for the transient processes.

• The GRNNs (both LSTM and GRU models) are presented for characterizing the AE signals with different transient phase patterns (laminar phases and variations on the modulated time series coefficients). The presented approach achieves around 87 % accuracy, which outperforms other advanced machine learning approaches, such as RF classification, for sequential data classification for characterization of AE signals under different machining conditions. Thus, GRNNs allow real-time process characterizations for machining NFRPs with changes in machining parameters as well as microstructure variations.

For future work, other factors, such as fiber (bundle) distributions and chip formations, need to be considered to characterize AE signals during the machining processes.

FIG. 1

 1 FIG. 1 (A) A representative AE waveform showing two distinct characteristics: (B) AE bursts and (C) stationaryemission.[START_REF]Comprehensive Composite Materials[END_REF]

→

  , the backward hidden sequence h ↼ , and the output sequence y. The matrix Ws are different weights for the associated layers (e.g., W xh ↼ connects the inputs x and the backward hidden layer h ↼ ). b h → , b h ↼ , and b y are the bias vectors for the forward hidden layer, backward hidden layer, and output layer, respectively.

FIG. 3

 3 FIG. 3 Schematic diagram of a BD-GRNN neural network where each box represents a gated unit.

FIG. 6

 6 FIG. 6 Plot of the symbolic sequence showing the tokenized chord progressions. Each chord is encoded with a unique number, i.e., {1: B, 2: E, 3: B b , 4: F, 5: A, 6: D, 7: G, 8: C}.

FIG. 5

 5 FIG. 5Schematic diagramshowing the structure of a GRU unit.

  FIG. 7 (A) A schematic diagram for the structure of input variables, (B) the hierarchical structure of the bidirectional neural network, and (C) the losses with training and cross validation.

FIG. 8

 8 FIG. 8 Result comparisons of one and five-step ahead predictions, where (A) and (C) are generated from BD-LSTM and (B) and (D) list the results from the BD-GRU model. Here, the predicted sequences are plotted as dashed lines with triangle markers, the ground truth is shown using the solid lines, and the inset (dashed rectangle) magnifies a short, 10 data point snippet of the prediction result. Both one-step ((A) BD-LSTM vs. (B) BD-GRU) and five-step ((C) BD-LSTM vs. (D) BD-GRU) predictions suggest that two developed models can finely capture the jumps between stationary segments of the chord progression transitions.

A

  set of the representative sensor signals synchronously collected from the two accelerometers (separately mounted on the tool holder and the workpiece holder) and the AE sensor (on the tool holder) is shown in figure 9B. It may be noted that Accelerometer 2 (attached to the workpiece holder) can properly separate the dynamics of the cutting and noncutting stages because of its sudden increase in amplitude when the cutting initiates. This allows us to isolate the signals of the cutting and noncutting stages.

FIG. 9

 9 FIG. 9 Schematic diagram showing the experimental setup: (A) the orthogonal cutting process with mounted AE and vibration sensors, and (B) the collected signals from the AE sensor and two vibration sensors during an orthogonal cutting experiment (FOA = 0 °and v = 4 m= min ).

ðtÞ 1 , X ðtÞ 2 ,

 12 : : : , X ðtÞ L ⊤ . By sequentially generating the frequency component vectors X ðtÞ (for t = L, L + 1, L + 2, : : : , T, where T is the time stamp at the end of the data recording), the matrix of the spectrogram could be represented as M TF = ½X ðLÞ , X ðL+ϑÞ , X ðL+2ϑÞ , : : : , X ðTÞ , where ϑ denotes the step of the sliding window.

  Figure 11A-C shows the spectrograms of the AE and two vibration signals, capturing the temporal-spectral features of the tool-approaching and cutting phases. The spectrograms as shown in figure

  FIG. 10 (A) The time portraits show the synchronized AE signal along with vibration signals from the tool holder (Accelerometer 1) and the workpiece holder (Accelerometer 2) during an orthogonal cutting process experiment; (B) and (C) show the temporal-spectral feature extraction. Machining condition: FOA = 0°and v = 4 m= min .

FIG. 11

 11 FIG. 11 Representative time-frequency domain spectrograms gathered during an orthogonal cutting experiment (v = 8 m= min , FOA as 45°): (A) AE sensor, (B) Accelerometer 1 (ACC-1), and (C) Accelerometer 2 (ACC-2). Here, the rectangular frames at the right side of every plot indicate the temporal-spectral information during the cutting phase.

  FIG. 12 A schematic diagram showing the data augmentation schemes for AE signals: (A) sequential padding, (B) random permutation followed by sequential padding, and (C) sequence truncation.

figure 13 .

 13 figure13. Taking the machining condition FOA = 0°and v = 12 m= min as one example (represented using the black color in its pie chart (the first row and the sixth column), the sector under the conditions FOA = 0°and v = 12 m= min depicts the precision (ratio of the true positives over sum of true and false positives) and other sectors show the portions of false positives (percent of misclassified data from other conditions). Note that even though such a condition achieves a high accuracy (over 90 % of the TPR), it still has the misclassified portion(s)

TABLE 1

 1 Accuracy for symbolic sequence prediction

	R 2	One Step	Five Steps
	BD-LSTM	0.99	0.99
	BD-GRU	0.99	0.99

DAQ Module NI-9223) is applied to rapidly stream the sensor data at 1 MHz sampling rate for each channel. The workpiece samples and mounted on clamp-on vice used the unidirectional flax fiber-reinforced polypropylene composites (manufactured by the material supplier Composites Evolution (UK)). Each NFRP workpiece (with dimensions as 20 by 15 by 4 mm 3 ) has fiber volume fraction around 40 % and polyester weft fiber, which helps maintain the unidirectionality of the natural fiber, around 5 %. The orthogonal cutting experiments were conducted with the depth of cut ∼10 μm at different cutting conditions with varying cutting speeds (ν = 2, 4, 6, 8, 10, 12 m/min) and different FOAs (FOA = 0°, 45°, and 90°) toward the cutting direction.

TEMPORAL-SPECTRAL FEATURE EXTRACTION

TABLE 2

 2 Confusion matrices for various fiber orientations (BD-GRU vs. BD-LSTM)

			Predicted				Predicted
	BD-GRU	0°45°90°0°45°90°A		BD-LSTM
	ctual	0°0.9997	0.0000	0.0003	Actual	0°0.8247	0.1139	0.0615
		45°0.0481	0.9519	0.0000		45°0.0156	0.8179	0.1665
		90°0.0045	0.0000	0.9955		90°0.0144	0.0024	0.9832

TABLE 4

 4 Cross validation results for various FOAs using the RF

			Predicted
	Confusion Matrix	0°45°90°A
	ctual	0°0.8531	0.1259	0.0210
		45°0.1354	0.7446	0.1200
		90°0.2047	0.1913	0.6040

TABLE 3

 3 Testing results for various fiber orientations (BD-GRU vs. BD-LSTM) functions, such as tanh function and ReLU function, on the achieved accuracy. The tanh function has consistent high performance in all cases compared with other functions tested. Based on the guidelines from the literature,

			Predicted				Predicted
	BD-GRU	0°45°90°0°45°90°A		BD-LSTM
	ctual	0°1.0000	0.0000	0.0000	Actual	0°0.8333	0.1667	0.0000
		45°0.3333	0.6667	0.0000		45°0.0000	0.8333	0.1667
		90°0.0000	0.0000	1.0000		90°0.0000	0.0000	1.0000

TABLE 5

 5 Cross validation results (recall/TPR)

	Recall			Cutting Speed, m/min
	FOA	2	4	6	8	1 0	1 2
	0	0.9912	0.9621	0.8801	0.6169	0.9231	0.9975
	45	0.8893	0.8001	0.6611	0.8598	0.7470	0.9902
	90	0.7795	0.7064	0.9951	0.9956	0.9804	0.9581

ACKNOWLEDGMENTS

Authors would like to thank the National Science Foundation [Civil, Mechanical and Manufacturing Innovation (CMMI #1432914) and Autonomous Experimentation Platform for Accelerating Manufacturing of Advanced Materials (S&AS: INT #1849085)] for supporting the research.

Appendix