Mai Huong Valin

Gaël A Obein

A new method for measuring the spectral irradiance at LNE-Cnam

A new method for measuring the spectral irradiance at LNE-Cnam Authors : Mai Huong VALIN 1 , Gaël OBEIN 1 Participants in this work : Bernard ROUGIE 2 , Thierry VALIN 1 , Daniel TRUONG 1 , Alain VISSIERE 1 , Frédéric BOURSON 1 1 LNE-Cnam EA2367, 2 Former member of the LNE-Cnam_ retired since November 2017 Corresponding e-mail address : mai-huong-christine.valin@lecnam.net, gael.obein@lecnam.net Since, French primary spectral irradiance scale was linked to the thermodynamic temperature reference. Measurement range was from 300 nm to 2500 nm. A new method for measuring the spectral irradiance of a source has been developed. This facility is based on the comparison of a source to be calibrated with a high temperature blackbody (HTBB). The reference spectral irradiance is determined by measuring the temperature of the HTBB with a filter radiometer. To minimize uncertainties, corrections on temperature homogeneity and emissivity of HTBB are applied.

The filter radiometer is based on a silicon 3 photodiodes trap detector. Its spectral responsivity is obtained in 3 steps :

• calibration of spectral responsivity of the trap detector against our cryogenic radiometer • calibration of the spectral regular transmittance of its bandpass filter with our primary spectrophotometer • calibration of diaphragm surface with our devoted setup. As the reflectivity of the trap detector is null, the flux responsivity of the radiometer is the product of the filter transmittance by the detector responsivity.

New method and traceability scheme Measurement irradiance reference principle

In the future, this measurement setup can be improved by using LabSphere scatterers (Spectralon and Infragold) instead of integrating sphere for :  an extension of our measurements up to 200 nm  an extension of our measurements up to 3000 nm.

From now on, LNE-CNAM spectral irradiance reference is based on the radiometric reference.

Thanks to the new setup, it is possible to measure the spectral irradiance of transfer lamps with a direct traceability to our cryogenic radiometer.

Our measurement capacity is extended to cover the spectral range from 250 nm to 2500 nm.

Our measurement uncertainties are reduced by at least a factor two over almost the entire range.

 A HTBB equipped with a 8 mm diameter diaphragm is heated up to 3000 K. It provides radiations that can be used in the 250 nm to 2500 nm range.

 A spectroradiometric system compares the spectral irradiance of HTBB and transfer lamps. It is composed of :

➢ an integrating sphere equipped with a circular entrance port diaphragm to receive the flux from the sources and with a rectangular slit shape of 2 mm x 15 mm output port located at 90°➢ two Ø 150 mm spherical mirrors that focus the image of the sphere exit slit on to the entrance slit of a double monochromator ➢ a Jobin Yvon HRD double monochromator on which the manual wavelength selection mechanism has been replaced by a high precision motorized translation stage and equipped with three gratings to cover the spectral range ➢ different detectors to be used depending on application area (photomultiplier(2), Si photodiode(1), InGaAs photodiode(1) or IR enhanced InGaAs photodiode(1)).

 A calibrated filter radiometer with a responsivity centered on 492 nm.

The filter radiometer and the integrating sphere are placed on the same translation stage to perform in one cycle the HTBB temperature measurement and the spectral irradiance ratio of the HTBB and the lamps.

The integrating sphere is rotated by 180°around the axis centered on the exit slit to collect either the HTBB or the transfer lamps flux.

Entire spectroradiometric system is placed in a box with two compartments, one for the detection part and one for the monochromator, in order to avoid stray light. The bench is fully automated and controlled by a custom LabVIEW software.

Our set of 4 reference transfer lamps have been measured and compared with their historical values on the range 300 nm to 2500 nm.

There is at least a factor two between historical and new measurement uncertainties (k=2) over the entire range.

Most of the mean deviations 2019/2016 observed are smaller than the expanded uncertainty of the comparison. At 300 nm, the significant deviation observed seems to correct the deviation that we had during the last CCPR K1-a in 2005. It could be explained by lack of flux and the stray light of former simple monochromator.

Conclusion Experimental setup Validation


E Vuillermet
email: enora.vuillermet@utt.fr

E Usureau

M Lazar

R Deturche

Enhancement of light extraction in 4H silicon carbide by nanostructuring the surface with high temperature annealing

Silicon carbide (SiC) is a wide bandgap semiconductor with actual applications in power electronics due to its high thermal resistance, chemical stability and high critical electrical field [1]. However, SiC is also more and more studied for its applications in optoelectronics and nanophotonics, becoming a strong contender to conventional wavelength-conversion materials, such as phosphors, for the fabrication of white light emitting sources (LED) [2][3]. Indeed, fluorescent SiC (f-SiC), which is co-doped with donoracceptor pairs (DAP) like nitrogen and bore or nitrogen and aluminum, presents a higher color rendering index and a longer lifetime compared to phosphor based LED [3]. More recently, it has been shown that SiC can also emit near infrared (NIR) light due to the excitation of color centers such as silicon vacancies, divacancies and nitrogen-vacancy centers [4][5]. However, one disadvantage of SiC for optoelectronic applications is its rather high refractive index (2.65 at 590 nm) compared to air. Due to that, the emitted light is totally reflected and stays trapped within the material which causes the low extraction efficiency of light of SiC [6][7]. To enhance the light extraction of SiC, nanostructuring of the material's surface has been done by various means such as e-beam lithography, nanosphere lithography, assembly of metal nanoparticles or reactive ion etching (RIE) [3][4][5][6][7].

In this work, we look to improve the extraction of light in 4H-SiC by changing the nanostructure of the SiC surface with the help of high temperature annealing by controlling the sublimation of Si in surface. The objective is to increase the surface roughness of 4H-SiC and thus, the reflection of the emitted light within the material decreases and its transmission through the SiC surface improves.

The samples we processed in this work are fabricated with off and on-axis n + -type doped 4H-SiC substrate on which were grown n -, p + and p ++ -type epitaxial layers (PIN diode structures). The off-axis SiC substrates are sliced with a 4° angle to the c-axis. Fluorinated ICP/RIE plasma etching was used to isolate the p-type zones of the surface (mesa structures) where we want to increase the roughness in order to extract the emitted light. After mesa etching, the p-doped layers of the samples were encapsulated with an AZ9260 photoresist and we used photolithography to remove the photoresist, creating windows where we want to nanostructure the surface. High temperature annealing process of 30 min plateau at different temperatures occurred then at 1700°C, 1600°C and 1500°C with a dedicated 6" RTP AET furnace with graphite resistors. For each temperature, photolithography is used on a different p-type top surface of the samples. The resin is removed by O2 plasma ICP/RIE after each annealing and the samples are cleaned with Piranha and BHF solutions. After each annealing the samples have been analyzed by SEM FEG SU8030 Hitachi and Bruker Dimension Icon AFM.

Our results presented in Figure 1, show that the roughness of the zones which are not protected by the photoresist during annealing tends to increase with the annealing temperature. Nevertheless, the surface nanostructuration morphology differs from the samples with on and off-axis substrates (Fig. 2). The mass transport during Si sublimation at the highest annealing temperatures drives into a needle-shaped surface more pronounced for the off-axis samples. This surface state has a morphology similar to blacksilicon [8] and thus expected to obtain interesting optical properties, with very low reflectivity and high absorption of visible (and infrared) light. When the annealing temperature decreases, these needles become larger and look more like peaks at an annealing temperature of 1500°C (Fig. 2). Close to the limit of the opened windows, a change of surface microstructure can also be observed as well as a light etching of the surface (Fig. 3.). The etching depth tends to increase with the temperature and when the temperature of annealing is too high (1700°C), the etching is strongly accentuated near from the limit of the opened windows.

At the conference and in the final paper, we will present in details these results completed with measurements of electroluminescence from PiN LED structures presented under these nanostructured surfaces.

Fig. 2

 2 Fig. 2 Surface morphology obtained after high temperature annealing at 1700, 1600 and 1500°C (from left to right) on 4° off-axis ((a) to (c)) and on-axis ((d) to (f)) 4H-SiC substrates with top epilayers.

Fig. 1 .

 1 Fig. 1. Surface roughness (Ra) of the nanostructured patterns after annealing at different temperatures (1500°C, 1600°C and 1700°C) and of the protected surface of an off-axis 4H-SiC substrate with top epilayers. The protected surface is the surface covered by resin during annealing.


E Usureau

E Vuillermet

M Lazar

A Andrieux

A Jacquemot

High quality single crystal recrystallization of thin 4H-SiC films deposed by PVD techniques, a way for new emerging fields

Silicon carbide is a compound semiconductor material with particular physical and chemical properties, making it very attractive for power electronics applications and harsh environment systems and sensors. Bulk and homoepitaxial growth of SiC crystals requires specific reactors. 150-mm-diameter wafers are currently produced by seeded sublimation method at ~2300 °C, on which homoepitaxial growth of SiC is performed typically at 1500-1650°C by chemical vapor deposition [1]. The layers obtained with these sophisticate and specific techniques are difficult to be directly localized at the substrate surface and thus device fabrication with limited architectures needs post-growth plasma etching. In this paper we present high quality 4H-SiC layers obtained by more classic physical vapor deposition (PVD) techniques opening a way to design new SiC devices exploiting these PVD thin films for power applications and also other emerging fields, such as bio sensors, high temperature sensors, and optoelectronics or quantum nanophotonics.

Starting from a commercial 4°-off-axis 4H-SiC, we deposited amorphous SiC layers at room temperature (RT) and at 750°C by sputtering amorphous SiC target and by electron-beam evaporation techniques using an Elettrorava machine. The surface of the sample was cleaned by in-situ etching before deposition. In order to recrystallize the thin amorphous films (0.15 to 1.5µm thicknesses), samples have been cut in pieces of 25×25 mm and annealing steps under Ar have been performed in a dedicated 150-mm AET RTP graphite resistive furnace (Fig. 1) with a plateau at 1400°C and 1700°C during 30 min. Before the annealing at the highest temperature, the sample was encapsulated with an AZ photoresist, spin-coated on the both sides and pyrolyzed at 750°C under Ar in a second chamber (in quartz) of the AET furnace. The obtained graphite cap has been removed by plasma oxygen after RTP annealing. Before and after the RTP annealing the samples have been analyzed by Scanning Electronic Microscope (SEM) FEG SU8030 Hitachi and UV µRaman Spectroscopy (λex=325 nm) in backscattering geometry with LabRAM HR Evolution from Jobin Yvon Technology. Finally, current-voltage measurements have been performed with a probe-station in order to test the electric conductivity in lateral and vertical configurations.

SEM images presented in Figure 2 correspond to the SiC surface obtained after 1400°C or 1700°C annealing (C-cap retired). The surface-state is homogenous with a slightly undulating grainy morphology typically found on recrystallized SiC amorphous layers created by high doses RT ion implantation [2]. The µRaman spectra (Fig. 3) prove that the post-annealed PVD layers get 4H-SiC crystallinity with polytype substrate replication, the LO and TO peaks corresponding to the 4H-SiC polytype. This denotes a solid phase epitaxy from the PVD layer/substrate interface. We used UV laser excitation (our lowest set-up wavelength) in order to limit the penetration depth of the laser within the PVD layer in surface. For the crystallized PVD layers deposed at high temperature (750°C) we obtained an epitaxial quality (Fig. 3a) with an annealing temperature as low as 1400°C. The decrease of the LO peak after the annealing at 1700°C is probably due to the dopant activation.

High n-type conductivity is obtained on the PVD layers deposited by sputtering, as Figure 4 shows with electrical measurements, both in lateral and vertical configurations. As the tungsten tips are put directly on the SiC surface without metal electrodes, the linearity of the I-V curves gives evidence for the high doped layers we obtained. This high conductivity is not found on the e-beam layers, which is explained by the higher vacuum level with lower impurity incorporation during this process.

In the final paper and at the time of the conference we will provide more details about our results, complemented with tests on ohmic contacted metallized structures. [1] T. Kimoto, Prog. Cryst. Growth Charact. Mater. 62/2, 329 (2016) [2] M. Lazar, et al. Materials Science Forum, 353-356, 571 (2001)

  150 mm AET furnace for this study: RTP chamber with graphite resistors (a), pyrolyzed quartz chamber (b) and typical temperature and Ar pressure profiles for the RTP 1700°C 30 min plateau.

Fig. 2 .

 2 Fig. 2. SEM images from the PVD layers after recrystallisation (C-cap removed by O2 plasma) : SiC e-beam deposed at 750°C and annealed at 1700°C/30 min (a), SiC sputtered at RT and annealed at 1400°C/30 min (b) and 1700°C/30 min (c).

Fig. 3 .

 3 Fig. 3. UV Raman spectroscopy at 325 nm from PVD layers before and after annealing compared to initial 4H-SiC substrate: SiC e-beam deposed at 750°C (a) and SiC RT sputtered layers (b)


Laurent Br Éh Élin

Laurent Br

Advancing regulatory genomics with machine learning

Keywords: Regulatory Genomics, Gene Expression, Transcription Factor Binding Sites, Machine Learning, Deep Learning, Model Interpretation

In recent years, several machine learning approaches have been proposed to predict gene expression and epigenetic signals from the DNA sequence alone. These models are often used to deduce, and, to some extent, assess putative new biological insights about gene regulation, and they have led to very interesting advances in regulatory genomics.

This article reviews a selection of these methods, ranging from linear models to random forests, kernel methods, and more advanced deep learning models. Specifically, we detail the different techniques and strategies that can be used to extract new gene-regulation hypotheses from these models. Furthermore, because these putative insights need to be validated with wet-lab experiments, we emphasize that it is important to have a measure of confidence associated with the extracted hypotheses. We review the procedures that have been proposed to measure this confidence for the different types of machine learning models, and we discuss the fact that they do not provide the same kind of information.

Introduction

Machine learning (ML) has been used for decades in genomics and bioinformatics. Among many other examples, protein modeling with Hidden Markov models dates back to 1992 [START_REF] David | Protein modeling using hidden Markov models: analysis of globins[END_REF] , and the book of P. Baldi and S.

Brünak Bioinformatics: the machine learning approach was published in 1998 [START_REF] Baldi | Bioinformatics: The machine learning approach[END_REF] . In regulatory genomics specifically, position weight matrices (PWMs), which are the most common models for transcription factor (TF) binding sites, appeared in the late 80's [START_REF] Thomas | Information content of binding sites on nucleotide sequences[END_REF][START_REF] Stormo | Consensus patterns in DNA Methods in[END_REF] . In the following years, many algorithms have been proposed in the literature to estimate the parameters of a PWM from sequence examples [START_REF] Stormo | Consensus patterns in DNA Methods in[END_REF][START_REF] Bailey | Fitting a mixture model by expectation maximization to discover motifs in biopolymers Proceedings[END_REF][START_REF] Shuxiang | Comparison of discriminative motif optimization using matrix and DNA shape-based models[END_REF][START_REF] Timothy | STREME: accurate and versatile sequence motif discovery Bioinformatics[END_REF] .

Today, PWMs of hundreds of TFs are available in databases like JASPAR [START_REF] Oriol | update of the open-access database of transcription factor binding profiles[END_REF] and HOCOMOCO [START_REF] Ivan | HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis Nucleic Acids Research[END_REF] , and can be used to compute binding affinities and to identify potential binding sites in genomes.

In recent years, several ML approaches have been proposed to go beyond single TF binding sites, by modeling entire regulatory sequences spanning hundreds or even thousands base pairs. These models range from linear models [START_REF] Vandel | Probing transcription factor combinatorics in different promoter classes and in enhancers[END_REF] to random forests [START_REF] John | Predicting the Human Epigenome from DNA Motifs Nature methods[END_REF] , kernel methods [START_REF] Dongwon | Discriminative prediction of mammalian enhancers from DNA sequence[END_REF][START_REF] Mahmoud | Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features[END_REF] , convolutional neural networks [START_REF] Babak | Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning[END_REF][START_REF] Zhou Jian | Predicting effects of noncoding variants with deep learning-based sequence model[END_REF] , and more advanced deep learning approaches [START_REF] Daniel | DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences[END_REF][START_REF] Vaswani | Attention is All you Need in Advances in Neural Information Processing Systems[END_REF] . Notably, deep learning approaches adapted from methods initially developed for image and natural language processing have attracted considerable attention in the field. These studies take place in a supervised framework, where the goal is to train a model able to predict a signal measuring gene expression (RNA-seq, CAGE, . . . ), TF binding or histone marks (ChIP-seq, ATACseq, . . . ) on the basis of the DNA sequence only. Despite the supervised framework, in a large number of studies these models are not really used as predictors. Instead, the goal is to use the model to deduce, and to some degree assess, new biological knowledge and hypotheses about gene regulation; hypotheses that have then to be experimentally validated. Along with the availability of a huge quantity and diversity of genomic, transcriptomic and epigenetic data, these approaches have allowed very interesting progresses in regulatory genomics. Maybe one of the most striking results is the fact that gene expression can be predicted with often high accuracy from the sequence only, which means [START_REF] David | Protein modeling using hidden Markov models: analysis of globins[END_REF] LIRMM, Univ Montpellier, CNRS, France that a large part of the instructions for the control of gene expression likely lie at the level of the DNA. This is in contradiction with a common belief that gene expression first depends on chromatin marks that are not necessarily controlled by the DNA sequence [START_REF] Zeitlinger | Seven myths of how transcription factors read the cis-regulatory code Current Opinion in Systems[END_REF] . Actually, several of the early works in the field have shown that epigenetic marks can be predicted from the sequence alone, often with very good accuracy [START_REF] John | Predicting the Human Epigenome from DNA Motifs Nature methods[END_REF][START_REF] Mahmoud | Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features[END_REF][START_REF] Zhou Jian | Predicting effects of noncoding variants with deep learning-based sequence model[END_REF] .

Beyond this very general result, other biological hypotheses can be deduced from ML models. As we will see in this review, different strategies/methods can be used to deduce and assess interesting biological hypotheses with these models. Certain general knowledge, such as the cell specificity of a regulatory mechanism, can be quite easily tested by learning different models for different conditions and comparing their accuracy. However, for more specific knowledge, such as the DNA motifs involved in a specific regulation, we have to directly analyse the learned model, and this knowledge-extraction process is highly dependent on the model type. In addition, because this putative knowledge must be validated with wet-lab experiments, it is important to have a measure of importance associated with each of the extracted hypotheses. However, we will also see that the different models do not provide the same kind of importance measure. Specifically, for some models these measures can be considered as objective, in the sense that they rely directly on the error of the model, estimated from the data. For other approaches, however, these estimates are more subjective, as they are computed solely on the basis of the signal predicted by the model, with no link to the associated error.

There are several recent reviews devoted to deep learning approaches for modeling genomic sequences [START_REF] Gökcen | Deep learning: new computational modelling techniques for genomics[END_REF][START_REF] Peter | Deep learning for inferring transcription factor binding sites Current Opinion in Systems[END_REF][START_REF] Talukder Amlan | Interpretation of deep learning in genomics and epigenomics[END_REF][START_REF] Routhier Etienne | Genomics enters the deep learning era[END_REF] .

In addition, several other papers address the problems of model interpretation in machine learning-see for example references [START_REF] Grégoire | Methods for interpreting and understanding deep neural networks Digital[END_REF][START_REF] Zachary | The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery[END_REF][START_REF] Leilani | Explaining Explanations: An Overview of Interpretability of Machine Learning[END_REF][START_REF] Amirata | Interpretation of Neural Networks Is Fragile Proceedings of the[END_REF] . This review differs from these papers on several points: i) it is specifically dedicated to the modeling of genomic sequences involved in the regulation of gene expression; ii) it presents a wide variety of models, without being restricted to deep learning approaches; iii) it makes an inventory of methods and practices that can be used to extract different levels of biological knowledge from these models. This review is intended to biologists and computational biologists who are curious to know how machine learning can be used to deduce new biological hypothesis about gene regulation. Special attention has been paid to explaining the mathematical concepts simply and intuitively but also in a sufficiently detailed way, so that readers without knowledge of machine-learning should be able to fully understand the advantages and limitations of the different approaches. This paper does not intend to provide an exhaustive list of methods and approaches that use machine learning to study regulatory sequences. Rather, the aim is to develop and provide the reader with the technical concepts necessary to understand this literature. Most of the works presented below have been selected for their pioneering aspect, in the sense that they are, to our knowledge, the first to introduce a specific type of machine learning model or to apply a specific method to evaluate new hypotheses or to interpret a given machine learning model. This review is organized as follows. We first present a selection of models for predicting either epigenetic marks or expression signals. The following sections discus how these models are used to infer new biological hypothesis, and give some examples of knowledge that were gained from these studies. Specifically, we give a brief literature tour on the identification and prioritization of nucleotide variants. Next, we describe simple approaches that are used to assess very general hypotheses with these models. Finally, the last section is devoted to the extraction of more complex DNA features, and to the measures of importance that are associated with these features.

Machine learning for regulatory genomics

Given a genome-wide experiment (RNA-seq, ChIP-seq, ATAC-seq, . . . ) monitoring a specific signal (gene expression, TF binding, histone mark, . . . ) in a specific condition (cell type, time point, treatment, . . . ), the aim is to learn a model able to predict this signal based on the DNA sequence alone. We have a set of sequences X = {x 1 , . . . , x N }, each sequence x i being associated with a signal y i . For classification problems (typically when predicting TF binding or histone mark) y i is limited to two values (e.g. -1 / +1) indicating whether the i-th sequence is or is not bound by the studied factor in the ChIP-seq experiment. For regression problems (typically when predicting a gene expression signal) y i is a continuous value that measures the expression associated with i-th sequence (in this case, sequences may be for example gene promoters or enhancer sequences).

Therefore, the goal is to learn a prediction function f (x) that predicts the signal associated with sequence x. An important remark is that the function f is associated with a specific experiment and therefore with specific conditions: if the conditions change (for example if the cell is treated with a new drug) the function f may no longer be a good fit of Prepared using sagej.cls the cell and another predictor should be learned from data monitoring these new conditions. Note that even when the purpose of the study is not to make predictions per se, the prediction framework keeps several interests: first, when estimating the accuracy of the model we also assess the amount of information that has been captured by the model-and, maybe more importantly, what remains to be captured. Second, in comparison to simple correlation analyses that can also identify some links between sequence features and the target signal, predictive models enable the combination of several DNA variables and hence reveal cellular mechanisms that cannot be studied with simple correlation analyses.

In the following, we distinguish the methods that predict an expression signal from those that predict epigenetic marks.

Predicting epigenetic marks

Support vector machines One of the first studies dedicated to the prediction of ChIP-seq signal based on long regulatory sequences was the kmer-SVM approach [START_REF] Dongwon | Discriminative prediction of mammalian enhancers from DNA sequence[END_REF] , latter upgraded in gkm-SVM [START_REF] Mahmoud | Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features[END_REF] . Support vector machines (SVMs) are among the most successful methods of ML [START_REF] Cortes | Support-vector networks Machine Learning[END_REF] . They work by searching for an hyperplane that best separates the training examples according to their classes. However, rather than searching for an hyperplane in the original space of the data, SVMs work in a usually much higher dimensional space (with possibly infinite dimension). As a result, while the separating hyperplane is by definition linear in the enlarged space, it can define non linear boundaries in the original space. The beauty of SVMs is that we do not need to define the enlarged space. The only thing we have to define is a kernel function that measure the similarity of any pair of examples. The "trick" is that the position of a new example relative to the separating hyperplane can be computed by a linear combination of the similarity of this new example with all training examples, i.e. Thus, the accuracy of the approach all depends on the chosen kernel function K(x, x ′ ) which must be meaningful

f (x) = b + N i=1 α i y i K(x, x i ), (1)
for the problem at hand. The authors of kmer-SVM [START_REF] Dongwon | Discriminative prediction of mammalian enhancers from DNA sequence[END_REF] proposed a kernel function based on the similarity of the kmers present in the sequences. More formally, each sequence x is encoded by a vector that reports the number of occurrences of each of the 4 k k-mers in x (for a given k-mer size k). Sequence pairs that have the same relative frequency for each k-mer have K(x, x ′ ) = 1, while sequences that have not any k-mer in common have K(x, x ′ ) = 0. The method has been improved a few years later by the gkm-SVM approach that uses gapped k-mers (i.e. k-mers with a certain number of non-informative positions for which any nucleotide is possible) [START_REF] Mahmoud | Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features[END_REF] , but the principle remains the same:

training sequences that most resemble the new sequence x in terms of (gapped) k-mers composition have more weight in Expression (1), and drive the predicted class toward their own class. The gkm-SVM approach has been applied to 467 human ChIP-seq experiments from the ENCODE project [START_REF]The ENCODE (ENCyclopedia Of DNA Elements) Project Science[END_REF] .

For each ChIP-seq experiment, sequences associated with a ChIP-seq peak were extracted and used as positive sequences (authors report an average sequence length of around 300bps), while the same number of random genomic sequences were used as negative sequences. Then, an SVM (one for each ChIP-seq experiment) was learned to discriminate between these two sets, and the accuracy of each of these 467 SVMs was estimated by the Area Under the Receiver Operating Curve (AUROC). The approach showed very good results, with AUROC above 80% for most datasets. As for any ML method, to avoid any optimistic bias the accuracy estimate must be computed on left-out sequences, i.e. positive and negative sequences that have not been used to train the model.

Random forests Another method that has been proposed for the prediction of ChIP-seq signal is random forests (RFs) [START_REF] John | Predicting the Human Epigenome from DNA Motifs Nature methods[END_REF] .

RFs [START_REF] Leo | Random Forests Machine Learning[END_REF] got considerable success and attention in the ML literature in the last twenty years. RFs are an extension of decision trees, which are certainly among the oldest approach in ML [START_REF] James | Problems in the Analysis of Survey Data, and a Proposal[END_REF] . A decision tree encodes a set of tests that can be done on the features of a given example to predict its class (see Figure 1). As the name suggests, these tests are organized in a tree structure that provides the order in which the tests are done (from the root to the leaves of the approach was measured by the AUROC and showed very good results above 80% for most histone marks and cell types.

Convolutional neural networks After SVMs and RFs, several authors proposed to predict TF binding and histone marks with deep neural networks [START_REF] Babak | Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning[END_REF][START_REF] Zhou Jian | Predicting effects of noncoding variants with deep learning-based sequence model[END_REF] . The simplest form of neural networks is the feedforward neural networks (see The same year, Zhou & Troyanskaya proposed another CNN approach named DeepSEA 15 . DeepSEA has three convolutional/pooling layers followed by a fully connected layer. It was applied to 690 TF binding profiles, 125 DHS profiles and 104 histone-mark profiles from ENCODE and the Roadmap Epigenomic projects [START_REF] Dunham Ian | An integrated encyclopedia of DNA elements in the human genome[END_REF][START_REF] Anshul | Integrative analysis of 111 reference human epigenomes[END_REF] . For each predictor, the positive sequences were the sequence associated with the mark, while the negative sequences were randomly selected among the sequences that are not associated with the mark but that are associated with another mark in another data. CNN accuracies were estimated by the AUROCs. DanQ has a single layer of convolutions that identify the motifs present in the input sequence, but this convolution is followed by a layer of recurrent neural network known as long short term memory (LSTM). As for convolutional filters, in a recurrent network the same function is applied to every input. However, contrary to CNN, this function also has a memory of its previous outputs. In this way, the computed output value depends both on the current input and on the previous outputs. In DanQ the LSTMs take in input the result of the convolution filters at every position of the sequence. Hence, the output of the LSTM depends both on the value of the filters at the current position and on the output of the LSTM at the previous positions. Moreover, DanQ implements two LSTMs: one that reads the sequence forward, and one that reads the sequence backward. The LSTM layer is then followed by a fully connected layer.

Input Output (A) (B)
DanQ was applied exactly to the same data as DeepSea.

Its accuracy, measured by AUROC and by the area under the precision-recall curve (AUPRC), showed a slight but constant improvement over DeepSea.

Other CNN approaches have been proposed in the following years for predicting TFBS and chromatin marks.

For example, the authors of BPNet slightly change the goal of the approach [START_REF] Žiga | Base-resolution models of transcription-factor binding reveal soft motif syntax[END_REF] . Rather than addressing a simple classification problem where the aim is to predict whether or not a sequence has a specific mark/TF, the goal of BPNet is to predict the ChIP-seq profile of the sequence, i.e. the expected number of reads at each base pair. According to the authors, training the model on the profile enables them to capture subtle regulatory features that are not captured when training on binary signal, a behavior also reported in [START_REF] Shushan | Evaluating deep learning for predicting epigenomic profiles tech[END_REF] .

Logistic models Following the former CNN approaches, our group proposed to predict TF binding with an approach based on a logistic model named TFcoop [START_REF] Vandel | Probing transcription factor combinatorics in different promoter classes and in enhancers[END_REF] . Logistic models are one of the oldest and most used approaches for binary classification problems. For a given example x = (x 1 , . . . , x M ) described by M variables, a logistic model expresses the probability that x belongs to the first class with a linear expression

P (1|x) = S a + M i=1 b i • x i , (2)
where P (1|x) is the probability that example x belongs to the first class, S is the sigmoid function, and a and b i are the regression coefficients which constitute the parameters of the model. While being quite simple, logistic and generalized linear models have gain considerable interest in domains with high-throughput data (like genomics), thanks to the development of modern regularization methods [START_REF] Jerome | Regularization Paths for Generalized Linear Models via Coordinate Descent[END_REF] . For problems with high amount of data, and in situations where the number of variables (i.e. M in Expression (2)) may be larger than the number of examples, these models can be trained very quickly and without being much affected by over-fitting. The LASSO penalty especially [START_REF] Robert | Regression Shrinkage and Selection Via the Lasso Journal of the Royal Statistical Society[END_REF] , is a powerful regularization technique that allows one to train a model and select the most important features at the same timei.e. many regression coefficients of Expression (2) (a and b i ) are set to zero by the learning algorithm-, which is of obvious interest when we are also interested to understand how a predictor works [START_REF] Peng | On Model Selection Consistency of Lasso Journal of Machine Learning Research[END_REF] .

As for SVMs and RFs (and contrary to CNNs) the accuracy of the approach essentially depends on our ability to provide to the learning algorithm a set of variables that contains meaningful information for the discrimination problem. In TFcoop were able to discriminate the binding sites of the same TF in different cell types with often good accuracy (AUROC ¿ 80%), and to identify the genomic features the were associated with these binding differences (see last section).

Predicting expression signal

Following the prediction of TF binding and chromatin marks, In 2021, we proposed a second method named DExTER that aimed to predict gene expression from the presence of low complexity sequences in promoters [START_REF] Christophe | Identification of long regulatory elements in the genome of Plasmodium falciparum and other eukaryotes[END_REF] . DExTER takes 4Kb sequences centered on the TSS (or gene start) and identifies long regulatory elements defined by a specific region (for example -500/+300 around the TSS) and a specific short k-mer (for example TAA). For this, DExTER computes the correlation between gene expression and the frequency of the k-mer in the region, and searches for pairs of (k-mer, regions) that show the highest correlation with gene expression. All identified pairs are then used as predictive variables of a linear model trained to predict gene expression with LASSO penalty (each model involves a few dozens parameters). The approach was applied to various organisms (malaria parasite, yeast, worm, human, plant, . . . )

in various conditions (cell types or development stages)

and still showed a surprising accuracy of 60% correlation between measured and predicted expression in average. For the malaria parasite, the accuracy even increases above 70%

in several life-cycle stages, indicating that low complexity sequences could have a predominant role in the control of gene expression for this organism.

Deep learning approaches In 2018, Zhou et al. [START_REF] Zhou Jian | Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk[END_REF] proposed the ExPecto approach, which capitalizes on the DeepSea method that the same group previously published (see above section) [START_REF] Zhou Jian | Predicting effects of noncoding variants with deep learning-based sequence model[END_REF] . The approach involves 3 steps.

Identifying and prioritizing genomic variants

Maybe one of the most common applications of machine learning, and especially deep learning, for regulatory Prepared using sagej.cls genomics has been the identification and prioritization of genomic variants from genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies. GWAS use statistical methods to identify genetic loci associated with common diseases and traits. This involves the analysis of thousands of variants in large cohorts of individuals, often split into cases and controls, to identify variants associated with the trait of interest (i.e.

genomes with this variant have often this trait/pathology).

Similarly, eQTL studies identify associations between genomic variants and the expression of specific genes (genomes with this variant often show over-expression of gene X). GWAS and eQTL studies have identified thousands of variants statistically-associated with a physiological trait or an expression profile, which are available in dedicated databases [START_REF] Macarthur | The new NHGRI-EBI Catalog of published genomewide association studies (GWAS Catalog)[END_REF][START_REF] Franc | Genetic effects on gene expression across human tissues[END_REF] . However, because variants are not independent (an effect called linkage disequilibrium), many variants are often present together in the same genomes. As a consequence, it is widely believed that only a few GWAS or eQTL variants are truly functional [START_REF] Franc | Genetic effects on gene expression across human tissues[END_REF] . In these conditions, an interesting idea is to use the ML models described above for the identification and prioritization of functional variants.

In its simplest form, the procedure involves taking a specific model-predicting for example if a sequence is bound by a given TF-and computing the prediction of this model at a given locus using i) the reference genome and ii) the mutated genome associated with a specific variant (for example a specific SNP). Variants that increase or decrease significantly the predicted signal are more likely to have a strong effect on the cell and hence to be functional [START_REF] Babak | Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning[END_REF][START_REF] Zhou Jian | Predicting effects of noncoding variants with deep learning-based sequence model[END_REF][START_REF] Daniel | DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences[END_REF][START_REF] Zhou Jian | Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk[END_REF] . Using this principle, the authors of DeepBind proposed a new genomic representation called mutation maps to visualize the effect that every possible point mutation in a sequence may have on binding affinity [START_REF] Babak | Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning[END_REF] .

More sophisticated approaches have also been proposed to identify variants using a combination of ML models.

For example, a deep learning model that uses the score of ∼ 600 DeepBind transcription factor models has been trained to discriminate between high-frequency variants (assumed as neutral) and putatively deleterious variants from GWAS studies [START_REF] Babak | Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning[END_REF] . This model takes in input the score of the ∼ 600 DeepBind models both for the wild type sequence (reference genome) and the mutant sequence.

Similarly, a boosted logistic regression classifier and a boosted ensemble classifier have been trained to discriminate between high-frequency SNPs and putatively deleterious variants from GWAS and eQTL studies using the epigeneticmark predictions of DeepSea and DanQ, respectively [START_REF] Zhou Jian | Predicting effects of noncoding variants with deep learning-based sequence model[END_REF][START_REF] Daniel | DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences[END_REF] .

Assessing regulatory hypotheses with ML models

Besides the prioritization of genomic variants, the ML approaches presented here, along with many others and the concordant development and availability of various omics data, have enabled several advances in regulatory genomics over the past decade. For this, the supervised framework offers the possibility to test various hypotheses by simply training a model and estimating its accuracy on specific problems. This can be done in various ways. One approach is to directly control the nature of the input of the model. For example, with Xpresso, Agarwal & Shendure studied the impact of sequence length on model accuracy [START_REF] Vikram | Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks[END_REF] .

They showed that although the large promoter sequence - Rather than controlling the sequences used for learning the model, another simple technique that can be used to test some hypotheses is to compare the accuracy achieved by a given model on different sets of sequences. For example,

Agarwal & Shendure studied the impact of enhancers on the prediction of Xpresso [START_REF] Vikram | Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks[END_REF] . Xpresso only uses the sequence located around the TSS for the prediction and hence cannot handle distal regulatory elements. Given that enhancers are frequently associated with large domains of H3K27Ac activity, the authors identified a set of 4,277 genes that overlap H3K27Ac marks and observed that the predicted expression level on these genes is markedly smaller than the measured expression, a trend that was not observed for the other genes. Hence, the expression of genes under enhancer control seems to be underestimated by the Xpresso model, which provides a practical way for identifying these genes. enhancers). On the contrary, models for mRNA promoters, lncRNA promoters and pre-miRNA promoters appeared to be perfectly interchangeable. Agarwal & Shendure also did swap experiments between human and mouse [START_REF] Vikram | Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks[END_REF] . Namely, they trained one Xpresso model on mouse and one on human using comparable RNA-seq experiments, and observed on a set of orthologous genes that the model learned on mouse has the same accuracy on human as the model directly learned on human (and conversely), meaning that the regulatory principles are likely very close in both organisms.

Breaking the regulatory code: Interpreting

ML models

Finally, the most ambitious application of ML is obviously to break the regulatory code and determine the rules used by the cell to regulate expression. A first step toward this goal is to identify all regulatory elements involved in the regulation process in question. Note that the term regulatory element has to be understood in its broadest sense here. It may stand for standard motifs of TF binding sites, but it can also refer to low complexity regions such as CpG islands [START_REF] Deaton Aimée | CpG islands and the regulation of transcription[END_REF] or short tandem repeat [START_REF] Melissa | Abundant contribution of short tandem repeats to gene expression variation in humans[END_REF] that are also involved in gene regulation, as well as any other kind of "sequence patterns" that we can think of, such as, for example, the DNA shapes [START_REF] Remo | The role of DNA shape in protein-DNA recognition[END_REF] . In addition to the regulatory elements, breaking the regulatory code also means determining how these elements are combined, and what rules they follow in terms of repetition, position, and orientation on the DNA sequence. Getting down to this level of detail involves analyzing the learned model to understand how it works. This is referred to as model interpretation in the ML community. There are several definitions of interpretability in the ML literature, and these definitions are often considered as domain-specific [START_REF] Zachary | The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery[END_REF][START_REF] Leilani | Explaining Explanations: An Overview of Interpretability of Machine Learning[END_REF][START_REF] Doshi-Velez Finale | Towards A Rigorous Science of Interpretable Machine Learning[END_REF][START_REF] Cynthia | Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead[END_REF] .

In the context of regulatory genomics, model interpretation has also several meanings, and we will see that the term covers different approaches that do not provide the same kind of information. The general problem is as follows: we have a predictive model that has been learned for a specific problem, and we want to understand how it works. There are at least three different ways to attempt this. The first and more direct approach is to explain the model by analyzing its different components and extracting the rules used to make its predictions. A second approach is to explain the predictions of the model rather than the model itself, i.e.

given a sequence and a prediction, we want to explain why the model predicted this value for this sequence. A third alternative is to try to explain the model by exploring its behavior on synthetic sequences. Theoretically, the three approaches are possible for any kind of model. However, as we will see, linear models and random forests can often be partly explained by a direct analysis of their components. On the contrary, this is more difficult to do for models based on CNNs, so different methods belonging to the second and third categories have been developed for these laters.

Explaining model components

In some cases, the model may be simple enough to be directly analysed as a whole. This view of interpretability argues for simple and sparse models (i.e. with few Prepared using sagej.cls parameters). This may be possible for some very small decision trees and linear models, but this is generally not the case for the regulatory models described above. Moreover, even for a simple linear model, it is known that interpretation is not obvious, as the sign of the coefficient associated with a specific variable (which is tempting to interpret as the sign of the association between this variable and the predicted signal) may change depending on the identity of the other variables included in the model.

A less stringent view of interpretability corresponds to models that can be broken down into different modules that are further analyzed [START_REF] Zachary | The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery[END_REF][START_REF] Leilani | Explaining Explanations: An Overview of Interpretability of Machine Learning[END_REF] . For regulatory genomics, this corresponds to the case where we can extract the DNA features used by the model for its predictions. Note that a DNA feature may be more complex than simply the identity of a regulatory element. For example, in the DExTER method, the predictive features also encode information about the position of the regulatory elements on the DNA sequence [START_REF] Christophe | Identification of long regulatory elements in the genome of Plasmodium falciparum and other eukaryotes[END_REF] . Hence, DNA features may potentially encode complex information related to the number of repetitions, position, and orientation of regulatory elements on the DNA sequence. Obviously, extracting the DNA features used by a model is relatively easy to do for all models that directly take these DNA features in input, such as logistic/linear models and random forests. On the contrary, for CNN approaches the task is more difficult as we will see below.

Random forests and linear models In these models, extracting the DNA features involves identifying which variables provided in the input are the most important for the predictions. ExPecto is an example of linear model where such interpretation is quite easy [START_REF] Zhou Jian | Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk[END_REF] . In this paper, the authors observed for example that, in most cases, the ExPecto models predict expression with features related to transcription factors and histone marks, but not with DNase I sequence features. Note that the DNA features extraction may be further facilitated if the learning algorithm directly includes a feature selection procedure such as the LASSO [START_REF] Robert | Regression Shrinkage and Selection Via the Lasso Journal of the Royal Statistical Society[END_REF][START_REF] Peng | On Model Selection Consistency of Lasso Journal of Machine Learning Research[END_REF] , which drastically decreases the number of predictive features used by the model.

Rather than simply providing the list of features used by the model, it is much more useful to also provide a measure of importance associated with each feature [START_REF] James | Definitions, methods, and applications in interpretable machine learning Proceedings of the National Academy of Sciences[END_REF][START_REF] Williamson Brian | A general framework for inference on algorithm-agnostic variable importance[END_REF][START_REF] Clément | Mean decrease accuracy for random forests: inconsistency, and a practical solution via the Sobol-MDA[END_REF] . For random forests, this is usually done by shuffling the values of each variable. Namely, the accuracy of the model is first estimated on the test set. Then, the values of the ith variable are randomly shuffled, and the loss of accuracy induced by this noise is used as a measure of the importance of the ith variable in the model [START_REF] Leo | Random Forests Machine Learning[END_REF][START_REF] Clément | Mean decrease accuracy for random forests: inconsistency, and a practical solution via the Sobol-MDA[END_REF] . By repeating this process, the relative importance of each variable is estimated. A very similar procedure also exists for linear/logistic models. In this procedure, the value of the coefficient associated with the ith variable is set to zero (which is equivalent to simply removing this variable from the model) and, as for random forests, the loss of accuracy induced by this operation is used as a measure of variable importance. In both cases, this measure can be considered as objective, as it is based on the loss of accuracy when removing information carried by each variable. This approach was extensively used in and that we could extract these filters to get the information.

It is however not so simple in practice. Koo & Eddy studied the way CNNs build representations of regulatory genomic sequences [START_REF] Peter | Representation learning of genomic sequence motifs with convolutional neural networks[END_REF] . They showed that the filters of the first layer actually encode partial motifs which are then assembled into whole DNA features in the deeper layers of the CNN. Hence, extracting the DNA feature associated with a specific filter is not as immediate. However, some attempts have been made in this direction. The authors of DeepBind proposed an interesting approach where all sequences that pass the activation threshold of the filter of interest are extracted and aligned on the position producing the maximum activation signal for this filter [START_REF] Babak | Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning[END_REF] . Then a PWM of predefined length m is learned from this alignment using standard PWMlearning methods [START_REF] Wyeth | Applied bioinformatics for the identification of regulatory elements[END_REF] . The same approach was also used for the DanQ model [START_REF] Daniel | DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences[END_REF] . The reconstructed PWMs were then aligned to motifs available in known-motif databases 8 using the TOMTOM algorithm [START_REF] Shobhit | Quantifying similarity between motifs[END_REF] . Of the 320 filters learned by the DanQ model, 166 significantly matched known motifs.

Hence, extracting the PWMs learned by a CNN seems possible at least partly, although the above approach does not completely warrant against the "partial motif" problem.

An open question that remains is how to associate a measure of importance to these PWMs? Theoretically, a CNN filter can be easily turned off, so it could be possible to estimate the accuracy of the CNN with and without the filter to have a measure of the importance of this filter for the predictions. However, as the CNN may model the same motif using different filters (or even using several filters that are combined in the deeper layers), turning a filter off does not warrant that the associated motif is also off, which may lead to underestimating its importance.

Explaining model predictions

Because of the partial motif problem, directly explaining a CNN remains a difficult question. Hence several methods have been proposed to explain the predictions of the model rather than directly explaining the model. This involves using the model on a specific sequence and identifying the nucleotides that have the highest weight for the computation of the predicted value. The simplest method (known as input perturbation or in silico mutagenesis) is similar to the mutation maps proposed for variant identifications. It involves systematically simulating every single-nucleotide perturbation of the input sequence, and recording the effect induced on the predicted value [START_REF] Babak | Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning[END_REF][START_REF] Zhou Jian | Predicting effects of noncoding variants with deep learning-based sequence model[END_REF][START_REF] An | Deep neural networks identify sequence context features predictive of transcription factor binding Nature machine intelligence[END_REF] . Computing model prediction on every variant that can be obtained from singlenucleotide perturbations of a sequence may however induce high computational cost, specifically if the sequence is long, or if the analysis is repeated over many sequences.

Backpropagation-based approaches have been proposed as more computationally efficient alternatives. The idea is to propagate an importance signal from the output neurons to the inputs through the different layers of the model using a backpropagation algorithm. In this way, a single pass is sufficient to compute the contribution of each nucleotide for the computation of the output value (these contributions can then visualize using saliency maps). Several approaches have been proposed on this idea (e.g. references [START_REF] Karen | Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps[END_REF][START_REF] Jack | Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks Pacific Symposium on Biocomputing[END_REF][START_REF] Avanti | Learning Important Features Through Propagating Activation Differences[END_REF] ). In Zheng et al. [START_REF] An | Deep neural networks identify sequence context features predictive of transcription factor binding Nature machine intelligence[END_REF] , a simulation-based study is used to benchmark these different approaches in the context of regulatory genomics.

By looking at the contribution scores of each nucleotide, one can identify the sub-sequences and regions that appear to be the most important for the prediction. Moreover, these sub-sequences can be further compared to known motifs to identify potential TFs involved in the regulation of the predicted signal. Hence, these approaches may highlight potentially interesting genomic information hidden into a specific sequence. However, it is important to note that because contribution scores are computed independently for each nucleotide, there is no warranty that the most important regions identified this way are really the most important regions for CNN prediction. Moreover, these analyses are restricted to some specific sequences and are not sufficient to understand the general mechanisms used by the cell to regulate gene expression at different loci. For this, further analyses are needed. A possible approach is to compute the contribution of every nucleotide of all positive sequences and to extract the recurrent DNA motifs from these contribution maps. A first method is to use these maps to compute the average importance of every k-mer of a fixed length. The most important k-mers are then identified and compared to known TF motifs using approaches like TOMTOM 64 . This is for example the approach used in Zheng et al. [START_REF] An | Deep neural networks identify sequence context features predictive of transcription factor binding Nature machine intelligence[END_REF] to identify several co-factors that likely explain the binding differences between positive and negative sequences of 38 TFs. Another more sophisticated approach is to directly learn DNA motifs (using PWMs or close models) from the contribution maps. This is the aim of the TF-MoDISco method that segments the contribution maps into seqlets (a kind of weighted kmers), and then clusters the seqlets in different motifs [START_REF] Avanti | Technical Note on Transcription Factor Motif Discovery from Importance Scores (TF-MoDISco) version 0[END_REF] . This approach has been used in Avsec et al. [START_REF] Žiga | Base-resolution models of transcription-factor binding reveal soft motif syntax[END_REF] to identify core motifs and potential co-factors in four ChIP-seq experiments.

Thus, different approaches based on prediction explanation can also be used to identify motifs that are likely encoded into the structure of the CNN. However, in our opinion an important difference with approaches based on the analysis of model components is that these methods lack an objective measure of importance associated with each motif. Indeed, it is not possible to turn the discovered motifs off in the CNN, and thus we cannot measure the accuracy of the model with and without a motif. Another problem is that there is no warranty that all important motifs used by the CNN were identified by the extraction procedure. Actually, it is even possible that some regulatory elements learned by the CNN but which were not identified by the motif extraction approach-for example because these elements do not fit the kind of motifs searched by the extraction procedureare actually more important for model predictions than the identified motifs.

Prepared using sagej.cls

Exploring model behavior on synthetic sequences

In the above sections, we have seen methods to extract some of the motifs that have been learned by a CNN for predicting a given signal. However, these interpretation methods are limited to the identification of simple motifs and cannot handle more sophisticated DNA features, such as the number of repetitions of a motif, the combination of several motifs, or their relative position on the sequence. For this, other approaches based on synthetic sequences have been proposed.

The first approach for this was the DeMo dashboard [START_REF] Jack | Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks Pacific Symposium on Biocomputing[END_REF] , which was inspired by the work of Simonyan et al. [START_REF] Karen | Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps[END_REF] to interpret CNNs in the context of image recognition. DeMo aimed to predict TF binding with a CNN. To identify the genomic features captured by the CNN, the authors proposed to construct the best synthetic sequence that would maximize the binding probability according to the CNN. The idea was to provide the users with an archetype of the sequences with the highest binding probability, which supposedly bears the DNA features required for the binding. The idea was interesting but may miss certain features, especially if several (exclusive) rules govern the binding.

To go one step forward, Koo et al. [START_REF] Peter | Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks[END_REF] proposed an approach named global importance analysis that samples a large number of synthetic sequences with and without a specific DNA feature, and uses the learned CNN as an oracle to predict the binding associated with each sequence. If the predicted binding signal is statistically higher for the sequences that embed the studied DNA feature than for the others, the feature is considered important. The approach is interesting in that it can be used to assess any feature.

For example, Koo et al. [START_REF] Peter | Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks[END_REF] used it to show that increasing the number of repeats of the RBFOX1 motif increases the binding probability of the sequence according to their model. Similarly, they also showed that including some GC bias in sequence 3'end also increases this probability. The approach was also used in Avsec et al. [START_REF] Žiga | Base-resolution models of transcription-factor binding reveal soft motif syntax[END_REF] to study the sinusoidal pattern that seems to regulate the distribution of the binding sites of the Nanog TF and its co-factors along the DNA sequence.

It is important to understand that this approach is not a fully automatic method that would extract all DNA features captured by a CNN. Rather, specific hypotheses have to be constructed before being assessed by generating appropriate synthetic sequences. In this sense, the approach is similar to that of the ML models that directly take meaningful DNA features in input: in both cases, one can discover solely what has been formerly hypothesized.

One advantage of this approach is that it can be used to estimate the relative importance of different features.

Note however that this measure of importance is somewhat subjective, as it is only dictated by the model, with no link with its accuracy. One issue is that the approach does not comply with a fundamental assumptions in ML, which states that the training and test sets must be representative of the samples to classify. Here, the synthetic sequences do not follow exactly the same distribution as the sequences used to train the model and to estimate its accuracy. As a result, this accuracy may be an optimistic estimate of the accuracy of the model on the synthetic data. Say differently: even if the model is quite good on the test sequences, it may be inaccurate on the synthetic sequences. This is related to the notions of extrapolation vs. interpolation. While ML models can be good at predicting a signal associated with a sequence that is close to other sequences in the training set (interpolation problem), when the sequence is far from any example of the training set (as the synthetic sequences can be) the problem is obviously more difficult, even if CNNs seem to be able to handle a certain level of extrapolation [START_REF] Randall | Learning in High Dimension Always Amounts to Extrapolation[END_REF] .

Note that the approach can nonetheless propose interesting hypotheses, which then need to be validated experimentally; however, users/experimentalists should be aware that the probability that these hypotheses holds can be lower than the model accuracy may lead one to believe.

Conclusions

Over the past decade, several ML models have been applied to the modeling of long regulatory sequences and have led to substantial advances in our understanding of regulatory genomics. These studies take place in a supervised framework, which enables, among other advantages, a fair comparison of model performance in terms of accuracy. On the contrary, identifying complex DNA features requires knowledge-extraction procedures that must be specifically adapted to the model at hand. The task is obviously easier for ML models that directly take these features in input than for CNN-based models that learn the predictive features from raw sequences. However, it would be inaccurate to consider these latter as complete black boxes, as several approaches are now available to extract such information. Notably, the oracle approach based on synthetic sequences looks like a promising avenue [START_REF] Peter | Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks[END_REF] , provided that the generated sequences remain close to the training sequences to avoid misleading conclusions. Actually, in our opinion, the main limitation of CNN models lies in the difficulty to globally and objectively assess the importance of the extracted features. A related issue is the fact that we do not know with precision whether all important features have been extracted or if the CNN actually uses additional important features that were not identified [START_REF] James | Definitions, methods, and applications in interpretable machine learning Proceedings of the National Academy of Sciences[END_REF] .

We have presented in this review some approaches for identifying the DNA features used by a specific model to make its predictions. A question that is rarely addressed in these studies is the stability of the model. DNA features are often highly correlated. For example, motifs of TFs of the same family are often very close. Moreover, motifs of co-factors often appear together at the same loci. Similarly, certain motifs are strongly enriched in DNA sequences with specific nucleotide or dinucleotide content. These strong correlations induce that different models with different DNA features may have close accuracy. As a result, the model that has been learned is not necessarily the only "good" model, a problem sometimes referred to as the Rashomon effect in ML (after the 1950 japanese movie directed by Akira Kurosawa) [START_REF] Leo | Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)[END_REF][START_REF] Fisher Aaron | All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously[END_REF] . From this perspective, by aggregating the predictions of many models, ensemble ML approachesand hence random forests-are less prone to this issue [START_REF] Leo | Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)[END_REF] .

For other methods, analysis of model stability can help [START_REF] Nicolai | Stability selection Journal of the Royal Statistical Society: Series B[END_REF][START_REF] Miriam | Feature Selection Stability and Accuracy of Prediction Models for Genomic Prediction of Residual Feed Intake in Pigs Using Machine Learning Frontiers in[END_REF][START_REF] Sarah | On the Stability of Feature Selection Algorithms[END_REF] .

However, this requires repeating the learning procedure several times using slightly different learning sets, which can involve prohibitive computing time for very complex models.

  with b and α i the parameters of the SVM estimated by the learning algorithm, y i (-1/+1) the class of the i-th sample of the training set, and K(x, x i ) the result of the kernel function between sample x and the i-th example of the training set. The sign of f (x) gives us the position of x relative to the hyperplane (up or down) and hence the predicted class of the sample. Moreover, only some training examples are associated with a α i different from 0 (the so-called support vectors), so in practice Expression (1) is computed without the need to compute the kernel function for all training examples.

Figure 2 .

 2 Figure 2.A). These networks are defined by a (potentially very large) set of neurons, inter-connected and organized in layers. The first layer is connected to the input values of the network, while the last layer encodes its output. In the classical feedforward network, each neuron takes in input the output of the neurons of the previous layer. It then computes a weighted sum of these values using its own set of weights, applies a simple activation function that realizes a non-linear transformation of this sum, and dispatches the computed value to the neurons of the following layer. When new values are applied to the input layer of the network, the outputs of all neurons are computed iteratively layer by layer until reaching the last one. For classification problems, the last layer is usually composed of a single neuron that produces a value in between 0 and 1 representing the probability that the example provided in input belongs to the positive class. The architecture of the network (the number of layers, neurons, and all the connections between them) as well as the set of weights associated with each neuron and the form of the activation functions define the parameters of the network.As for RFs and SVMs, specialized learning algorithms can be used to train a neural network by minimizing its

  DeepSEA takes input sequences of 1kb length, much longer than DeepBind (100bp) but very similar to the size of the sequences used by Epigram (see above). While a direct comparison of DeepSEA and Epigram has not been explicitly done, both approaches report an AUROC of ∼ 85% on average for predicting histone marks on comparable positive and negative sequences. Following DeepSEA, Quang & Xie proposed DanQ 16 .

Figure 2 .

 2 Figure 2. Neural networks.(A)An example of feedforward network with 4 layers. The first layer is connected to 9 input values (in black). The last layer involves a single neuron that outputs a value between 0 and 1. The width and color of the lines represent the weights and signs associated with the inputs to each neuron. (B) An extract of a convolutional neural network that scans DNA sequences. The input sequence is encoded as a one-hot matrix. A filter of length 5 scans the inputs and computes a convolution at each position. A non-linear activation function then filters these values by removing all values below a given threshold. Finally, the results of these operations are pooled by groups of 4, reducing the output of the convolution to only 4 values. On the figure, only one filter is represented, although each convolution layer usually involves dozens or hundreds filters with their own activation and pooling operations. The results of all filters are then combined in the following layers of the network (not represented here).

  [START_REF] Vandel | Probing transcription factor combinatorics in different promoter classes and in enhancers[END_REF] , each sequence was described by the affinity scores of all PWMs of the vertebrate JASPAR database[START_REF] Oriol | update of the open-access database of transcription factor binding profiles[END_REF] , and by the relative frequency of every dinucleotide. Affinity scores and dinucleotide frequencies were computed on the entire sequences. The data thus resembles that used in Epigram[START_REF] John | Predicting the Human Epigenome from DNA Motifs Nature methods[END_REF] , except that in this latter the PWMs were trained de novo and the dinucleotides were not used as predictive variables. TFcoop was applied on 409 ChIP-seq experiments from ENCODE. The positive sequences were either the promoters (+/-500bp around the TSS) or the enhancers (+/-500bp around the peaks defined by the FANTOM5 project[START_REF] Robin | An atlas of active enhancers across human cell types and tissues[END_REF] ) with a peak in the studied ChIP-seq experiment, while the negative sequences were the promoters or enhancers without a peak. The goal of TFcoop was to study the rules governing TF combinations in promoters and in enhancers, as well as for different gene categories. The accuracy of TFcoop was assessed by the AUROC. Our experiments on the same sequence sets showed that its accuracy was close to that of DeepSea.Recently, we proposed another logistic+LASSO model named TFscope whose aim is to identify the key regulatory elements that differentiate two ChIP-seq experiments[START_REF] Romero Raphaël | Systematic analysis of the genomic features involved in the binding preferences of transcription factors[END_REF] .TFscope is used to analyze the binding differences of one TF in two conditions (two cell types or two treatments), or of two paralogous TFs with similar PWMs. Contrary to the above approaches, in TFscope the positive and negative sequences are the sequences associated with a peak in the first and second ChIP-seq experiments, respectively.As in TFcoop, the logistic model integrates affinity scores of JASPAR PWMs as well as the nucleotide environment of the sequence. However, these variables are not simply computed on the entire sequences but on different regions of interest identified by TFscope. Moreover, the logistic model also integrates the score of a discriminative PWM which is directly learned from the sequences, in order to identify the subtle differences that may exist between the binding sites of the target TF in the two experiments. With this model, we

  several approaches have been proposed to directly predict gene expression from the sequence. While for the former problem the sequence length rarely exceeds 1000 bp, for expression signals there is a clear tendency to propose new approaches that can handle increasing sequence lengths to capture long-range interactions with distant regulatory sequences (enhancers/silencers).Linear models In 2018, our group proposed a linear regression model[START_REF] Chloé | Probing instructions for expression regulation in gene nucleotide compositions[END_REF] , for predicting the RNA-seq signal associated with a gene from the nucleotide and dinucleotide frequencies computed on different DNA regions of the gene: its core promoter (+/-500bp around TSS), distal upstream (-2000bp before TSS) and downstream (2000bp after TSS) promoter regions, 5' and 3' UTRs, exons, and introns. The aim was to study the links between gene expression and the nucleotidic/dinucleotidic composition of specific parts of the genes. With this approach, each gene is described by a vector of ∼150 variables, and a linear model with LASSO penalty is trained to predict the expression signal (hence, each model involves ∼ 150 parameters). This approach was applied to 241 human RNA-seq datasets from the TCGA database[START_REF] Roger | Comprehensive genomic characterization defines human glioblastoma genes and core pathways[END_REF] , and each model was assessed by computing the correlation between the measured and predicted expression signal. This approach showed modest accuracy (50-60% correlation) but still very surprising given the simplicity of the features used for prediction, and questioned our understanding of the role that simple nucleotidic and dinucleotidic enrichments may have on the control of gene expression.

  7Kb/+3.5Kb around the TSS provides the best accuracy, most of the information for the control of mRNA expression lies in -1.5Kb/+1.5Kb because these sequences provide approximately the same accuracy as the longer -7Kb/+3.5Kb.Similar results were obtained with the deepSTR model for studying the regulation of the transcriptomic signal that initiates at microsatellites[START_REF] Mathys | Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network[END_REF] . Specifically, the analyses shown that the 50bp sequence centered on the 3' end of the microsatellites is sufficient to predict the CAGE signal with often good accuracy. Similarly, by limiting the input features to the frequency of short kmers on specific long DNA regions, we have shown with DExTER that low complexity regions may play an unsuspected role in gene expression regulation of P. falciparum and several other eukaryotes[START_REF] Christophe | Identification of long regulatory elements in the genome of Plasmodium falciparum and other eukaryotes[END_REF] .Another procedure to test different hypotheses is to control the way positive and negative examples are selected. For example, in reference[START_REF] An | Deep neural networks identify sequence context features predictive of transcription factor binding Nature machine intelligence[END_REF] the authors study TF binding with a CNN. The originality of this study is that the sequences are selected in a way that insures that the TF motif is present both in the positive (bound) and negative (unbound) sequences. In this way, the predictive model cannot simply use the presence/absence of the target motif to discriminate the sequences, and the good accuracy of the approach showed that other features are likely involved in the binding site recognition mechanism of the studied TFs. To go one step further, the authors restricted all sequences to be within DNaseI hypersensitive sites (a mark of accessible chromatin). They trained a new model and observed a drop in the accuracy, suggesting that DNA features related to open chromatin are likely involved in this recognition process.

Finally, a very

  useful tool available in the ML toolbox is swap experiments. Model swapping is used to test hypotheses related to the specificity of a regulation mechanism. The procedure involves learning different models on different conditions (says conditions A and B), and to compare the accuracy of the model learned on A when it is applied to B, to the accuracy of the model learned and applied on B. In practice, an important point to which practitioners must pay close attention is that the sequences used for testing in one condition were not used for training in another one, to avoid any bias in the accuracy estimates during swaps. This technique was extensively used in the TFcoop analyses 10 . Given two ChIPseq experiments targeting the same TF in two cell types, a TFcoop model predicting the binding was learned for each cell type. Then the two models were swapped. In some cases, both models got the same accuracy, but for some TFs the model learned on cell type A was not as good on cell type B as the model directly learned on cell type B, meaning that the co-factors of the TF were likely not the same in the two cell types. Similarly, for a given ChIP-seq experiment, a model was learned for enhancer sequences and another model for promoter sequences. The swap experiment revealed that the two models are not interchangeable, and hence that the co-factors of a TF may vary depending on the nature of the regulatory sequence (promoters vs.

  TFscope for analyzing the binding differences of one TF in two conditions (two cell types or two treatments), or of two paralogous TFs with similar PWMs[START_REF] Romero Raphaël | Systematic analysis of the genomic features involved in the binding preferences of transcription factors[END_REF] . For this, the TFscope model integrates three kinds of variables that model i) the core motif, ii) the nature and position of binding sites of co-factors, and iii) the nucleotidic environment of specific regions around the core motif. The importance of each feature in the different comparisons was assessed with the importance measure described above. For comparisons involving one TF in two conditions, the co-factors, and to a lesser extent the nucleotidic environment, were often (but not always) the most important features explaining the differences of binding sites. For paralogous TFs (two TFs in the same cell type) the picture is different, and subtle differences in the core motif often explained most bindingsite differences.CNNs Approaches based on CNNs take raw sequences in input, so the DNA features are not directly provided to the model. However, as we have seen, the filters of the first convolutional layer correspond to PWMs modeling DNA motifs. Hence, we could think that the DNA features used by the model are actually encoded in the first convolutional layer

From

  this perspective, deep learning approaches based on CNNs often show substantial improvement over simpler methods. This is especially true for the prediction of gene expression signal, where many efforts have been made to allow CNNs to handle very long sequences to capture the effect of potential distant enhancers. Besides model accuracy, we have seen that different level of knowledge can be gained by these studies. The prioritization of nucleotide variants, which is of great interest in medical and therapeutic studies, can be deduced from model predictions without the need for sophisticated procedures of model interpretation. Similarly, general hypotheses about the specificity or the extent of a regulatory mechanism can often be tested by training and swapping different models, or by changing the training or the test sets.

negative Figure 1. A toy decision-tree inspired from the Epigram method 11 . The

	edges that follow the node correspond to the two possible	seq. 1	y 1	pwm#1 15	pwm#2 24	pwm#3 17	pwm#4 24	. . . . . .	pwm#50 26
		seq. 2	0	9	21	20	19	. . .	12
	outcomes of the test. Thus, each path from the root to a leaf	seq. 3 . . .	0 . . .	17 . . .	19 . . .	16 . . .	18 . . .	. . . . . .	24
	corresponds to a series of tests that lead to the prediction of the class associated with that leaf (which is the majority class	seq. 1000	1	18	18 score PWM #5 > 80 17 500 / 500	20	. . .	15
	of the training examples that belong to this leaf). Learning a decision tree involves deciding the tests associated with					190 / 415 score PWM #12 > 70
	each node (typically which feature and which threshold on
	this feature?), and several algorithms have been proposed to				180 / 200 score PWM #21 > 85
	build trees with minimum prediction error. Decision trees are
	accurate on certain problems and have the great advantage			310 / 85 positive	150 / 20 positive	30 / 180 negative	10 / 215
	of being simple to understand and interpret. However, they
	are known to be quite unstable (a slight change in the
	training data can lead to very different trees) and they can be relatively inaccurate on some problems 29 . Random forests have been proposed to overcome these issues, at the price however of an indubitable loss of readability. A RF is a combination of many (sometimes hundreds or even thousands) decision trees. All these trees are independently	learning set involves 1000 sequences (500 positive + 500 negative, see top table). Each sequence is described by the score of 50 PWMs. The decision tree learned from these sequences encodes a set of rules that classify a sequence as positive if the score of PWM#5 is > 80, or if the score of PWM#12 and #21 are > 70 and 85, respectively. Numbers at the top of each node provide the repartition of training sequences in the two classes.
	learned on the same problem, with a dedicated algorithm that
	integrates a stochastic procedure to ensure that all trees are
	different. Once learned, the RF can be used to predict the
	class of a new sample with a very simple procedure: each
	tree is used to predict the class, and the most popular class
	is proposed by the forest. This voting scheme removes the
	instability problem, and, if the learned trees are sufficiently
	independent, the accuracy of the forest can be much higher
	than that of single trees 29 . The independence is ensured
	by two stochastic components of the learning algorithm: 1)
	each tree is learned on a different bootstrap sample of the
	original data, which means that the learning set is obtained
	by randomly sampling with replacement the original learning
	set (in this way, some samples may appear several times
	in the bootstrap sample, while others are absent); 2) at
	each node, only a randomly chosen subset of features are
	considered to determine the exact test associated with the
	node.
	The Epigram approach 11 uses RFs to predict the
	presence of six histone marks in different human cell types
	from DNA sequence. A RF is learned for each mark based
	on the peaks identified by dedicated ChIP-seq experiments.
	Sequences associated with the peaks were used as positive
	sequences (sequence length is around 1000bp). Negative
	sequences were genomic regions not covered by a peak.
	Before learning a RF, a de novo motif finding algorithm was
	used to identify motifs that are more present in one class
	than in the other. Then, each sequence was described by the								). Each
	score obtained by the different motifs, and the RF learning	node corresponds to a binary test on a specific feature of the
	algorithm was run on these data. As for SVM, the accuracy	samples (for example, "is feature #3 > 0.66?"), and the two

Prepared using sagej.cls

Acknowledgements

I thank Charles Lecellier, Sophie Lèbre and Olivier Gascuel for their careful reading of this manuscript. I am also grateful to Charles Lecellier and Sophie Lèbre for our long-standing collaboration in the field. Some of the views developed here undoubtedly stem from our daily discussions.

Funding

This work was supported by the SIRIC and the Labex NUMEV (projet MOTION), the National Research Agency (ANR project LOWCO) and by the LabMUSE EpiGenMed (R-loops project).


Nisrine Hammi

Nelly Couzon

Alla Abramova

Christophe Volkringer

Thierry Loiseau

Sébastien Royer

Jérémy Dhainaut

<<one Blank>>

MOF/CHITOSAN COMPOSITES FOR THE CAPTURE OF GASEOUS IODINE <<One blank>>

jeremy.dhainaut@univ-lille.fr, https://uccs.univ-lille.fr/index.php/fr/ <<One blank>> <<One blank>> <<One blank>> Keywords: metal-organic frameworks; chitosan; beads; films; composites; I2 capture <<One blank>> <<One blank>> Summary: Metal-organic frameworks (MOF) is an emerging class of crystalline and porous materials. Their structure results from the combination of metal clusters (or ions) with multitopic organic linkers. By changing the metal or the linker, or by adding organic functionalities to the linker, the physicochemical properties of the MOF can be tailored for specific applications. For instance, iodine-131 is a major fission by-product which can increase the incidence of thyroid cancers. Following a nuclear meltdown, venting is conducted to avoid overpressurization. The vented steam, containing radionuclides such as 131 I2, passes through a filtered containment venting system to capture the contaminants. Typically, a fixed bed of silver-doped ZSM-5 zeolite is used. However, several limitations remain due to the small pore aperture of the zeolite (0.55 nm), hardly accommodating bulky iodine derivatives, and the competitive adsorption of contaminants (mainly CO). Hence adsorbents more specific towards I2 and its derivatives, presenting larger pores and/or higher iodine capture capacity, remain desired.

In particular, the UiO-66 MOF presents a good stability against water, a high adsorption capacity, and larger pore sizes (0.8 to 1.1 nm). By adding amino moieties on the terephthalate linker, one can obtain UiO-66_NH2, an adsorbent with high binding energy towards electro-acceptor species such as I2. Recently, our group applied severe nuclear accidental conditions to this MOF, previously shaped as binderless granules, showing high retention of 131 I2 and preserved physicochemical properties. [1] In a subsequent step, we studied the preparation of UiO-66_NH2-based extrudates and granules with improved mechanical properties by adding biopolymers. With lesser than 5 wt.% of chitosan, the textural properties of the resulting composites are barely affected -in line with the chitosan loading, and as-prepared materials present both high adsorption capacity towards iodine (see Figure 1.A-B) and a significantly improved mechanical resistance. [2] Still, shifting from a fixed bed of powder to a fixed bed of granules has consequences over the adsorption kinetics, with gaseous I2 diffusing in-between the granules. Hence, in a next step, we prepared composite films with up to 60 wt.% of UiO-66_NH2 MOF (Figure 1.C). By doing so, the adsorption equilibrium was reached within 24 h as per the MOF powder. Moreover, the total quantity of I2 adsorbed correspond to the loading of the MOF, meaning that up to 40 wt.% of chitosan, the porosity of the MOF remains fully accessible. The main results and perspectives of these works will be discussed.

Fig. 1 UiO-66_NH2@chitosan granules (A) and their relative iodine adsorption kinetic curve (B, red) compared to their powder counterpart (B, black). UiO-66_NH2@chitosan films with various MOF loadings (B).


Joseph Boulos
email: joseph.boulos@univ-lille.fr@uccs.univ-lille.fr

Firat Goc

Noémie Perret

Franck Rataboul

Eric Marceau

Jeremy Dhainaut

Sebastien Royer

Éric Marceau

Jérémy Dhainaut

Sébastien Royer

New insights on the catalytic reductive amination of hydroxyacetone amination over RUWXC/AC Catalyst

Keywords: Reductive amination, carbides, biomass conversion, bimetallic catalysts

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Summary:

The production of chemicals and liquid fuels from renewable and non-edible lignocellulosic biomass has been considered as a promising way to reduce our dependance to fossil resources as well as to reduce CO2 release. Especially, nitrogen-containing molecules, particularly primary amines, are broadly used for the synthesis of pharmaceuticals, polymers, surfactants, agrochemicals, and dyes [1] . Owing to the high O/C ratio (~1/1) in biomass feedstocks [2] , the production of oxygenates from biomass is rather straightforward and has been largely studied. However, the further production of valuable nitrogen-containing products is far less evident due to the deficit of efficient amination strategies of oxygenates. One way is the amination of aldehydes and ketones to primary amines, employing ammonia as the nitrogen source [3] . Due to the development of biorefining, renewable aldehydes and ketones including glycolaldehyde, glyceraldehyde, hydroxyacetone, and aromatic compounds are nowadays available at large scales, opening new opportunities to produce nitrogencontaining compounds [4] . For example, Liang et al. reported the use of partly reduced Ru/ZrO2 for the reductive amination of different biomass-based aldehydes/ketones in aqueous ammonia [5] . Despite this encouraging development, effective heterogeneous catalytic systems that allow the amination reaction to take place under milder conditions (T < 100 °C, P < 50 bar, aqueous phase, and without additives) with high amines' yields are still lacking. In particular, the production of large-market amino alcohols from hydroxyacetone hasn't been reported so far. Herein, we report the preparation of a highly efficient and robust catalyst, RuWxC/AC, for the reductive amination of hydroxyacetone (Figure 1). By varying several process parameters including time, temperature, the nature of nitrogen source, and pressure, up to 60 mol.% amines' yield has been obtained. The promoting effect of tungsten carbide nanoparticles has been particularly investigated. Finally, a kinetic study has been conducted and will be discussed.

  References: 1. Yan, N.; Chen, X. Nature 2015, 524 (7564), 155-157. 2. Esposito, D.; Antonietti, M. Chem. Soc. Rev. 2015, 44 (16), 5821-5835 3. Gross, T.; Seayad, A. M.; Ahmad, M.; Beller, M. Org. Lett. 2002, 4 (12), 2055-2058. 4. Vitasari, C. R.; Meindersma, G. W.; Haan, A. B. de. Green Chem. 2012, 14 (2), 321-325. 5. Liang, G.; Wang, A.; Li, L.; Xu, G.; Yan, N.; Zhang, T. Angew. Chem. Int. Ed. 2017, 56 (11), 3050-3054.

Fig. 1

 1 Fig.1Reductive amination of 10 g hydroxyacetone with 100 mL NH3 (28 wt.%) and 1 g catalyst for 1 h.