Jean-Louis Giavitto

All Watched Over by Machines of Loving Grace & A brief and subjective chronology of AI technics in music composition

à Atlanta dans le cadre de l'événement France -Atlanta.

All Watched Over by Machines of Loving Grace Introduction

All Watched Over by Machines of Loving Grace is a 1967 poem by Richard Brautigan that describes the peaceful and harmonious cohabitation of humans and computers.

You can read the poem or listen Richard Brautigan reading it.

At that time, this vision may have seemed very far away from a reality where there were only a few hundred computers around the world, each occupying an entire room but no more powerful than today's pocket calculator. Fifty years later, they are more than two billion computers, five billion smart phones and twenty billion IoT devices in the world. With the technological revolutions brought about by the internet, big data, the cloud and deep learning, Brautigan's vision resonates singularly and compel us to rethink our interactions with machines.

It is therefore not a coincidence that Adam Curtis used the title of Brautigan's poem for a three episodes documentary about how humans have been colonized by the machines they have built -"Although we don't realize it, the way we see everything in the world today is through the eyes of the computers." Curtis argues that computers have failed to liberate humanity, and instead have "distorted and simplified our view of the world around us".

You can see the first episode, the second and the last one.

The attack is severe but deserved. The potential wide-ranging impact urges to look carefully at the ways in which these technologies are being applied now, whom they're benefiting, and how they're structuring our social, economic, and interpersonal lives. The social implications of data systems, machine learning and artificial intelligence are now under scrutiny, with for instance the emergence of a dedicated research institute (AI Now). The massive amount of data needed to couple the human's world and the machine's world, and their automatic handling, poses an unprecedented threat to individual freedom, justice and democracy.

But, beyond the misuse of these technologies that could perhaps be regulated by law, our face-toface encounters with machines bring about major anthropological changes when they address not only the physical world, but also our moods, emotions and feelings. The question of how our personalities and preferences are being shaped by our digital surroundings seems more pressing than ever. How are our tools shaping us at the heart of the most intimate of human beings: emotion, art and creation? Big Tech may turn to ethology to help advance artificial intelligence, self-driving cars, and more. We turn to artists and computer scientists who work with artists, to fuel our reflections and question them in two webinars on October 14 and 15, on how digital tools, especially artificial intelligence, are shifting aesthetic issues and transforming the artistic workflow, challenging the notion of authorship, disrupting education and opening up new creative dimensions.

We give here a brief, subjective and partial chronology of the interactions between music and artificial intelligence for the sake of our discussion during the two webinars. The links between music and computer science have indeed existed since the birth of the latter. It is perhaps its abstract character -the nature of imitation that connects music to the world is still discussed by philosophers-that has attracted computer scientists. In any case, people tried to get a computer to write music long before they tried to make it paint.

The few examples given will show that we are far from being able to replace composers and musicians. But above all, why do it?

What musical needs are met by the use of AI tools? How does one make music progress by using these tools? Our hypothesis is that music is an extraordinary field of experience that allows us to imagine new uses and new interactions with machines, going well beyond a tool towards a creative companionship; and that these machines allow us to better understand and to elaborate or test new answers to artistic questions: Recently, Jason co-designed EarSketch, a free online learning platform that leverages the appeal of music to teach students how to code. Used by over 500,000 show how students combine music and coding to create expressive computational artifacts, and exemplify how machine learning will create even deeper connections between music and.

• Performances with Machines

o Jérôme Nika (Ircam) Jérôme is researcher in human-machine musical interaction in the Music Representations Team / STMS lab at IRCAM. Through the development of generative software instruments, Jérôme Nika's research focuses on the integration of scenarios in music generation processes, and on the dialectic between reactivity and planning in interactive human-computer/music improvisation. His work takes place in the broad family of Omax approaches to man-machine musical interactions.

o Benjamin Levy (Ircam) Benjamin is a computer music designer at IRCAM. He collaborated on both scientific and musical project implying AI, in particular around the OMax improvisation software. The artistic project A.I. Swing marks several years of artistic and experimental experiences with musician and jazz improviser Raphaël Imbert.

o Grace Leslie (Georgia Tech) Grace is a flutist, electronic musician, and scientist at Georgia Tech. She develops brain-music interfaces and other physiological sensor systems that reveal aspects of her internal cognitive and affective state to an audience.

o Daniele Ghisi (composer) Daniele studied and composition. He is the creator, together with Andrea Agostini, of the project bach: automated composer's helper, a real-time library of computer-aided composition. AI technics were instrumental in his work for La Fabrique des Monstres. His work explores many facets of the relationship between digital tools and music. In the installation An Experiment With Time, which can be viewed online from October 12 to October 18, is journey through three different time cycles, their dreams and the construction of a time-dilating machine.

o Elaine Chew (CNRS) Elaine is a senior researcher and pianist at the STMS Lab and PI of the ERC projects COSMOS and HEART.FM. She designs mathematical representations and analytical computational processes to decode musicians' knowledge and explain artistic choices in expressive musical performance. She integrates her research into concert-conversations that showcase scientific visualisations and labgrown compositions. She has collaborated with Dorien Herremans to create the MorpheuS system, and used algorithmic techniques to make pieces based on arrhythmia electrocardiograms.

Special online Event

From online from October 12 to October 18, the audience can access

An Experiment With Time, an audio and video installation by Daniele Ghisi inspired by a book bearing the same name published by John W. Dunne, an aeronautical engineer and philosopher. John Dune believed that he experienced precognitive dreams and proposed that our experience of time as linear is an illusion brought about by human consciousness.

A central theme addressed in the installation of Daniele Ghisi is the construction and sharing of time and dreams between humans, and its transformation in the face of technology. If Richard Brautigan's vision comes true, how will we share our time with machines? How can we reconcile the elastic time of our human activities, from the dolce farniente to the ubris of our the Anthropocene era, and the regulated, chronometric, Procrustean time of the tireless machine?

A brief and subjective chronology of AI technics in music composition

Electronic music i.e., music that employs electronic musical instruments, has been produced since the end of the 19th century. But producing a sound by a computer needed the existence of computers and the earliest known recording of computer music was recorded at Alan Turing's Computing Machine Laboratory in Manchester in 1951:

• https://soundcloud.com/the-british-library/first-recording-of-computer-music-1951copeland-long-restoration

• The story of this recording is told here.

In • https://youtu.be/3SQYWsfL_Fo

Carl Philipp Emanuel Bach used the same approach in 1758 to propose Einfall, einen doppelten Contrapunct in der Octave von sechs Tacten zu machen, ohne die Regeln davon zu wissen (German for "A method for making six bars of double counterpoint at the octave without knowing the rules"). A perhaps better-known example is that of Mozart:

• W. A. Mozart's Musikalisches Würfelspiel K.516f Trio 2 proposed here by Derek Houl At the time, people chose at random using a dice. In 1957, a computer was used: Lejaren Hiller, in collaboration with Leonard Issacson, programmed one of the first computers, the ILLIAC at the University of Illinois at Urbana-Champaign, to produce what is considered the first score entirely generated by a computer. Named Illiac suite, it later became the String Quartet number 4.

The piece is a pioneering work for string quartet, corresponding to four experiments. The two composers, professor at the University, explicitly underline the research character of this suite, which they regard as a laboratory guide. The rules of composition and order that define the music of different epochs are transformed in automated algorithmic processes:

• the first is about the generation of cantus firmi • the second generates four-voice segments with various rules • the third deals with rhythm, dynamics and playing instructions • the fourth explores various stochastic processes Whether in musical dice games or in the Illiac suite, a dialectic emerges between a set of rules driving the structure and form of a piece, and the randomness used to ensure a certain diversity and the exploration of an immense combinatorial game. This dialectic is at work in almost every automated composition system.

At the same time, in France, Iannis Xenakis was also exploring several stochastic processes to generate musical material. He will also mobilize other mathematical notions to design new generative musical processes. In his first book, Musiques formelles (1963; translated in English with three added chapters as Formalized Music -Thought and mathematics in composition, 1972), he previews for instance the application to his work of probability theory (in the pieces Pithoprakta and Achorripsis, 1956-1957), ensemble theory (Herma, 1960(Herma, -1961) ) and game theory (Duel, 1959;Stratégie, 1962).

We jump in time to the eighties. Expert systems are flourishing. This set of technics takes a logical approach to knowledge representation and inference. The idea is to apply a set of predefined rules to facts to produce a reasoning or answer a question. These systems have been used to generate scores by explaining rules that describe a musical form or the style of a composer. The rules of fugue, or Schenkerian analysis, for example, are used to harmonize in the style of Bach.

A notable example of the rule approach is given by the work of Kemal Ebcioğlu at the end of the eighties. In his PhD thesis work (An Expert System for Harmonization of Chorales in the Style of J.S. Bach) he develops the CHORAL system based on 3 principles:

• the encoding of a large amount of knowledge about the desired musical style,

• the use of constraints both to automatically generate solutions (with backtrack) and to eliminate those that would be unacceptable (so there are rules to evaluate the quality of the result), • the use of style-specific heuristics to prioritize the choices of the algorithm when extending a partially created composition.

Backtracking is a technique used in particular for constraint satisfaction problems, which allows a series of choices to be questioned when these choices lead to an impasse. For example, f we build a musical sequence incrementally, it may happen at some point that we can no longer increment this sequence without violating the constraints we have set ourselves. The idea is then to go back to a previous point of choice and make another choice to develop an alternative. If there are no further possible choices, one has to go back to the previous choice point, and so on until one can develop a complete solution.

Heuristics are practical methods, often relying on incomplete or approximate knowledge, which do not guarantee correct reasoning, but which often produce satisfactory results (and quickly). When the search for an optimal solution is not feasible nor very practical, heuristic methods can be used to speed up the process of finding a suitable solution.

• Here is an example of chorale harmonization (first the orignal Bach's harmonization then teh result produced by CHORAl at 4'42). The concert note skeches the expert system.

Another outstanding example from the same decade is the EMI system "Experiment in Music Intelligence" developed by David Cope at the University of Santa Cruz. David Cope began to develop this system while he was stuck on writing an opera:

"I decided I would just go ahead and work with some of the AI I knew and program something that would produce music in my style. I would say 'ah, I wouldn't do that!' and then go off and do what I would do. So it was kind of a provocateur, something to provoke me into composing." https://computerhistory.org/blog/algorithmic-music-david-cope-and-emi/

The system analyzes the pieces submitted to it as input characterizing a "style". This analysis is then used to generate new pieces in the same style. The analysis of EMI applied to his own pieces, makes the composer aware of his own idiosyncrasies, of his borrowings and finally leads him to make his writing evolve: "I looked for signatures of Cope style. I was hearing suddenly Ligeti and not David Cope." the composer noted, "As Stravinski said, 'good composers borrow, great composers steal'. This was borrowing, this was not stealing and I wanted to be a real, professional thief. So I had to hide some of that stuff, so I changed my style based on what I was observing through the output [of] Emmy, and that was just great." https://computerhistory.org/blog/algorithmic-music-david-cope-and-emi/ You can hear many pieces produced by this system :

• A Mazurka in the style of Chopin produced by EMI,

• An intermezzo in the manner of Mahler Right from the start, David Cope wanted to distribute this music in the classic commercial circuit. They are often co-signed with Emmy, the little name that designates his system. Over the years, the system has evolved with sequels called Alena and Emily Howell who are also recorded artists. Machine learning relies on numerical representations of the information to be processed. An example of a technique that falls within this domain are artificial neural networks. This technique was already used in the 1960s with the perceptron invented in 1957 by Frank Rosenblatt which allows supervised learning of classifiers. For Instance, a perceptron can be trained to recognize the letters of the alphabet in handwriting. The input of the system is a pixel array containing the letter to be recognized, and the output is the recognized letter. During the learning phase many examples of each letter are presented and the system is adjusted to produce the correct output categorization.

Once the training has been completed, a pixel array can be presented containing a letter that is not part of the examples used for training and the system correctly recognizes the letter.

Depending on the time, the dominant paradigm in AI has fluctuated. In the sixties, machine learning was fancy. But at the end of the decade, a famous article put the brakes on this field, showing that perceptrons could not classify anything. This was because its architecture was reduced to a single layer of neurons. It is shown in the following that more complex classes of examples can be recognized by increasing the number of neuron layers. Unfortunately, there was no learning algorithm available at that time to train multi-layered networks.

Such an algorithm appeared in the 1980s but it is still very heavy to implement and it is also realized that to train a multi-layer network, you need a lot, a lot of data.

At the beginning of the 2000s, the algorithms are still making progress, the machines are much faster and we can access numerous databases of examples as a result of the development of all digital techniques. This favorable conjunction relaunched numerical machine learning techniques and we now encounter the term "deep learning" at every turn (here "deep" refers to the many layers of the network to be trained).

The contribution of these digital learning techniques is considerable. It allows for example to generate sound directly and not a score (the sound signal being much richer in information, it takes many layers to do this and hours of recorded music to train the network). We have examples of instrument sounds reconstructed by these techniques. Of course, one can also compose, and there are many examples of Bach's choir. Here is an example of an organ piece produced by a neural network (folk-rnn) and then harmonized by another (DeepBach). And another example of what can be achieved (with folk-rnn) by training a network on 23,962 Scottish folk songs (from midi type transcriptions).

One challenge faced by machine learning is that of the learning data. For reasons that are rarely discussed, and despite all academic and non-academic researches, the project of interpreting music is a profoundly complex and relational endeavor. Music is a remarkably slippery things, laden with multiple potential meanings, irresolvable questions, and contradictions. Entire subfields of philosophy, art history, and media theory are dedicated to teasing out all the nuances of the unstable relationship between music, emotion and meanings. The same question haunts the domain of images.

The economic stakes are not far away. A company like AIVA thus organized a concert (at the Louvre Abu Dhabi) featuring five short pieces composed by their system and played by a symphony orchestra. Other examples include • a piece composed especially for the Luxembourg national holiday in 2017.

• An example in some chinese style But beware, in fact only the melody is computer generated. The orchestration work, arrangements, etc., are then done by humans: https://www.aiva.ai/engine. This is also true for a lot of systems that are claiming automatic machine composition.

Making music automatically with a computer is probably of little interest to a composer (and to the listener). But the techniques can be used to solve compositional problems or to develop new kind of performances. An example in composition is to produce an interpolation between two rhythms A and B (given at the beginning of the recording)

Another compositional example is to help orchestration problems. The Orchid* software family, initiated in Gérard Assayag's RepMus team at IRCAM, proposes an orchestral score that comes as close as possible to a given target sound as input. The latest iteration of the system, Orchidea, developed by Carmine Cella, composer and researcher at the Univ. of Berkeley, gives not only interesting but also useful results. Some (short) examples are available on the page:

• An original archeos bell and its orchestral imitation • A girl's and an orchestra's screaming • Falling drops and the orchestral results

• A roaster and it musical counterpart Far from a replacement approach, where AI substitutes for man, these new techniques suggest the possibility of a musical companionship.

This is the objective of the OMax family of systems, developed at IRCAM, still in Gérard Assayag's team. These systems propose a machine that co-improvise in real-time with musicians on stage:

• An example conceived and developed by Georges Bloch with Hervé Sellin at the piano, to which Piaf and Schwartzkopf respond on the theme of The Man I Love. • Here, the saxophonist Remy Fox and Jérôme Nika, author of one of the extensions of the system which allows to impose high level predefined scenarios to the response of the machine and thus to better manage the evolutions in the duration: o from 9', the system's response corresponds to excerpts from speeches whose prosody aligns with what Remy Fox plays o from 10'40, the system response evolves to respond with sung voices.

The type of scenario used to co-improvise in the last example, was also used for Lullaby Experience, a project developed by Pascal Dusapin using nursery rhymes collected from the public via the Internet. There is no improvisation here. The system is used to produce material which is then taken up with the composer and integrated with the orchestra.

A last example where AI assists the composer rather than substituting for her or him, is given by La Fabrique des Monstres by Daniel Ghisi. The musical material of the piece is the output of a network of neurons at various stages of its learning on various corpuses. At the beginning of the learning process, the music generated is rudimentary, but as the training progresses, one recognizes more and more typical structures. A remarkable passage is StairwayToOpera which gives a "summary" of great moments typical of operatic arias. These examples show that while it is possible to make music that is not very interesting with these techniques, they can also offer new forms of interaction, open new creative dimensions and ask intriguing and still unresolved questions:

How could emotional music be coming out of a program that had never heard a note, never lived a moment of life, never had any emotions whatsoever? (Douglas Hoffstader)

Jean-Louis Giavitto

CNRS -STMS lab, IRCAM, Sorbonne Université, Ministère de la Culture giavitto@ircam.fr

Une brève chronologie subjective de l'usage de l'intelligence artificielle en composition musicale1

Les prémices

On produit de la musique électronique, c'est-à-dire de la musique qui a recours à des instruments de musique électroniques, depuis la fin du XIXe siècle. Mais la production d'un son par un ordinateur exige l'existence desdits ordinateurs, et le plus ancien enregistrement d'une musique générée par un ordinateur est celle produite par la machine développée au laboratoire d'Alan Turing2 à Manchester en 1951. On peut écouter un enregistrement de cette musique, restauré par Copeland et Long3 , ainsi que le récit de cette nuit passée à programmer pour donner naissance à cette création, suivi de la réaction de Turing à l'écoute du résultat le matin suivant :4 À la fin des années 1940, Alan Turing s'est aperçu qu'il pouvait produire des notes de hauteur variée en modulant le signal contrôlant le haut-parleur relié à son ordinateur, lequel haut-parleur servait alors à signaler la fin d'une série de calculs. Bientôt, Christopher Strachey (https://en.wikipedia.org/wiki/Christopher_Strachey) tire profit de cette petite astuce pour produire ses premières mélodies : l'hymne national (God Saves the King), une petite comptine et « In the Mood » de Glenn Miller.

À la fin de l'été 1952, Christopher Strachey développe un « jeu de dames complet jouant à une vitesse raisonnable »5 . Il est aussi responsable des étranges lettres d'amour qui apparaissent sur le tableau d'affichage du département informatique de l'université de Manchester à partir d'août 19536 7 .

La méthode de Strachey pour générer ses lettres d'amour consiste à extrapoler un échantillon en substituant aléatoirement des mots présélectionnés à certains endroits de la phrase. Ces endroits correspondent à des catégories déterminées, et chaque catégorie ouvre à une collection de mots prédéfinis. L'algorithme utilisé par Strachey est le suivant :

Generate Salutation 1 and Salutation 2, Do this 5 times:

Randomly generate one of the following two templates:

1. "You are my" Adjective Noun 2. "My" Adjective(optional) Noun Adverb(optional) Verb, Your Adjective(optional) Noun Generate "Your" Adverb, "MUC" Les instructions algorithmiques sont en italique, les positions (espaces réservés aux collections de mots) sont soulignées et les séquences invariantes en sortie sont en gras (l'acronyme MUC correspond à : « Manchester's University Computer »). C'est en réalité le même processus qui a été utilisé au XVIIIe siècle par les Musikalisches Würfelspiel 8 (« Jeu de dés musical ») pour générer aléatoirement de la musique à partir d'un réservoir d'options pré-composées. L'un des exemples les plus anciens qui nous soient parvenus sont les Der allezeit fertige Menuetten-und Polonaisencomponist (« Menuets et polonaises prêts à composer ») proposée en 1757 par Johann Philipp Kirnberger. En voici un exemple, interprété par le Quatuor Kaiser 9 .

Carl Philipp Emanuel Bach a suivi la même approche en 1758 pour proposer Einfall, einen doppelten Contrapunct in der Octave von sechs Tacten zu machen, ohne die Regeln davon zu wissen (« Principe pour composer un double contrepoint de six mesures à l'octave sans en connaître les règles »). Un autre exemple, peut-être plus fameux, est celui des Musikalisches Würfelspiel K.516f de Wolfgang Amadeus Mozart. En voici le Trio no. 2, proposé par Derek Houl 10 . À l'époque, on choisit grâce à un jet de dé. En 1957, un ordinateur est utilisé : Lejaren Hiller 11 , en collaboration avec Leonard Isaacson 12 , programme un des premiers ordinateurs, l'ILLIAC à l'université de l'Illinois à Urbana-Champaign 13 , pour produire ce qui est considéré comme la première partition entièrement générée par ordinateur. Intitulée Illiac Suite, c'est devenu plus tard le Quatuor à cordes numéro 4. La pièce est une oeuvre pionnière pour quatuor à cordes, résultat de quatre expériences 14 . Les deux compositeurs, professeurs à l'université, soulignent explicitement l'aspect « recherche » de cette suite, qu'ils considèrent comme une expérience de laboratoire. Les règles et ordres de composition qui définissent les caractéristiques de la musique d'une période donnée, sont transformés en processus algorithmiques automatiques : la première expérience traite de la génération de cantus firmi, la deuxième génère des segments à quatre voix régis par diverses règles, la troisième concerne le rythme, les dynamiques et les indications de jeu, la quatrième explore divers processus stochastiques. 15 Que ce soit au cours d'un jeu de dé musical ou pour la Illiac Suite, une dialectique se fait jour entre une série de règles régissant la structure et la forme d'une pièce, et l'aléatoire utilisé pour s'assurer d'une certaine variété et de l'exploration de l'immense champ des possibles de la combinatoire. Cette dialectique est à l'oeuvre dans quasiment tous les systèmes de composition automatisée. À la même époque, en France, Iannis Xenakis explorait lui aussi de nombreux processus stochastiques pour générer du matériau musical. Il mobilisera d'autres notions mathématiques pour mettre au point de nouveaux processus musicaux génératifs. Dans son livre Musiques formelles 16 8 https://en.wikipedia.org/wiki/Musikalisches_Würfelspiel 9 https://youtu.be/3SQYWsfL_Fo 10 https://youtu.be/yGLSSiYWSUA 11 https://en.wikipedia.org/wiki/Lejaren_Hiller 12 https://en.wikipedia.org/wiki/Leonard_Isaacson 13 https://music.illinois.edu/ems-history-illiac-i 14 https://www.musicainformatica.org/topics/illiac-suite.php 15 Lejaren Hiller -Illiac Suite pour quatuor à cordes :

• [1/4] https://youtu.be/n0njBFLQSk8 (1963), il donne par exemple à voir l'application, à son travail de composition, de la théorie probabiliste (dans ses pièces Pithoprakta et Achorripsis, 1956Achorripsis, -1957)), de la théorie des ensembles (Herma, 1960(Herma, -1961) ) et de la théorie des jeux (Duel, 1959et Stratégie, 1962).

Systèmes experts et représentation symbolique des connaissances

Faisons à présent un saut dans le temps jusque dans les années 1980. On assiste alors à l'explosion des systèmes experts 17 . Cet ensemble de techniques suit une approche logique de la représentation et de l'inférence des connaissances. L'idée est d'appliquer aux faits un ensemble de règles prédéfinies afin de produire un raisonnement ou de répondre à une question. Ces systèmes ont été utilisés pour générer des partitions sonores en explicitant des règles qui décrivent une forme musicale ou le style d'un compositeur. Les règles de la fugue 18 , ou l'analyse schenkérienne 19 , par exemple, sont mises à profit pour réaliser des harmonies dans le style de Bach.

Un exemple remarquable de cette approche par règles nous est offert par le travail de Kemal Ebcioglu 20 à la fin des années 1980. Dans sa thèse de doctorat (intitulée « Un système expert pour l'harmonisation des chorales dans le style de J. S. Bach »), il développe le système CHORAL 21 qui s'appuie sur trois principes :

• l'encodage d'une grande quantité de connaissances sur le style musical considéré • l'utilisation de contraintes, à la fois pour générer automatiquement des solutions (avec algorithmes de retour-arrière) et pour éliminer celles qui seraient inacceptables (ce qui suppose l'instauration de règles pour évaluer la qualité du résultat) • l'utilisation d'heuristiques spécifiques au style considéré, afin de hiérarchiser les choix de l'algorithme dans le cas de l'extension d'une composition partiellement existante.

Le retour-arrière est une technique utilisée en particulier pour résoudre des problèmes de satisfaction de contrainte (CSP), qui permet de remettre en question une suite de choix lorsque celle-ci mène à une impasse. Par exemple, si on élabore une séquence musicale de manière incrémentale, il peut arriver qu'on se trouve à un moment dans l'impossibilité d'incrémenter une nouvelle fois la séquence sans violer les contraintes qu'on a soi-même fixées. L'idée est alors de revenir sur ses pas jusqu'à la décision précédente, afin d'en prendre une autre et de développer une solution alternative. S'il n'y a pas d'autres choix possible, on revient alors à un choix antérieur, et ainsi de suite jusqu'à développer une solution complète. o Wavenet : https://magenta.tensorflow.org/assets/nsynth_04_05_17/sounds/Flugelhorn-WaveNet.mp3 35 https://theconversation.com/machine-folk-music-composed-by-ai-shows-technologys-creative-side-74708 36 https://www.youtube.com/watch?v=BUIrbZS5eXc&feature=youtu.be 37 https://youtu.be/Wm5yQR336cI 38 http://www.abudhabi2.com/innovation-week-at-louvre-arts-event-in-abu-dhabi/ 39 https://youtu.be/H6Z2n7BhMPY 40 https://youtu.be/Bd9p2oi760g 41 https://consumer.huawei.com/uk/campaign/unfinishedsymphony De la composition automatique au compagnonnage musical Générer de la musique automatiquement avec un ordinateur est probablement assez peu intéressant pour un compositeur (ainsi que pour l'auditeur). Mais les techniques mentionnées peuvent être utilisées pour résoudre des problèmes compositionnels ou pour développer des performances d'un nouveau genre. Un exemple, dans le domaine de la composition, serait de produire une interpolation entre deux rythmes A et B (donnés au début de l'enregistrement) 42 .

Un autre exemple, dans le cadre compositionnel, est l'aide à la résolution de problèmes d'orchestration. La famille de logiciels Orchid, initiée par l'équipe RepMus de Gérard Assayag à l'Ircam, propose une partition d'orchestre produisant un résultat sonore le plus proche possible d'un « son » donné comme cible, en entrée. La dernière itération du système, développée par Carmine Emanuele Cella, compositeur et chercheur à l'université de Berkeley, donne des résultats non seulement intéressants, mais utiles. Quelques exemples (brefs) sont disponibles sur la page du compositeur 43 . Loin d'une approche remplaciste, où l'IA se substitue à l'humain, ces nouvelles techniques suggèrent la possibilité d'un compagnonnage musical.

C'est l'objectif de la famille de systèmes OMax 44 , développé à l'Ircam, toujours par l'équipe de Gérard Assayag. OMax et ses équivalents ont été mis en oeuvre partout dans le monde, avec de grands artistes, et nombre de vidéos de performances et de concerts publics 45 témoignent des capacités du système. Ces systèmes implémentent des agents qui produisent de la musique en navigant avec créativité dans une mémoire musicale apprise en amont ou durant la performance. Ils offrent une vaste palette de styles de « composition » ou de « jeu ».

• Un exemple conçu et développé par Georges Bloch avec Hervé Sellin au piano, auquel Piaf et Schwatzkopf répondent sur le thème de The Man I Love. La deuxième partie de la vidéo met en oeuvre un agent réactif qui écoute le saxophoniste Rémi Fox, et qui joue en réponse des phrases musicales enregistrées en direct peu avant ou durant la même performance 46 .

Dans cet autre exemple, un saxophone virtuel dialogue avec un saxophone humain en temps réel, en suivant la structure d'une musique funk. La dernière version d'OMax développée par Jérôme Nika, chercheur à l'Ircam, combine une notion de « scénario musical » préétabli à une écoute 42 https://youtu.be/OsxP4lFp76I

  how do we evaluate a work, what is its value, what is the difference between novelty and modernity, how do we teach an artistic practice, what do we transmit on this occasion, and, among the most enigmatic, what is an artistic choice.

	Participants
	• Working Creatively with Machines
	o Camine Emanuele Cella (CNMAT -CU Berkeley)
	Composer and computer scientist, Carmine develops (amongst other thing)
	Orchidea an automated system to help instrumentation and orchestration.
	o Rémi Mignot (Ircam)
	Rémi do researches about audio indexing and classification (MIR) at STMS lab.
	Since 2018, he has been responsible of researches on music information retrieval
	in the analysis-synthesis team.

o Nicolas Obin (Sorbonne Université -Ircam) Nicolas is associate professor at the Faculty of Sciences of Sorbonne Université and researcher in the STMS lab where his work focuses on speech synthesis and transformations, conversational agent and computational musicology. You can see some example of speech synthesis and "voice fake" on his web page. o Alex Ruthman (NYU) Alex is Associate Professor of Music Education & Music Technology, and the Director of the NYU Music Experience Design Lab (MusEDLab) at NYU Steinhardt where he creates new technologies and experiences for music making, learning, and engagement. Digital technologies have disrupted art education and in particular music education. They have the potential to make Creative Musical Expression more accessible to all. o Jason Freeman (Georgia Tech) Jason is a Professor of Music at Georgia Tech and Chair of the School of Music. His artistic practice and scholarly research focus on using technology to engage diverse audiences in collaborative, experimental, and accessible musical experiences.

  It is the same process that was used in the 18th century by the Musikalisches Würfelspiel to randomly generate music from precomposed options. One of the earliest known examples are the Der allezeit fertige Menuetten-und Polonaisencomponist proposed in 1757 by Johann Philipp Kirnberger. An example by the Kaiser string quartet :

	the late 1940s, Alan Turing noticed that he could produce notes of different pitches by
	modulating the control of the computer's loudspeaker used to signal the end of a calculation batch.
	Christopher Strachey used this trick to make the first pieces: the national anthem, a nursery rhyme
	and Gleen Miller's "In the Mood".
	By the summer of 1952, Christopher Strachey develop "a complete game of Draughts at a reasonable
	speed". He was also responsible of the strange love-letters that appear on the notice board of
	Manchester University's Computer Department from August 1953.
	• http://www.alpha60.de/art/love_letters/
	• https://www.gingerbeardman.com/loveletter/
	Strachey's method of generating love letters by computer is to expand a template by substituting
	randomly chosen words at certain location. Locations belong to certain categories and each
	category corresponds to a pool of predefined words. The algorithm used by Strachey is as follows:
	Generate Salutation 1 and Salutation 2,
	Do this 5 times:
	Randomly generate one of the following two templates:
	1. "You are my" Adjective Noun
	2. "My" Adjective(optional) Noun Adverb(optional) Verb, Your Adjective(optional) Noun
	Generate "Your" Adverb, "MUC"
	Algorithmic control is in italic, locations (placeholders) are underlined and fixed sequence in the
	output are in bold.

  Subsequent versions of EMI also use learning techniques that blossomed again in the early 2000s. As a mater of fact, throughout the history of computer science, two approaches have confronted each other.Symbolic reasoning denotes the AI methods based on understandable, explicit and explainable highlevel "symbolic" (human-readable) representations of problems. Knowledge and information is often represented by logical predicates. The preceding examples fall more into this category.

	• An example produced by Emily Howell
	When David Cope is asked if the computer is creative, he answers :

"Oh, there's no doubt about it. Yes, yes, a million times yes. Creativity is easy; awareness, intelligence, that's hard."

  QyqiSbbwHIs 16 https://iannis-xenakis.org/fxe/ecrits/mus_form.html https://monoskop.org/images/7/74/Xenakis_Iannis_Formalized_Music_Thought_and_Mathematics_in_Composi tion.pdf

• [2/4] https://youtu.be/MrN0pOnA1x4 • [3/4] https://youtu.be/cuq4smO_4Js • [4/4] https://youtu.be/

  Une heuristique désigne une méthode empirique, reposant bien souvent sur des connaissances incomplètes ou approximatives, qui ne garantit pas la rectitude du raisonnement, mais qui produit bien souvent (et rapidement) des résultats satisfaisants. Lorsque la recherche d'une solution optimale n'est pas faisable ou praticable, des méthodes heuristiques peuvent être mises en oeuvre pour accélérer le processus et trouver une solution acceptable.Le raisonnement symbolique correspond aux méthodes d'intelligence artificielle s'appuyant sur une représentation symbolique (c'est-à-dire lisible par l'humain) des problèmes, représentation de haut niveau, compréhensible, explicite et explicable. Les exemples précédents peuvent être classés dans cette catégorie. L'intelligence artificielle symbolique du milieu des années 1980 est aujourd'hui affublée du sobriquet GOFAI (« Good Old Fashioned Artificial intelligence » ou Bonne vieille intelligence artificielle).Au cours de la dernière décennie, on a assisté à un retour en force de toute une série de techniques digitales, souvent inspirées par la biologie, mais prenant également en compte les avancées dans les domaines de la science statistique et de l'apprentissage machine numérique. L'apprentissage numérique repose sur des représentations numériques de l'information à traiter. Un exemple emblématique de ce genre de techniques peut se trouver dans les réseaux de neurones artificiels30 . Cette technique avait déjà été utilisée au cours des années 1960, avec le perceptron 31 inventé en 1957 par Frank Rosenblatt, qui permettait un apprentissage supervisé de classifieurs (un algorithme permettant de décider si l'entrée qu'on lui présente appartient à une catégorie donnée). Par exemple, un perceptron peut être entraîné à reconnaître les lettres de l'alphabet écrites à la main (ici les catégories sont les lettres de l'alphabet). On entre dans le système une matrice de pixels contenant une lettre à reconnaître, et le système donne comme résultat la lettre reconnue. Au cours de la phase d'apprentissage, de nombreux spécimens de chaque lettre sont présentés et le système est ajusté pour donner la classification correcte. Une fois l'apprentissage terminé, une matrice de pixels peut être présentée contenant une lettre qui ne fait pas partie des spécimens utilisés pour l'apprentissage, et le système reconnaît correctement la lettre.Selon l'époque, le paradigme dominant de l'intelligence artificiel a fluctué. Dans les années 1960, l'apprentissage machine était à la mode. Mais à la fin de cette décennie, un fameux article a mis un frein à ce champ de recherche, en montrant que les perceptrons n'étaient pas en mesure de classifier tous les objets. Cela était dû à son architecture, réduite à une couche unique de neurones. On verra plus loin que des classes plus complexes de spécimen peuvent être reconnues en augmentant le nombre de couches de neurones. Malheureusement, il n'existait alors aucun algorithme d'apprentissage pour entraîner des réseaux multicouches.Au début des années 2000, les algorithmes progressent encore, les machines sont de plus en plus rapides, et l'on a un accès de plus en plus important à nombre de bases de données d'exemples, résultat du développement de toutes les techniques digitales. Cette conjonction favorable a relancé la mode des techniques numériques d'apprentissage machine, et on peut entendre dorénavant à tout bout de chant l'expression deep learning 32 ou apprentissage profond (le terme « deep » ou « profond » fait référence à l'épaisseur des couches du réseau à entraîner).La contribution de ces nouvelles techniques d'apprentissage digitales est considérable. Elle permet par exemple de générer des sons directement, et non pas via une partition (le signal sonore étant bien plus riche en informations, son apprentissage nécessite de nombreuses couches et d'innombrables heures de musique enregistrée 33 ). Voici quelques spécimens de sons instrumentaux reconstitués grâce à ces techniques 34 .Bien sûr, ces techniques ont été appliquées à la composition, et on trouve de nombreux exemples de chorals de Bach. Voici un exemple 35 d'une pièce d'orgue 36 produite par un réseau de neurones (folk-rnn), puis harmonisée par un autre réseau de neurones (DeepBach). Et voici un autre exemple 37 de ce qui a pu être accompli (avec folk-rnn) en entraînant un réseau avec 23.962 chansons traditionnelles écossaises (à partir de transcriptions MIDI).L'un des enjeux soulevés par l'apprentissage machine est celui des données utilisées pour ledit apprentissage. Pour des raisons rarement évoquées, et malgré les nombreuses recherches, académiques ou non, la compréhension musicale est un processus profondément complexe et relationnel. La musique est un objet élusif, chargé de polysémies, de questions insolubles et de contradictions. Des pans entiers de la philosophie, de l'histoire de l'art et de la théorie des médias sont consacrés à la mise en évidence de toutes les nuances qui entrent dans la relation ambiguë entre musique, émotion et sens. Les mêmes questions hantent le champ des images mais celles-ci peuvent souvent être abordées sous l'angle de la représentation. La musique, plus abstraite, reste plus insaisissable.Les enjeux économiques ne sont en outre jamais bien loin. Une entreprise comme AIVA a ainsi organisé un concert au Louvre Abou Dabi 38 avec au programme cinq courtes pièces composées par son système et interprétées par un orchestre symphonique. On peut aussi citer l'exemple d'une pièce composée spécifiquement pour la fête nationale du Luxembourg 39 en 2017, et celui de cet extrait d'un album de musique chinoise 40 : Mais attention, seule la mélodie est effectivement générée par l'ordinateur.Le travail d'orchestration, d'arrangement, et le reste, a été réalisé par des humains : https://www.aiva.ai/engine. C'est aussi vrai de nombre de systèmes qui se prétendent des machines de composition automatique, y compris la Symphonie inachevée de Schubert, complétée par un smartphone Huawei 41 .

	Un tel algorithme apparaît dans les années 1980 mais il est encore terriblement lourd à implémenter
	et l'on se rend également compte que l'apprentissage d'un réseau multicouche exige une masse
	faramineuse de données.
	L'apprentissage machine
	33 https://magenta.tensorflow.org/nsynth
	34
	• Contrebasse
	o Original : https://magenta.tensorflow.org/assets/nsynth_04_05_17/sounds/Bass-Original.mp3
	o Wavenet : https://magenta.tensorflow.org/assets/nsynth_04_05_17/sounds/Bass-WaveNet.mp3
	Voici un exemple d'une harmonisation de choral 22 (d'abord l'harmonisation originale de Bach, puis • Glockenspiel
	o Original : https://magenta.tensorflow.org/assets/nsynth_04_05_17/sounds/Glockenspiel-Original.mp3 le résultat produit par CHORAL à 4'42). La note de concert 23 propose une esquisse du système o Wavenet : https://magenta.tensorflow.org/assets/nsynth_04_05_17/sounds/Glockenspiel-WaveNet.mp3 expert. • Bugle
	o Original : https://magenta.tensorflow.org/assets/nsynth_04_05_17/sounds/Flugelhorn-Original.mp3

•

  Une cloche archeos originale et son imitation orchestrale o http://www.orch-idea.org/targets/archeos__bell-sound-b.wav o http://www.orch-idea.org/archeos2/solution_0.wav • Un cri de petite fille et un cri d'orchestre o http://www.orch-idea.org/targets/girl-scream.wav o http://www.orch-idea.org/scream/solution_0.wav • Des gouttes d'eau et leur reproduction orchestrale o http://www.orch-idea.org/targets/drops.wav o http://www.orch-idea.org/drops/connection.wav • Un coq et son homologue musical o http://www.orch-idea.org/targets/coque.wav o http://www.orch-idea.org/coque1/connection.wav 44 https://www.stms-lab.fr/shop/product/omax/ 45 https://www.dailymotion.com/RepMus 46 https://vimeo.com/showcase/7325210/video/376887800 et https://vimeo.com/454041143

The following translation has been made by Jérémie Szpirglas for a publication on the IRCAM and Forum Ircam web sites / Cette traduction du texte precedent a été réalisé par Jérémie Szpirglas pour une reprise sur les sites web de l'Ircam et du ForumIrcam. Les liens qui accompagnent la version anglaises ont été repris dans cette version française sous forme de liens dans les notes (pour ce document).

https://en.wikipedia.org/wiki/Alan_Turing

https://soundcloud.com/the-british-library/first-recording-of-computer-music-1951-copeland-long-restoration

https://blogs.bl.uk/sound-and-vision/2016/09/restoring-the-first-recording-of-computer-music.html

https://videogamehistorian.wordpress.com/2014/01/22/the-priesthood-at-play-computer-games-in-the-1950s/

http://www.alpha60.de/art/love_letters/

https://www.gingerbeardman.com/loveletter/

https://en.wikipedia.org/wiki/Artificial_neural_network

https://blogs.umass.edu/brain-wars/files/2016/03/rosenblatt-1957.pdf

https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html


Anna Gielnik
email: aniagielnik@gmail.com

Yoan Pechaud

David Huguenot

Aurélie Cebron

Giovanni Esposito

Eric D Van Hullebusch

Aurélie Cébron

Functional potential of sewage sludge digestate microbes to degrade aliphatic hydrocarbons during bioremediation of a petroleum hydrocarbons contaminated soil

Keywords: alkB genes, bioreactors, organic wastes, soil clean-up, PHCs

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Functional potential of sewage sludge digestate microbes to degrade aliphatic hydrocarbons during bioremediation

of a petroleum hydrocarbons contaminated soil

Introduction

Intensive exploration and refinery of crude oil have resulted in a worldwide legacy of soils polluted with crude oil composed mostly of alkanes and its derivates, which constitutes a threat for human health, disables soil services and affects land management [START_REF] Shahi | Evaluation of microbial population and functional genes during the bioremediation of petroleum-contaminated soil as an effective monitoring approach[END_REF].

Bioremediation is an efficient strategy to treat petroleum hydrocarbons (PHCs) contaminated soils [START_REF] Lu | Enhanced bioremediation of hydrocarbon-contaminated soil using pilot-scale bioelectrochemical systems[END_REF]. A successful bioremediation treatment requires regulation of soil nutrient ratio (C:N:P), oxygen supply and most of all, dense, specialized microbial biomass able to metabolize the contaminants [START_REF] Safdari | Development of bioreactors for comparative study of natural attenuation, biostimulation, and bioaugmentation of petroleum-hydrocarbon contaminated soil[END_REF]. One of the most important enzymes group involved in PHCs oxidation are alkane monooxygenases encoded by alkB genes, responsible for the first step of alkanes oxidation [START_REF] Fuentes | Bioremediation of petroleum hydrocarbons: Catabolic genes, microbial communities, and applications[END_REF]. Actual research and development efforts are focusing on the increase of the 'soil bioremediation potential' which is connected with the abundance of petroleum degraders, and thus functional genes, responsible for contaminant degradation [START_REF] Abtahi | Effect of competition between petroleum-degrading bacteria and indigenous compost microorganisms on the efficiency of petroleum sludge bioremediation: Field application of mineral-based culture in the composting process[END_REF][START_REF] Powell | Using Real-Time PCR to Assess Changes in the Hydrocarbon-Degrading Microbial Community in Antarctic Soil During[END_REF].

An efficient way to increase the density of PHCs metabolizing bacteria in soil, and thus, to increase soil alkB genes content, is bioaugmentation. This method includes addition of liquid biomass suspensions or immobilized bacteria (e.g. onto biochar) to soil [START_REF] Agnello | Comparative bioremediation of heavy metals and petroleum hydrocarbons cocontaminated soil by natural attenuation, phytoremediation, bioaugmentation and bioaugmentation-assisted phytoremediation[END_REF][START_REF] Mrozik | Bioaugmentation as a strategy for cleaning up of soils contaminated with aromatic compounds[END_REF]. Immobilization of inoculum on porous organic carriers becomes popular practice due to significant improvement of microbial cells preservation [START_REF] Mrozik | Bioaugmentation as a strategy for cleaning up of soils contaminated with aromatic compounds[END_REF]. Dense and diverse microbial populations with a wide metabolic capacity, which may serve for bioremediation purposes are also found in organic fertilizers [START_REF] Barra Caracciolo | Changes in microbial community structure and functioning of a semiarid soil due to the use of anaerobic digestate derived composts and rosemary plants[END_REF][START_REF] Megharaj | Bioremediation approaches for organic pollutants: A critical perspective[END_REF]. However, those fertilizers are poorly investigated in terms of functional genes content allowing alkanes degradation.

Digestate is a by-product of anaerobic digestion of organic residues (e.g. sewage sludge, animal manures, organic fraction of municipal solid wastes) and constitutes a valuable soil amendment with undiscovered potential in bioremediation [START_REF] Kataki | Assessment of by-products of bioenergy systems (anaerobic digestion and gasification) as potential crop nutrient[END_REF]. Compared to raw feedstock, nutrients in digestate are concentrated and present in bioavailable form (e.g. ammonia) [START_REF] Gómez-Brandón | Effects of digestate on soil chemical and microbiological properties : A comparative study with compost and vermicompost[END_REF], additionally the amount of easily biodegradable compounds is reduced which is beneficial for bioremediation as they constitute a preferable carbon source for microorganisms [START_REF] Sayara | Effects of compost stability and contaminant concentration on the bioremediation of PAHs-contaminated soil through composting[END_REF].

Organic fertilizers were already studied in soil bioremediation, however mainly as nutrient sources [START_REF] Osei-Twumasi | Synergistic effects of compost, cow bile and bacterial culture on bioremediation of hydrocarbon-contaminated drill mud waste[END_REF][START_REF] Xi | Biogas slurry as an activator for the remediation of petroleum contaminated soils through composting mediated by humic acid[END_REF]. If the microbial population engaged in PHCs metabolism was studied, the data were restricted to simple analysis monitoring general changes of microbial community (DGGE, TTGE) or identification of culturable bacterial strains [START_REF] Abtahi | Effect of competition between petroleum-degrading bacteria and indigenous compost microorganisms on the efficiency of petroleum sludge bioremediation: Field application of mineral-based culture in the composting process[END_REF]. We hypothesized that bacteria contained in digestate can enrich metabolic potential of soil providing new PHCs degrading taxa and thus increase the content of alkB genes. We also hypothesized that addition of bacteria immobilized onto biochar may further increase process efficiency if inoculated microorganisms have been previously acclimated to treatment conditions. In this study, for the first time, we tested sewage sludge digestate as soil inoculum in lab-scale aerated bioreactors. Our goal was to study how addition of sewage sludge digestate to PHCs contaminated soil affects the concentration of alkB genes and contaminants removal in time. We have also performed sequencing of total bacteria in multiple time points to verify if addition of digestate affected the number of genera associated with PHCs metabolism.

Materials and methods

Experimental set-up

The experiments (Figure 1) were performed in 12 L glass solid phase bioreactors with active aeration designed according to [START_REF] Namkoong | Bioremediation of dieselcontaminated soil with composting[END_REF]. To provide appropriate oxygen amount, bioreactors were aerated from the bottom, with air flow rate ranging between 190 and 200 L h -1 m -3 soil. Before entering into the reactors, CO2 was removed from the inlet air by a NaOH trap. Next, the air passed through a humidifier to remove aspirated alkali solution and to increase humidity content in order to prevent reactors from drying. Exhaust air from the reactors was filtered by an Amberlite ® column in order to adsorb volatile organic compounds, which could be released due to hydrocarbons volatilization. Further, the exhaust air passed through 5M NaOH trap to accumulate CO2 generated during bacterial respiration. The amount of trapped CO2 was quantified by titration with HCl [START_REF] Namkoong | Bioremediation of dieselcontaminated soil with composting[END_REF]. To ensure high porosity of the soil mixture, treated soil was mixed with bulking agent (sawdust). The ratio of added sawdust was adjusted to favor a homogeneous airflow throughout reactor (Rhykerd et al., 1999). Apart from control condition (B1), three biostimulation and bioaugmentation treatment conditions were tested in duplicate (Table 1). Biostimulation treatments covered addition of nitrogen in the form of NH4Cl (B2) or sewage sludge digestate (B3). The digestate to soil ratio was optimized based on previous studies [START_REF] Gielnik | Effect of digestate application on microbial respiration and bacterial communities during bioremediation of petroleum hydrocarbons contaminated weathered soils[END_REF][START_REF] Namkoong | Bioremediation of dieselcontaminated soil with composting[END_REF]. In bioaugmented treatments, bacteria immobilized on biochar were added together with digestate (B4). The ratio of added biochar was suggested by other studies [START_REF] Brown | Comparison of landfarming amendments to improve bioremediation of petroleum hydrocarbons in Niger Delta soils[END_REF]. Bioreactors were incubated during 63 days at 20 °C (±2 °C). To ensure the homogeneity of the mixture, the content of bioreactors was mixed once a week and humidity level was checked by drying the samples overnight at 110 °C and further adjusting reactors content to fit 60% of the water holding capacity (WHC). Temperature inside the reactors was measured during the treatment by thermocouple systems. To study the volatile fraction of PHCs, exhaust column with 25 g of Amberlite ® was extracted every week and the sorbent was replaced.

Once a week, 50 g of a representative sample (10 g of sample collected from 5 points and pooled together) originating from different reactor locations was collected from each reactor and homogenized for PHCs quantification and DNA extraction. Before PHCs extraction, soil mixture was air dried and grinded in a mortar. DNA analyses of every bioreactor were performed in triplicate. For DNA extraction, 5 g aliquots of the fresh samples were stored at -20 °C. Biochar used for bacteria immobilization was produced from sewage sludge digestate by pyrolysis using the Biogreen ® technology [START_REF] Wongrod | Lead sorption by biochar produced from digestates: Consequences of chemical modification and washing[END_REF]. Detailed characteristics of the biochar are given elsewhere [START_REF] Wongrod | Lead sorption by biochar produced from digestates: Consequences of chemical modification and washing[END_REF]. Before use, biochar was washed with ultrapure water to remove remaining impurities until the pH value of leaching water was stabilized, then sterilized by autoclaving and air dried under sterile hood (Xu and Lu, 2010a).

Bacteria enriched for immobilization originated from the soil mixtures with the same composition as in bioreactor B3 (Table 1). Before enrichment, soil mixture was incubated for 2 weeks to allow microbial adaptation and consequent community rearrangements. The immobilization procedure is given in details elsewhere [START_REF] Gielnik | Effect of digestate application on microbial respiration and bacterial communities during bioremediation of petroleum hydrocarbons contaminated weathered soils[END_REF]. The final amount of immobilized bacterial cells represented by colony-forming unit was 3.4 × 10 8 cells per 1 g of biochar.

Addition of biochar to contaminated soil may result in irreversible sorption of PHCs and formation of non-extractable residues. To verify if addition of biochar had significant effect on PHCs concentration decrease, not due to biological processes, a simple test was performed.

Contaminated soil was mixed with non-inoculated biochar in a proportion corresponding with bioreactor B4 (Table 1). Control constituted of a sole soil sample. Water content of both variants was maintained at 60% of WHC and samples were incubated in triplicate for 2 months at 20 °C. After that time PHCs were quantified in all samples. Results have not revealed significant differences in PHCs level between the control and sample with biochar (data not shown).

PHCs quantification

For PHCs analysis 10 g of air-dried and grinded sample was extracted with 10 mL of hexane. To study PHCs volatilization, 25 g of Amberlite ® from the exhaust gas column was extracted with a mix of 15 mL methanol and 15 mL hexane. The extraction procedure was based on USEPA 8015B and 3550s methods (USEPA, 2007(USEPA, , 1996)), with mechanical shaking combined with ultrasonic extraction. PHCs were quantified on gas chromatography with flame ionization detector (Shimadzu) with capillary column 30 m ´ 0.25 mm ´ 0.25 µm (ZB5HT Inferno, Phenomenex) and hydrogen as a carrier gas. Maximal oven temperature was 325 °C.

Injection was done with 1 µL of sample in the split mode with split ratio 20 and temperature 285 °C. Column flow was set at 3.0 mL.min -1 with pressure 113.5 kPa. Diesel oil was used as quantification standard.

DNA extraction

Genomic DNA was extracted from 500 mg of freeze samples using Fast DNA Spin Kit for Soils (MP Biomedicals). Extracted DNA was eluted in 100 µL of DNA free ultra-pure water.

DNA concentration and purity was determined using spectrophotometer UV-1800 (Shimadzu) equipped with a TrayCell adaptor for micro-volumes (Hellma) [START_REF] Biache | Bioremediation of PAH-contamined soils: Consequences on formation and degradation of polar-polycyclic aromatic compounds and microbial community abundance[END_REF]. DNA was stored at -20 ºC for further analyses.

Real-time PCR

The extracted genomic DNA was used to quantify total bacterial and fungal populations.

Primers selected for total bacteria were 968F/1401R [START_REF] Felske | Quantification of 16S rRNAs in complex bacterial communities by multiple competitive reverse transcription-PCR in temperature gradient gel electrophoresis fingerprints[END_REF] targeting 16S rDNA and Fung5F/FF390R [START_REF] Smit | Analysis of fungal diversity in the wheat rhizosphere by sequencing of cloned PCR-amplified genes encoding 18S rRNA and temperature gradient gel electrophoresis[END_REF][START_REF] Vainio | Direct analysis of wood-inhabiting fungi using denaturing gradient gel electrophoresis of amplified ribosomal DNA[END_REF] targeting 18S rDNA for fungi. Functional genes, i.e. alkanes hydroxylating monooxygenases genes (alkB), were amplified using primers AlkBF: 5'-AACTACATCGAGCACTACGG-3' and AlkBR: 5'-TGAAGATGTGGTTGCTGTTCC-3' [START_REF] Powell | Using Real-Time PCR to Assess Changes in the Hydrocarbon-Degrading Microbial Community in Antarctic Soil During[END_REF] using 50°C for annealing temperature. Real-time polymerase chain reaction (qPCR) quantifications were performed using CFX96 Real Time PCR detection system (Bio-Rad) and SybrGreen detection (iQ sybr green Supermix, Bio-Rad), according to the procedure described elsewhere [START_REF] Cébron | Impact of clay mineral, wood sawdust or root organic matter on the bacterial and fungal community structures in two aged PAH-contaminated soils[END_REF][START_REF] Cébron | Real-Time PCR quantification of PAH-ring hydroxylating dioxygenase (PAH-RHDα) genes from Gram positive and Gram negative bacteria in soil and sediment samples[END_REF] and using dilution series (10 1 to 10 8 copies.µL -1 ) of standard plasmids.

Sequencing analysis

Ilumina MiSeq v3 Sequencing ( 2´ 300 bp) of the V3-V4 region of the 16S rDNA was performed by MicroSynth AG (Switzerland) on previously isolated DNA. The company is ISO certified according to 9001:2008 and ISO / IEC 17025. Library preparation included sample quality control and Nextera two-step PCR amplification using primer set 341f_ill/802r_ill, PCR product purification, quantification and equimolar pooling. Bioinformatic analysis included demultiplexing, merging of forward and reverse reads, quality filtering, trimming, chimera removal, operational taxonomic unit (OTU) clustering (97% identity threshold) and subsampling for even sample size (rarefaction to the lower number of reads per sample).

Taxonomy assignment was based on the SILVA 16S database v.123 (> 60% confidence). Alpha diversity calculation and comparative statistics were done with the use of Phyloseq and DeSeq2 (R packages). Heat map of the most abundant genera and clustering with Pearson method were performed using free Heatmapper software (heatmapper.ca).

Germination ecotoxicity tests

Germination tests were performed at the beginning and at the end of the experiment with the use of germination plates and seeds of garden cress (Lepidium sativum) and mustard (Sinapis alba) as recommended by previous studies [START_REF] Gargouri | Bioremediation of petroleum hydrocarbons-contaminated soil by bacterial consortium isolated from an industrial wastewater treatment plant[END_REF][START_REF] Maila | The use of biological activities to monitor the removal of fuel contaminants-perspective for monitoring hydrocarbon contamination: a review[END_REF][START_REF] Mao | Estimation of ecotoxicity of petroleum hydrocarbon mixtures in soil based on HPLC-GCXGC analysis[END_REF]. Germination plates were filled with 80 g of fresh homogenized sample from each reactor. Control consisted of uncontaminated soil (obtained from the same industrial area as contaminated soil) mixed with sawdust and digestate in the same ratio as soil from bioreactors. Ten seeds were placed equally on each plate. Germination plates were incubated in a growth chamber for 7 days, at 21 °C, with a photoperiod of 12/12 and photosynthetic photon flux density at 100 µmol m -2 s -1 . After 7 days, germinated plants were counted, and radicle length was measured. Based on obtained data the germination index (GI) was calculated [START_REF] Graj | Bioaugmentation with petroleum-degrading consortia has a selective growth-promoting impact on crop plants germinated in diesel oil-contaminated soil[END_REF] according to the equation:

(1)

where: Gs and Gc are numbers of seeds germinated in the sample and control, respectively, whereas Ls and Lc are the radicle lengths in the sample and control [mm], respectively.

Microtox ® ecotoxicity assay

The toxicity of soil elutriates was determined using the Microtox ® bioassay according to previous studies [START_REF] Khudur | Evaluating the efficacy of bioremediating a diesel-contaminated soil using ecotoxicological and bacterial community indices[END_REF]. Briefly, 1 g of air-dried soil was mixed with 9 mL of ultra-pure water and incubated overnight in the dark at 150 revolutions per minute. After incubation each sample was centrifuged at 4 500 revolutions per minute for 10 min. The luminescence was measured on the dilutions of soil leaches at 15 °C after 5 and 15 min of exposure, using a Microtox M500 Analyzer and with Aliivibrio fischeri (Hach, France) as a biological reagent. Phenol (20 mg L -1 ) and zinc sulfate (10 mg L -1 ) standards, as well as reagent

[ ] % 100 = Lc Ls Gc
Gs GI control were run with each bath. Delta EC50 represents toxicity loss between samples at day 1 and day 63.

Statistical analysis

Statistical analyses were performed using XL Stat (Addinsoft 2015) statistical software for Excel. Significant differences of parameters among the treatments were detected with one-way ANOVA (p < 0.05) followed by Tukey Post-Hoc test.

Results and discussion

PHCs removal and mineralization extent

PHCs removal over time is presented in Fig. 2. The highest removal after 63 days was observed for treatment B4 containing digestate and immobilized bacteria (94.5%), then for B3 with digestate (73.7 %), B2 with mineral nutrients (56.5 %) and the control B1 (13.6 %).

Differences observed after 63 days are significant (ANOVA; P ≤ 0.05). During the first 30 days more than 50% of PHCs were degraded in treatments containing digestate (B3 and B4), while in treatment with mineral nutrients 50% of PHCs were degraded after 55 days. PHCs degradation efficiencies reported in the literature vary from few weeks to few months depending on soil and contamination characteristics as well as experimental scale [START_REF] Agnello | Comparative bioremediation of heavy metals and petroleum hydrocarbons cocontaminated soil by natural attenuation, phytoremediation, bioaugmentation and bioaugmentation-assisted phytoremediation[END_REF][START_REF] Dados | Rapid remediation of soil heavily contaminated with hydrocarbons: a comparison of different approaches[END_REF][START_REF] Megharaj | Bioremediation approaches for organic pollutants: A critical perspective[END_REF]. In the present study, 2 months of treatment were sufficient for almost complete soil clean-up in the condition employing digestate and immobilized bacteria (B4).

PHCs removal was followed by the decrease of characteristic unresolved chromatographic peak (Fig. S1) representing weathered polar substances of highly recalcitrant nature [START_REF] Bruckberger | Biodegradability of polar compounds formed from weathered diesel[END_REF]. For the treatments B1 and B2, PHCs increased above initial concentration in the first 15 days, which was not observed in treatments containing digestate.

Similar observations were reported in another study [START_REF] Dados | Rapid remediation of soil heavily contaminated with hydrocarbons: a comparison of different approaches[END_REF], where the use of (NH4)2SO4 as a nutrient source resulted in initial increase of PHCs concentration within the first 20 days, while for treatment with compost the increase did not occur. This suggests buffering capacities of organic amendments in terms of PHCs metabolites (e.g. aldehydes) (Xu and Lu, 2010b). B3 condition; B4 condition. Abbreviations (B1, B2, B3, B4) refer to Table 1. Results are presented as a mean of samples collected from two reactors and analyzed in triplicate (n=6) with standard deviation.

Petroleum compounds in contaminated soils can be removed by biodegradation, biotransformation, volatilization or can be adsorbed on organic matter in a form of nonextractable residues [START_REF] Kästner | Classification and modelling of nonextractable residue (NER) formation of xenobiotics in soil -A synthesis[END_REF]. In the present study, volatilization values were below 0.5 % of PHCs loss and thus were not further considered. Mineralization of organic carbon represented by CO2 evolution was monitored to support information about microbial metabolic activity and PHCs degradation (Fig. 3). We observed that CO2 evolution was correlated with PHCs removal, suggesting that biodegradation was the major force driving contaminant concentration decrease (Table 3). Mineralization values observed in the study are corresponding with the data of other authors reporting that about 10 g CO2 kg -1 soil was produced during first 30 days of soil treatment with sewage sludge in similar amendment ratio [START_REF] Namkoong | Bioremediation of dieselcontaminated soil with composting[END_REF]. Application of mineral nutrients often results in lower mineralization rates than organic fertilizers [START_REF] Cerqueira | Comparison of bioremediation strategies for soil impacted with petrochemical oily sludge[END_REF][START_REF] Tahhan | Biodegradation of petroleum industry oily-sludge using Jordanian oil refinery contaminated soil[END_REF]). In the present study, the use of digestate doubled respiration rates in comparison with mineral nutrients and was further increased by the application of immobilized bacteria which correlated with PHCs removal degree (Table 3).

Table 3. Pearson correlation coefficients and p values between soil respiration and PHCs levels.

Abbreviations are reported in Table 1.

Intensive mineralization of organic matter during soil incubation is often followed by temperature rise [START_REF] Antizar-Ladislao | Bioremediation of polycyclic aromatic hydrocarbons (PAH) in an aged coal-tar-contaminated soil using different in-vessel composting approaches[END_REF]. However, in the present study, temperature inside reactors was equal to the temperature of the inlet air (20 ± 2 ºC). As during anaerobic digestion easily biodegradable carbon is depleted and organic matter becomes stabilized, incubation of digestate may not result in rapid organic matter degradation and heat production.

However, a significant organic fraction of organic matter can still be slowly degraded, which eventually contributes to observed CO2 production. This is in accordance with the observations of other authors [START_REF] Tambone | Composting of the solid fraction of digestate derived from pig slurry: Biological processes and compost properties[END_REF], where composting of different pig slurry digestates did not always resulted in temperature rise but induced changes in Potential Dynamic Respiration Index (PDRI).

Soil toxicity changes

The concentration of contaminants does not provide direct information about soil (Xu and Lu, 2010a).

The values of germination index (GI) for both tested plants followed the trend of PHCs removal yield with high toxicity for condition B1 (GI < 20 %) and low toxicity values for conditions B3 and B4 (GI > 90%). In the same way, Microtox ® assay, presented as the relative change of EC50 between the start and the end of the experiment, also revealed a toxicity decrease in accordance with PHCs removal yields (B1 < B2 < B3 < B4).

High efficiency of PHCs biodegradation was followed by high soil toxicity decrease in treatments containing digestate and digestate with immobilized bacteria. Observed soil toxicity decrease is comparable with results of other authors analyzing bioremediation of industrially contaminated soils treated with mineral fertilizers (Xu and Lu, 2010a). Moreover, in the study of [START_REF] Nwankwegu | Studies on organic and in-organic biostimulants in bioremediation of diesel-contaminated arable soil[END_REF] as well as in the present study, application of organic fertilizer resulted in higher PHCs removal and toxicity decrease than the use of mineral nutrients pointing that successful remediation may be performed with the use of sustainable nutrient sources like digestate.

Functional genes abundance

Genes copies corresponding to the total bacterial (16S rDNA) and fungal (18S rDNA) communities (Fig. S2), as well as alkB genes (Fig. S3) encoding monooxygenases crucial for alkanes oxidation [START_REF] Powell | Using Real-Time PCR to Assess Changes in the Hydrocarbon-Degrading Microbial Community in Antarctic Soil During[END_REF] were measured at day 1, 30 and 63. The estimated abundance of fungi was around 2 orders of magnitude lower compared to bacteria (Fig. S2),

which is a common observation in soil. Total bacterial population differed among treatments and slightly changed with time. Significantly more 16S rRNA gene copies were found in digestate and treatments B2, B3, B4 compared to the control (B1).

Figure 4A presents changes of the alkB genes concentrations with time. The content of alkB genes for all treatments was as follow B4>B3>B2>B1 (ANOVA; p < 0.05) at all monitoring time points, which corresponds with PHCs removal. For all treatments, excluding control, the lowest concentration of alkB genes was observed at the beginning of the experiment, then it greatly increased during the middle phase and further slightly decreased at the end. Addition of digestate (containing about 5.3 × 10 6 copies of alkB genes per g of dry weight), has significantly increased the copy number of alkB genes in soil. The initial number of alkB genes in control was 7.6 × 10 5 while in treatment B3 containing digestate the value exceeded 10 6 copies g -1 soil (Fig. 4A). Application of immobilized bacteria in treatment B4 additionally raised the gene copies number above 10 7 copies g -1 soil. Such a high concentrations of alkB genes are outstanding in comparison to values obtained with mineral fertilizer as well as values reported in the literature [START_REF] Masy | Hydrocarbon biostimulation and bioaugmentation in organic carbon and clay-rich soils[END_REF][START_REF] Shahi | Evaluation of microbial population and functional genes during the bioremediation of petroleum-contaminated soil as an effective monitoring approach[END_REF][START_REF] Sutton | Biodegradation of aged diesel in diverse soil matrixes: Impact of environmental conditions and bioavailability on microbial remediation capacity[END_REF]. 1. D: digestate. Results are presented as an average value of three samples collected from the same reactor with standard deviation.

At day 1, the percentage of alkB relative to 16S rRNA gene copies for control (B1) and treatment with mineral nutrients (B2) were 1.4% and 0.4%, respectively (Fig. 4B), which is in accordance with other studies where the observed values of alkB/16S rDNA for soil treatment with mineral nutrients ranged between 0.1 and 0.6% [START_REF] Powell | Using Real-Time PCR to Assess Changes in the Hydrocarbon-Degrading Microbial Community in Antarctic Soil During[END_REF][START_REF] Sutton | Biodegradation of aged diesel in diverse soil matrixes: Impact of environmental conditions and bioavailability on microbial remediation capacity[END_REF].

In treatment with digestate (B3) the initial alkB/16S rDNA ratio reached 4.2% while in treatment with digestate and inoculated biochar (B4) reached 17.6%, the differences between all treatments were significant (ANOVA; p < 0.05). The final values of the ratio were 2.0, 1.3, 11.6, 11.9% for set-up B1, B2, B3 and B4, respectively, which indicates an important role of digestate in supplementation and maintenance of high proportion of bacteria carrying alkB genes in soil. These results are in accordance with a previous microcosm study (Gielnik et al., 2019).

The number of PHCs degrading bacteria, and thus the amount of alkB genes are connected to the amount of available substrate (e.g. PHCs) and bacterial activity. Thus, low contents of alkB genes were observed at the beginning of the experiment, before the intensive proliferation of biomass which is normally observed in the first half of the treatment. Intensive bioremediation in the first month was followed by lower activity in the second part of the experiment due to decrease of available substrate, which can be observed in Figure 2.

Interestingly, very high ratio of alkB/16S rDNA (60.1%) was observed in the middle phase for treatment B4. It was probably due to introduction of inoculum already acclimated to treatment conditions which allowed fast microbial growth and dominance of bacteria carrying alkB genes over non-specialized soil microbial population. It also suggests that in other treatments, in the phase of intensive proliferation, not only PHCs degraders, but also other bacterial species were promoted.

Bacterial community composition

The effect of microorganisms originating from organic fertilizers on bioremediation of PHCs is poorly studied. It was previously observed that successful bioremediation of petroleum products is connected with diverse bacterial community carrying functional genes (e.g. alkB or cytochrome P450 monooxygenase genes) but also with ecological equilibrium among bacteria, including species not directly engaged in bioremediation [START_REF] Akbari | Pilot-scale bioremediation of a petroleum hydrocarboncontaminated clayey soil from a sub-Arctic site[END_REF]. Thus, sequencing of 16S rRNA was performed to study in which extent soil indigenous microflora may be affected by the presence of organic fertilizer and which bacterial groups (originating from soil or from digestate) dominate.

The main bacterial phyla are presented in Figure S3. For all the treatments, dominant phyla throughout the incubation were Proteobacteria or Actinobacteria which are common soil phyla detected during bioremediation studies [START_REF] Fuentes | Bioremediation of petroleum hydrocarbons: Catabolic genes, microbial communities, and applications[END_REF], while in digestate the dominant phyla were Proteobacteria (35.9%) and Aminicenantes (30.0%). In treatments B1 and B2, an increase of Verrucomicrobia with time was observed. This phylum is common in soil environments [START_REF] Bergmann | The under-recognized dominance of Verrucomicrobia in soil bacterial communities[END_REF]. Verrucomicrobia was already found in minority in hydrocarbon affected soils and the phylum tended to diminish at the end of bioremediation [START_REF] Ramadass | Bioavailability of weathered hydrocarbons in engine oil-contaminated soil : Impact of bioaugmentation mediated by Pseudomonas spp . on bioremediation[END_REF], which suggests its secondary role in PHCs degradation. Aminicenantes and Atribacteria phyla were specific for fresh digestate, and were already detected during anaerobic digestion of sewage sludge [START_REF] Pan | Impact of nano zero valent iron on tetracycline degradation and microbial community succession during anaerobic digestion[END_REF][START_REF] Zhao | Effects of individual and complex ciprofloxacin, fullerene C60, and ZnO nanoparticles on sludge digestion : Methane production, metabolism, and microbial community[END_REF]. Probably due to anaerobic preferences, representatives of the Atribacteria phylum were not detected after mixing of digestate with soil and application of aeration, while Aminicenantes representatives were detected in samples B4 but their relative proportion decreased with time.

In all treatments, except for B4, Proteobacteria dominated over Actinobacteria at the beginning of the treatment, however with time Actinobacteria tended to increase.

Actinobacteria is the main phylum observed in oil bioremediation treatments and contains many species recognized as hydrocarbons degraders [START_REF] Ros | Microbial communities involved in the bioremediation of an aged recalcitrant hydrocarbon polluted soil by using organic amendments[END_REF]. Interestingly, in treatment B4, Actinobacteria was a dominant phylum at the beginning of the experiment which can be explained by previous acclimation of microbial inoculum. This observation suggests that introduced inoculum was well prepared to degrade hydrocarbons and thus its application allowed to increase process performance.

Top genera differed among samples according to treatment and sampling time (Fig. 5). Detailed information about genera and list of identified species are included in supplementary materials (Tables S1 andS2).

Clustering analysis revealed a distinct bacterial population in digestate and treated soil.

Similarities between microbial profiles were initially observed for treatments B1 and B2 (B1_S and B2_S), however in the middle phase of the treatment the two profiles became distinct which suggests that addition of mineral nutrients shaped soil microbial profile. Samples from reactors B4 displayed similar microbial profiles at the beginning and in the middle phase of the treatment (B4_S and B4_M) which could be explain by the applied inoculum microbial populations that were well adapted to degrade PHCs. A slight population shift in treatment B4 occurred at the end of the incubation (B4_E) which was probably a consequence of PHCs depletion.

In the control (B1), the most abundant genera at the beginning of the experiment were Pseudomonas (46.8%). Pseudomonas genus is a common soil bacterial taxa and contains many well-known PHCs degrading species [START_REF] Liu | Multiple alkane hydroxylase systems in a marine alkane degrader, Alcanivorax dieselolei B-5[END_REF][START_REF] Reyes-Sosa | Bioprospecting for microbes with potential hydrocarbon remediation activity on the northwest coast of the Yucatan Peninsula, Mexico, using DNA sequencing[END_REF]. After 30 days, the population of Pseudomonas decreased to 1.6% and other species associated with PHCs degradation were promoted including Pseudoxanthomonas, Microbacterium and Nocardioides [START_REF] Liu | Multiple alkane hydroxylase systems in a marine alkane degrader, Alcanivorax dieselolei B-5[END_REF][START_REF] Reyes-Sosa | Bioprospecting for microbes with potential hydrocarbon remediation activity on the northwest coast of the Yucatan Peninsula, Mexico, using DNA sequencing[END_REF][START_REF] Yu | Chemosphere Indigenous microorganisms residing in oil sands tailings biodegrade residual bitumen[END_REF].

In the treatment B2, Pseudomonas were dominant in the first days of the study (90%) and dropped to 4% after 30 days. At the end of the incubation the dominant genera included Microbacterium (24.2%), Cellvibrio (13.6%), Dietzia (12.1%) and Georgenia (6%).

Interestingly, most Dietzia affiliated OTUs were assigned to Dietzia maris previously identified as hydrocarbons degrading bacteria [START_REF] Sutton | Biodegradation of aged diesel in diverse soil matrixes: Impact of environmental conditions and bioavailability on microbial remediation capacity[END_REF].

In soil amended with digestate (B3), the most common genera at the beginning were Pseudomonas (33%), Acinetobacter (32.3%) and Rhodococcus (12.4%), all of these genera contain species with known ability to degrade PHCs [START_REF] Liu | Multiple alkane hydroxylase systems in a marine alkane degrader, Alcanivorax dieselolei B-5[END_REF][START_REF] Reyes-Sosa | Bioprospecting for microbes with potential hydrocarbon remediation activity on the northwest coast of the Yucatan Peninsula, Mexico, using DNA sequencing[END_REF][START_REF] Yu | Chemosphere Indigenous microorganisms residing in oil sands tailings biodegrade residual bitumen[END_REF]. Interestingly, in the middle part of the process, Alkanindiges reached a peak abundance of 17.5% and almost totally disappeared after the next 30 days (0.2%). Alkanindiges genera contains many PHCs degrading species [START_REF] Bogan | Alkanindiges illinoisensis gen. nov., sp. nov., an obligately hydrocarbonoclastic, aerobic squalane-degrading bacterium isolated from oilfield soils[END_REF]. The increase of Alkanindiges population in the middle part of the treatment may be connected with the phase of intensive PHCs degradation while decrease at the end of the treatment could be a result of bioavailable hydrocarbons depletion.

In case of treatment B4, at the beginning, the dominant genera were Rhodococcus (41.6%) and Acinetobacter (13.1%) associated with PHCs degradation [START_REF] Liu | Multiple alkane hydroxylase systems in a marine alkane degrader, Alcanivorax dieselolei B-5[END_REF][START_REF] Reyes-Sosa | Bioprospecting for microbes with potential hydrocarbon remediation activity on the northwest coast of the Yucatan Peninsula, Mexico, using DNA sequencing[END_REF][START_REF] Yu | Chemosphere Indigenous microorganisms residing in oil sands tailings biodegrade residual bitumen[END_REF]. After 63 days, the relative importance of Rhodococcus decreased to 15.8% and other PHCs degrading genera became dominant including Alcanivorax (21%), Microbacterium (7.6%) and Arenibacter (5%) [START_REF] Reyes-Sosa | Bioprospecting for microbes with potential hydrocarbon remediation activity on the northwest coast of the Yucatan Peninsula, Mexico, using DNA sequencing[END_REF]. In all treatments amended with digestate, initial richness of genera associated with PHCs degradation and overall diversity measures were higher compared to reactors B1 and B2.

It was observed that treatments supplemented with digestate were characterized by more dense and diverse microbial communities compared to treatments with mineral nutrients. It could be a result of the dense digestate microbial population as well as improved soil conditions including increase of soil organic matter, water holding capacity and contaminant dilution, making soil more suitable environment for microbial development (Ren et al., 2018). Not only bacterial taxa directly involved in the metabolism of PHCs were promoted during the study.

The relations between different bacterial taxa are not evident and may include symbiosis, cooperation or mutualistic dependences additionally affected by abiotic conditions. Bacterial community composition was initially influenced by digestate addition however it had not crucial effect on the direction of microbial community changes. As mentioned in other studies, the direction of the bacterial community evolution in amended soils does not only depend strictly on the type of organic matter but also on soil type and environmental conditions [START_REF] Pérez-Piqueres | Response of soil microbial communities to compost amendments[END_REF]. Abbreviations are reported in Table 1. D: digestate; S: day 1; M: day 30; E: day 63. Results are presented as a mean of three samples collected from the same reactor. To show similarities in genera composition between samples Pearson clustering method was employed.

Conclusions

Application of digestate to a PHCs contaminated soil in lab scale aerated bioreactors has enhanced soil bioremediation potential, as reflected by the higher concentration of alkB genes compared to mineral fertilizer. Digestate supplementation to soil improved activity, density and diversity of total microbial population. Moreover, use of digestate positively affected the density and diversity of bacterial taxa containing PHCs degrading species.

Monitoring of the concentration of alkB genes during the treatments reflected the PHCs removal efficiency, which confirms role of alkB genes as biodegradation marker. Future studies could focus on alkB genes diversity in organic fertilizers, the kinetics of functional genes spreading in soil after fertilizer application and fluctuation of functional genes abundance according to the bioavailable contaminant concentration.

Figure 1 .

 1 Figure 1. Scheme of bioreactor set up used in the study. FM: flow meter. Arrows indicates the direction of the airflow.

Figure 2 .

 2 Figure 2. PHCs depletion during 63 days treatment for B1 condition; B2 condition; B3 condition;B4 condition. Abbreviations (B1, B2, B3, B4) refer to Table1. Results are presented as a mean of samples collected from two reactors and analyzed in triplicate (n=3) with standard deviation.

Figure 3 .

 3 Figure 3. Carbon mineralization represented by cumulated CO2 production for B1 condition; B2 condition;B3 condition; B4 condition. Abbreviations (B1, B2, B3, B4) refer to Table1. Results are presented as a mean of samples collected from two reactors and analyzed in triplicate (n=6) with standard deviation.

Figure 4 .

 4 Figure 4. A) AlkB genes copy number during the treatments; B) Percentage alkB genes relative to total bacteria represented by 16S rRNA gene copy number. Treatment times: day 1 ; day 30 ; day 60 . Abbreviations are reported in Table1. D: digestate. Results are presented as an average value of three samples collected from the same reactor with standard deviation.

Figure 5 .

 5 Figure 5. Heat map illustrating top genera (> 0.2%) based on relative abundance (%).Abbreviations are reported in Table1. D: digestate; S: day 1; M: day 30; E: day 63. Results are presented as a mean of three samples collected from the same reactor. To show similarities in genera composition between samples Pearson clustering method was employed.

Table 1 . Treatments analyzed during the experiment. Set-up Description

 1

	B1	Control: Soil (5.00 kg) + sawdust (0.49 kg = 500 cm 3 ) + water (60% WHC)
	B2	Soil (5.00 kg) + sawdust (0.49 kg = 500 cm 3 ) + NH4Cl (C:N:P 100:10:2)
		+ water (60% WHC)
	B3	Soil (3.75 kg) + sawdust (0.49 kg = 500 cm 3 ) + digestate (1.25 kg) + water (60% WHC)
	B4	Soil (3.50 kg) + sawdust (0.49 kg = 500 cm 3 ) + digestate (1.25 kg)
		+ bacteria immobilized onto biochar* (0.25 kg) + water (60% WHC)
	*indigenous hydrocarbon degrading bacteria were enriched from bioreactor B3
	1

.2 Soil, digestate and biochar

	Sewage sludge digestate was obtained from a biogas plant located in Limoges (France).
	Soil contaminated with petroleum products was obtained from an oil refinery site located in the
	north-east of France. Soil was air dried and sieved through < 2 mm according to the ISO 11464
	standard method. Fresh digestate was homogenized, sieved through < 2 mm and stored at 4 °C
	before analysis. Detailed parameters of both digestate and soil are presented in Table 2. Initial
	physico-chemical characterization of soil and digestate as well as concentration of polycyclic
	aromatic hydrocarbons (PAHs) in the soil was performed by Synlab (France, ISO/IEC
	17025:2005).

Table 2 Characteristics of soil and digestate used in the study

 2

		Soil	Sewage sludge digestate	Method
	pH (H2O)	6.2 (0.2)	7.1 (0.1)	NF ISO 10693
	Water content (%) 0.4 (0.1)	95.6 (0.2)	NEN-ISO 11465
	WHC (mL cm -3 )	0.2 (0.1)	n.a.	OECD test No. 222
	OM (%)	2.2 (0.1)	87.3 (0.7)	NF ISO 14235
	TOC (g kg -1 DW) 15 (1)	120 (7)	NEN-EN 13137
	TN (g kg -1 DW)	0.3 (0.1)	20.8 (0.8)	NEN-EN-ISO 11732
	P (g kg -1 DW)	0.3 (0.1)	10.0 (0.2)	NEN 6961, CEN/TS 16171,
				NF-EN 16179
	C:N:P	100:2.3:2.0	100:17.3:8.3	-
	PHCs (mg kg -1	13200 (124)	1.8 (0.1)	Section 2.4
	DW)
	US-EPA 16 PAHs	25 (1)	n.a.	Internal method (SynLab)
	(mg kg -1 DW)
	Total elements content (mg kg -1 DW)	NEN 6961, NEN-EN-ISO17294-2
	Fe	7600 (124)	54000 (210)
	Al	2700 (35)	7600 (14)
	Cd	0.3 (0.0)	0.7 (0.0)
	Cr	8.7 (0.1)	34.0 (0.3)
	Cu	57 (1.0)	110 (1.2)
	Hg	6.5 (0.2)	0.7 (0.1)
	Pb	120 (4.4)	27 (2.3)
	Ni	7.0 (0.1)	8.9 (0.4)
	Zn	88 (1.5)	270 (6.2)
	n.a.: not analyzed

. OM: organic matter; P: phosphorus; TN: total nitrogen; TOC: total organic carbon; PHCs: petroleum hydrocarbons, WHC: water holding capacity, DW: dry weight, US-EPA: United States Environmental Protection Agency.

Table 4 Toxicity changes after 63 days of incubation.

 4 Abbreviations are reported in Table1. GI: Germination Index. Results are presented as a mean of samples collected from two reactors and analyzed in triplicate with standard deviation in bracket. Values of the same line followed by different letter are significantly different (ANOVA p < 0.05).

	Toxicity assay	B1	B2	B3	B4
	GI Lepidium sativum (%)	11.4 (2.6)c	58.2 (5.3)b	98.7 (1.7)a	96.2 (3.5)a
	GI Sinapis alba (%)	16.5 (5.2)b	86.7 (6.3)a	93.0 (6.3)a	93.7 (1.9)a
	Microtox ® Δ EC50	16.7 (2.8)c	35.0 (5.0)b	63.3 (5.8)a	73.3 (5.8)a

Acknowledgements

This research project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement N 643071.

Supplementary materials

Functional potential of sewage sludge digestate microbes to degrade aliphatic hydrocarbons during bioremediation of a petroleum hydrocarbons contaminated soil Anna Gielnik 1,2 , Yoan Pechaud 1 , David Huguenot 1 , Aurélie Cébron 3 , Giovanni Esposito 2 , Eric D.