Cristiana Bolchini
email: cristiana.bolchini@polimi.it

Alberto Bosio
email: alberto.bosio@ec-lyon.fr

Luca Cassano
email: luca.cassano@polimi.it

Bastien Deveautour
email: bastien.deveautour@cpe.fr

Giorgio Di Natale
email: giorgio.di-natale@univ-grenoble-alpes.fr

Antonio Miele
email: antonio.miele@polimi.it

Ian O'connor
email: ian.oconnor@ec-lyon.fr

Elena Ioana Vatajelu

Ioana Vatajelu
email: ioana.vatajelu@univ-grenoble-alpes.fr

Dependability of Alternative Computing Paradigms for Machine Learning: hype or hope?

Keywords: Machine Learning (ML), ML-specific HW Architectures, Neuromorphic Computing, ML Dependability

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

I. INTRODUCTION

Machine Learning (ML) has proven to give very good results for many complex tasks and applications, such as object recognition in images/videos, natural language processing, satellite image recognition, robotics, aerospace, smart healthcare, and autonomous driving [START_REF] Deng | Recent advances in deep learning for speech research at microsoft[END_REF]- [START_REF] Silver | Mastering the game of go with deep neural networks and tree search[END_REF]. Unfortunately, nothing came for free. Indeed, the hidden cost behind ML performance stems from their high complexity in terms of operations to be computed and the involved amount of data. The direct consequence is the need of high performance computers, long execution time (especially during training) and high power consumption. For example, the famous AlphaGo [START_REF] Silver | Mastering the game of go with deep neural networks and tree search[END_REF] required 4 to 6 weeks of training executed on a machine composed of 2,000 CPUs and 250 GPUs consuming about 600kW.

The direct consequence is that the computational workload is limited, in particular for embedded platforms, by the two well-known walls of computing architectures: [START_REF] Deng | Recent advances in deep learning for speech research at microsoft[END_REF] The memory wall due to the increasing gap between processor and memory speeds, and the limited memory bandwidth making the memory access the killer of performance and power for memory access dominated applications; [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF] The power wall as the practical power limit for cooling is reached, meaning no further increase in CPU clock speed.

Nowadays, there is intense activity in designing custom Artificial Intelligence hardware accelerators based on alternative computing paradigms to support the energy-hungry data movement, speed of computation, and memory resources that MLs require to realize their full potential [START_REF] Mauro | Always-on 674 w@4gop/s error resilient binary neural networks with aggressive sram voltage scaling on a 22-nm iot end-node[END_REF]- [START_REF] Chen | Eyeriss v2: A flexible accelerator for emerging deep neural networks on mobile devices[END_REF]. A promising solution leverages on the Computation in Memory (CiM) paradigm that moves the computation directly inside the memory, reducing thus the need for data transfer between memory and processor [START_REF] Yang | Design considerations for efficient deep neural networks on processing-in-memory accelerators[END_REF]. Other solutions leverage on the Neuromorphic computing aiming at reproducing the biological neurons/synapse structure directly in hardware [START_REF] Ezzadeen | Low-overhead implementation of binarized neural networks employing robust 2t2r resistive ram bridges[END_REF], [START_REF] Markovic | [END_REF].

Independently on the adopted computing architecture and technology, specialized hardware for machine learning is subject to design and fabrication issues, such as: variations in fabrication process parameters, fabrication process defects, latent defects, i.e., defects undetectable at time-zero post-fabrication testing that manifest themselves later in the field of application, silicon ageing, e.g., time-dependent dielectric breakdown, or even environmental stress, such as heat, humidity, vibration, and Single Event Upsets (SEUs) stemming from ionization.

All these problems can lead to performance degradation or operational failures, which in turn can have important consequences, especially for safety-critical systems [START_REF] Lotfi | Resiliency of automotive object detection networks on gpu architectures[END_REF]- [START_REF] Spyrou | Reliability Analysis of a Spiking Neural Network Hardware Accelerator[END_REF]. It is thus crucial to determine the reliability of ML applications implemented leveraging on emerging computing paradigms, especially when they are deployed in safety-critical and mission-critical applications, such as robotics, aerospace, smart healthcare, and autonomous driving.

This paper presents the state of the art of custom hardware architectures for ML. In particular, we will focus on two well known Deep Neural Network (DNN) types, the Spiking and the Convolutional Neural Network. For each type of DNN, we analyze the best practice related to their dependability assessment. Section II presents the main concepts of Artificial Intelligence focusing on Spiking and Convolutional Neural Networks, while the Section III is devoted to the state of 978-1-6654-9431-1/22/$31.00 ©2022 IEEE the art of hardware architectures specific to DNN. Section IV presents the methodologies to asses the robustness of DNN. Section V discusses the main challenges and opportunities about the deployment of DNN for safety-critical applications.

II. BACKGROUND

Artificial intelligence (AI) is indeed a vast scientific field including several disciplines: from biology to computer science. We focus on the computer engineering aspects of AI, in particular the hardware architectures designed to accelerate the inference of AI applications. Let us first present a brief taxonomy of AI adapted from [START_REF] Abiodun | State-of-the-art in artificial neural network applications: A survey[END_REF]:

• Artificial Intelligence (AI): is the superset of methodologies devoted to the simulation of human intelligence processes by machines;

A. Convolutional Neural Network (CNNs)

A CNN [START_REF] Lecun | Backpropagation applied to handwritten zip code recognition[END_REF] is a Deep Learning model generally employed in image processing and computer vision to carry out a high-end task, such as item classification, object detection and image segmentation. As shown in Figure 1, a CNN is internally organized in a sequence of layers, each one processing multidimensional data, dubbed tensors, by means of a number of operators. A tensor is of a multi-dimensional stack of bi-dimensional value matrices, dubbed feature maps, that generate a multidimensional grid. The most employed operators can be grouped into the following classes:

• Convolution, used to extract features from the input by inferring the appropriate weights, and thus to "learn"; • Batch normalization, used to fix the data distribution, speeding up the learning process;

• Activation function, used to mimic the biological activation of neurons by means of a mathematical function.

Common examples of activation functions are the Sigmoid, the softmax and the Rectified Linear Unit (ReLU); • Max-pooling (and other similar operators for dimensionality reduction), used to reduce the size of the tensor thus increasing the degree of generalization; • Element-wise operators used to carry out mathematical operations or single-element manipulation, such as addition, multiplication, exponent and bias addition. The operators in a CNN are usually organized in a sequence of layers devoted to the feature learning: each operator takes a tensor in input and produces a tensor in output (as shown in the example in left-hand side of Figure 1). The output of the feature learning is a tensor as well; when the goal of the CNN is image segmentation or object identification, such output tensor is considered as the output of the CNN itself. On the other hand, when the CNN is used for classification purposes, such final tensor is first flattened and then fed into a fully-connected Neural Network and a softmax operator that produce the probability values representing the likelihood of the identified object to be classified according to a set of predefined classes (as shown in right-hand side of Figure 1).

Given the complexity of designing CNNs, a number of frameworks, such as TensorFlow [START_REF] Abadi | TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems[END_REF], Caffe [START_REF] Jia | Caffe: Convolutional Architecture for Fast Feature Embedding[END_REF], Py-Torch [START_REF] Paszke | PyTorch: An Imperative Style, High-Performance Deep Learning Library[END_REF] and Keras [START_REF] Chollet | Keras[END_REF], has been proposed. These design frameworks provide general and extensible programming interfaces to specify the structure of a CNN in terms of its dataflow and employed operators. Moreover, these frameworks provide tools to automatically train the model and an automated backend support to target several processing devices such as CPUs or Graphic Processing Units (GPUs) to optimize performance.

B. Spiking Neural Network (SNNs)

Artificial neural networks, inspired by the computing capabilities of the biological brain, have been and still are intensely researched. Several electronic substrates are currently studied that offer interesting characteristics to achieve the promises of energy efficiency of the biological model and the technological maturity and the programming capacities expected by the applications. The ambition of neuromorphic chips is to get closer to brain-inspired neuron and synapse computation models. Spiking Neural Networks (SNN) [START_REF] Maass | Networks of spiking neurons: The third generation of neural network models[END_REF] are an important class of bio-inspired computing paradigms offering promising solutions for on-chip cognitive applications. The SNNs incorporate the concept of time in their operation models and they process data encoded in spikes.

Due to their biological plausibility and promise of low power operation, the SNNs have been widely studied both as a new paradigm for neuromorphic computing as well as a replacement for common DNNs. Irrespective of their use, all SNNs require spike-coded signals and spiking neurons.

The literature proposes various methods for numerical-tospike conversion [START_REF] Kumar | Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding[END_REF], [START_REF] Van Rullen | Rate coding versus temporal order coding: what the retinal ganglion cells tell the visual cortex[END_REF] such as, for instance, the ratecoding which produces a train of spikes, by encoding the numerical value as spike frequency. Since in bio-systems the temporal placement of spikes appears random, a Poisson distribution is usually applied to rate-coding [START_REF] Peter | Unsupervised learning of digit recognition using spike-timing-dependent plasticity[END_REF]. An other example of numerical-to-spike conversions is the time-coding (also known as latency-coding or time-to-first-spike) [START_REF] Saeed | Stdpbased spiking deep convolutional neural networks for object recognition[END_REF], [START_REF] Rueckauer | Conversion of analog to spiking neural networks using sparse temporal coding[END_REF] where the numerical value is encoded as the delay of a single spike (inversely proportional to the input amplitude).

Different spiking neuron models such as the integrateand-fire spike response, or Hodgkin-Huxley have been proposed [START_REF] Pfeil | Deep learning with spiking neurons: Opportunities and challenges[END_REF]. Typically they integrate currents from arriving spikes and generate new spikes whenever some threshold is crossed. The neuron model must be computationally simple and capable of producing firing patterns. The Hodgkin-Huxley-type models is bio-plausible but computationally prohibitive, while the integrate-and-fire model is computationally effective, but simple and less bio-plausible. When SNN is used to emulate the nervous system, Hodgkin-Huxleytype neurons are preferred, while for machine learning applications, SNNs are using (leaky) integrate and fire neurons.

As previously mentioned, when seeking for power-efficient artificial neural networks, researchers have started considering the possibility of using SNNs in machine learning applications to replace the formal DNNs. In this context, the SNN is mainly used for inference, with the training being done offline (as is the case for formal DNNs). Several training techniqes for SNNs have been proposed, the most common one is the the conversion method trains. The conversion method trains a formal neural network by traditional means (such as backpropagation for instance) and then maps the trained network on SNN [START_REF] Bodo Rueckauer | Conversion of continuous-valued deep networks to efficient event-driven networks for image classification[END_REF] by transcoding the synaptic weights taking into account the numerical-to-spike conversion used by the SNN and the neuron model. Another solution is to apply backpropagation (BP) directly to train SNNs. However, the BP process needs all operations through a network to be derivable, while the spiking neuron activation function is usually not. This problem can be solved by introducing a surrogate derivable parameter [START_REF] Jun | Training deep spiking neural networks using backpropagation[END_REF], [START_REF] Neftci | Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks[END_REF]. However, the networks trained by BP are implemented in hardware only for inference, since a BP learning is prohibitively expensive in hardware. To bring the training closer to the biology, and find means of training the SNN directly on hardware, the Spike-Timing-Dependent-Plasticity (STDP) rule proposed [START_REF] Natalia Caporale | Spike timing-dependent plasticity: a hebbian learning rule[END_REF] is being considered for low power applications. The STDP is a local learning rule, which updates the synaptic weight as a function of the delay before the input and output spike. This learning rule represents a relation of temporal causality between the input and the output spikes of any given neuron. STDP has been used in unsupervised learning of single layer SNNs [START_REF] Peter | Unsupervised learning of digit recognition using spike-timing-dependent plasticity[END_REF] or several layers SNNs [START_REF] Saeed | Stdpbased spiking deep convolutional neural networks for object recognition[END_REF]. It has also been used in supervised learning by adapting the backpropagation to STDP [START_REF]Deep learning in spiking neural networks[END_REF].

III. HARDWARE ARCHITECTURES FOR MACHINE LEARNING A. CNN

CNNs were initially implemented in software and executed on general purpose CPUs. In order to accelerate their bandwith, GPU-based implementations have been proposed [START_REF] Guzhva | Multifold acceleration of neural network computations using gpu[END_REF] where specific kernels (i.e., portions of the code) were deployed to the GPU.

The first proposed CNN-specific hardware accelerators were implemented as ASICs [START_REF] Chen | Dadiannao: A machine-learning supercomputer[END_REF]- [START_REF] Jouppi | In-datacenter performance analysis of a tensor processing unit[END_REF], and they achieved orders of magnitude improvements in energy efficiency compared to GPUs. This gain nevertheless comes at the expense of flexibility, with the design cost being very high. FPGAs, on the other hand, provide a good balance between flexibility, design cost, and performance [START_REF] Guo | Angel-eye: A complete design flow for mapping cnn onto customized hardware[END_REF], [START_REF] Reddy | Dlau: A scalable deep learning accelerator unit on fpga[END_REF].

Independently of the target (ASIC or FPGA), CNN-specific hardware accelerators adopt the same strategy of maximizing data reuse, an element that has been extensively studied by Chen & al. in [START_REF]Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks[END_REF]. The main architectures adopted by reconfigurable accelerators such as FPGAs is a dedicated grid of Processing Element (PE) [START_REF]Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks[END_REF], while the main architecture adopted by ASICs are based on more generic systolic arrays [START_REF] Jouppi | In-datacenter performance analysis of a tensor processing unit[END_REF]. This is mainly because a systolic array is more flexible once designed and can efficiently process matrix products, while a PE array requires tuning some parameters for efficiently execute a DNN (like the number of PEs and the size of the memory bus), making them more suitable for re-configurable accelerators.

B. SNN

As introduced in Section II-B there are issues to consider when looking for the implementation of an SNN, such as the numerical-to-spike coding, the neuron model, and, most importantly the learning rule. There are a plethora of proposals for off-line and on-line learning in SNNs, which opens up the research community to look for hardware solutions for their implementations. In this field, the research has not reached the industry-grade maturity levels and it is mostly developed at academic levels or on industrial prototypes. Today, there are several brain-inspired chips able to simulate numerous spiking neurons to investigate new kinds of computer architectures (SyNAPSE [START_REF] Cassidy | Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores[END_REF], TrueNorth [START_REF] Merolla | A million spiking-neuron integrated circuit with a scalable communication network and interface[END_REF], DYNAPs [START_REF] Moradi | A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (dynaps)[END_REF], Loihi [START_REF] Davies | Loihi: A neuromorphic manycore processor with onchip learning[END_REF], and Braindrop [START_REF] Neckar | Braindrop: A mixed-signal neuromorphic architecture with a dynamical systems-based programming model[END_REF]), or to speed-up neuroscience simulations through the Human Brain Project (SpiNNaker [START_REF] Jin | Modeling spiking neural networks on spinnaker[END_REF] and BrainScaleS [START_REF]Structural plasticity on an accelerated analog neuromorphic hardware system[END_REF]). These solutions are mainly dedicated to perform inference tasks. They are designed with distinct technologies, and their working principles and capabilities all differ. Loihi and SpiNNaker are using fully digital, corebased designs, while Braindrop and BrainScaleS are based on mixed or fully analog designs. The SpiNNaker is a many core architecture with optimized spike communication network and programmable local learning, while the BrainScaleS is build with analog neural cores and digital spike communication and it is capable of programmable local learning. Digital neuromorphic prototypes, such as Loihi and TrueNorth deploy a fully digital circuit-based strategy for the neuron and synaptic model parameters and also for learning algorithms, providing a larger flexibility of network configuration. Many academic solutions focus on hybrid and heterogeneous architectures where hardware architectures operate on spike coding (rate, frequency, time) to reach better energy efficiency [START_REF] Khacef | Confronting machine-learning with neuroscience for neuromorphic architectures design[END_REF] and to allow local learning rules thanks to the bio-inspired Spike Time Dependent Plasticity (STDP). Proposed solutions include explorations of state-of-the-art CMOS and emerging nanoelectronic technologies capable of mimicking the computational primitives of spiking neural networks. In this context, the most significant improvements come from the utilization of memristive devices to emulate the synaptic plasticity and facilitate local learning [START_REF] Jo | Nanoscale memristor device as synapse in neuromorphic systems[END_REF] [START_REF] Sengupta | Magnetic tunnel junction mimics stochastic cortical spiking neurons[END_REF].

IV. TOOLS AND METHODOLOGIES FOR ROBUSTNESS ANALYSIS

A. CNN

There is a large literature related to the analysis of the reliability of CNNs, as surveyed in [START_REF] Ibrahim | Soft errors in DNN accelerators: A comprehensive review[END_REF]. Several papers (e.g. [START_REF] Bosio | A Reliability Analysis of a Deep Neural Network[END_REF], [START_REF] Arechiga | The Robustness of Modern Deep Learning Architectures against Single Event Upset Errors[END_REF]) analyzed how faults corrupting the weights of the convolutional layers affect the final output of the CNN. Other works (e.g. [START_REF] Reagen | Ares: A framework for quantifying the resilience of deep neural networks[END_REF], [START_REF] Li | Understanding Error Propagation in Deep Learning Neural Network (DNN) Accelerators and Applications[END_REF]) presented approaches that trace the propagation of errors through the layers of the CNN to analyze how the application is able to mask them and how the propagated errors affect the produced output. All these works consider faults corrupting both the memory storing the weights and the internal registers of the underlying processing platform. Finally, in [START_REF] Fernandes | Evaluation of Histogram of Oriented Gradients Soft Errors Criticality for Automotive Applications[END_REF] the effects of faults are analysed by observing how they affect the precision and recall of the considered object detection application.

Commonly, CNNs are accelerate by exploiting GPUs, therefore tools and methodologies for fault injection in such technology may be exploited to analyse the reliability of CNNs themselves. Several Fault Injection (FI) tools have been proposed to emulate faults in GPUs. GPU-Qin [START_REF] Fang | GPU-Qin: A methodology for evaluating the error resilience of GPGPU applications[END_REF] exploits the CUDA-GDB debugger for NVIDIA devices to inject single bit-flips in the registers exposed by the Instruction Set Architecture (ISA); the solution is quite sophisticated and presents a 100x slowdown w.r.t. nominal execution. A similar debugging-based approach is used by CAROL-FI [START_REF] Oliveira | Increasing the Efficiency and Efficacy of Selective-Hardening for Parallel Applications[END_REF] which acts at the source code-level to inject both bit-flips and random values. CAROL-FI presents less than a 5x execution time degradation and the benefit of performing both the injection and the error propagation at the same level of the source code. However, the approach limits the injection sites to the variables. An alternative approach is adopted in SASSIFI [START_REF] Hari | Sassifi: An architecture-level fault injection tool for GPU application resilience evaluation[END_REF] and LLFI-GPU [START_REF] Li | Understanding error propagation in GPGPU applications[END_REF]: the source code is instrumented for FI before being executed on the GPU. SASSIFI, proposed by NVIDIA, is able to corrupt all registers of the ISA by means of several injection modes, with a 5x slowdown as reported in [START_REF] Oliveira | Increasing the Efficiency and Efficacy of Selective-Hardening for Parallel Applications[END_REF]. LLFI-GPU uses a similar approach while acting at instruction level, claiming a speedup w.r.t. GPU-Qin about 42x on the execution time and similar benefits as CAROL-FI in terms of the analysis of the effects of the errors. Finally, in other works [START_REF] Bosio | A Reliability Analysis of a Deep Neural Network[END_REF], [START_REF] Li | Understanding Error Propagation in Deep Learning Neural Network (DNN) Accelerators and Applications[END_REF], FI is performed again at the software level by manually modifying the application code to integrate the corruption facilities. All these tools are based on complex modification and recompilationof the target code to enable FI, thus leading to considerable performance degradation. More recently, the new NVIDIA tool, called NVBitFI, has been proposed [START_REF] Tsai | NVBitFI: Dynamic Fault Injection for GPUs[END_REF]. This tool overcomes several limitations of previous tools by performing a dynamic and selective code instrumentation. On the other hand, implementation issues still persist when working with external libraries.

Working at a higher abstraction level than the architecturelevel fault injection would be beneficial for two main reasons: i) to dominate the complexity of CNN applications, and ii) to speed-up and simplify experiments set-up and execution. Several simulation-based approaches have been proposed where errors are injected during the execution of the considered CNN. A representative example of error simulation environment for Machine Learning (ML) is TensorFI [START_REF] Chen | TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications[END_REF] which is integrated in the commonly employed TensorFlow framework. TensorFI works at the level of the application dataflow and it injects errors by manipulating the output of the operators, thus emulating the effects of faults affecting the ML operators execution. A similar strategy but integrated in other ML frameworks is the one presented in [START_REF] Reagen | Ares: A framework for quantifying the resilience of deep neural networks[END_REF], [START_REF] Mahmoud | PyTorchFI: A Runtime Perturbation Tool for DNNs[END_REF], [START_REF] Neggaz | Are CNNs Reliable Enough for Critical Applications? An Exploratory Study[END_REF], integrated in the Keras, Pytorch and Caffe frameworks, respectively. Finally, a cross-layer approach, implemented in PyTorch, where the CNN is executed via software and the fault injection is carried out by switching down to the Hardware Description Language (HDL) description of the processing unit has been proposed in [START_REF] Ruospo | A Pipelined Multi-Level Fault Injector for Deep Neural Networks[END_REF].

The most recent contributions, from which our proposal moves the steps, are Fidelity [START_REF] He | FIdelity: Efficient Resilience Analysis Framework for Deep Learning Accelerators[END_REF] and BinFi [START_REF] Chen | BinFI: An Efficient Fault Injector for Safety-Critical Machine Learning Systems[END_REF]. The former defines a set of error models adherent to what is observed after FI experiments and it then analyses the effects of the faults in terms of the Architectural Vulnerability Factor (AVF). On the other hand, the latter identifies the safety-critical bits in the considered ML application, relying on TensorFI to carry out fault injection in the operators. The effects of the faults on the produced outputs are then analyzed w.r.t. the application, to determine whether they would deeply affect its behavior, or not. Our proposal integrates and merges these two strategies: indeed, we first extract error models, like Fidelity does, and we then perform a classification of the effects of the errors on the produced outputs, like BinFI. Indeed, we believe that our proposal fills the gap between FI, Fuctional Error Simulation (FES) and an application-related resiliency classification.

1) Cross-layer CNN Reliability Analysis: Our proposal is a cross-layer framework for the reliability analysis of CNNs that relies on an error simulation engine. Such engine in turn exploits a set of validated error models previously extracted from a detailed fault injection campaign performed on the operators that compose the considered CNN. Therefore, our proposal bridges the gap between fault injection and error simulation, exploiting the advantages of both approaches.

The first step of the flow is dubbed the "Operators Extractor" that extracts all the operators from the considered CNN. For each identified operator a standalone test program, namely "Experiment Instance", is generated in Caffe. The program transmits input tensors to the GPU, executes the operator on that device, and retrieves the output tensor. These programs represent the code that will be executed in the FI experiments.

The subsequent step of the flow is the actual fault injection. Since we target CNNs accelerated on GPUs, FI environments specifically meant for this processing platforms, e.g., SASSIFI or NVBitFI, are suitable for our flow. Due to the complexity of modern GPUs and to the size of the their memory, an exhaustive FI campaign is unfeasible. Nevertheless, it is possible to exploit the extreme regularity of the Single Instruction Multiple Data (SIMD) architecture to reduce the number of required experiments. Indeed, several threads simultaneously executing the same code, elaborate on different bunch of data of the same type and are run on different instances of the same hardware resources. Thus, the output corruption patterns observed when injecting faults during the execution of one of the threads will be representative of the effects of faults in all the threads. To make our FI experiments as general as possible, in each experiment we change the input of the operator and we randomly chose the fault-injected thread.

The outputs of the FI campaign are the collected and compared against the expected output. The correct output are discarded while the erroneous ones are further inspected to identify recurrent error patterns by semi-automated scripts. The goal of this activity is the identification of recurrent corruption patterns in the output tensors, to be exploited for the definition of functional error models. Every time a corruption pattern is statistically relevant and it can be implemented by means of an algorithm, it leads to the definition of an error model. We may identify and classify the corruption patterns according to:

• the cardinality (i.e. the number) of erroneous values in the output tensor, • the domains the erroneous values belong to, and • the spatial distribution of the erroneous values. These three aspects (in particular the last one) are highly influenced by the characteristics of the CNN application, the multidimensionality of the processed data, and the SIMD architecture of the GPU. The result of this modeling activity is the description of the identified error models in terms of an algorithm to reproduce their effects (erroneous values cardinality and domains and spatial pattern of the erroneous values) and the occurrence probability. This detailed information is at the basis of the library of validated models to support the error simulation frameworks.

After a repository of error models has been identified, the actual error simulation can be carried out. More in details, we implemented the defined error models in an error simulation engine based on TensorFlow, being it much more popular and versatile than Caffe. However the error models are valid perse, and can be exploited in any framework. The error injection mechanism has been implemented by means of saboteurs, i.e., new operators inserted in the CNN graph intercepting the output tensor and corrupting it. The previously observed occurrence probabilities of the various error models are used to randomly select the error to inject. Figure 3 depicts the overall scheme on a toy example.

B. SNN

When considering a SNN at a high level of abstraction and without considering the on-line learning (i.e., only during the inference mode), techniques for robustness and reliability assessment developed for CNNs and DNNs can be easily adapted to SNNs, since they work at functional level. Nevertheless, when considering the online learning aspect, it is necessary to develop new methodologies dedicated to the particular properties of the SNN, as the ones proposed in [START_REF] Vatajelu | Special session: Reliability of hardwareimplemented spiking neural networks (snn)[END_REF] and [START_REF] Vatajelu | Fully-connected single-layer stt-mtj-based spiking neural network under process variability[END_REF]. In addition, when the hardware implementations are based on memristive technologies, new defects and fault models have to be considered for an accurate robustness and reliability estimation. Due to the aggressive technology scaling and the introduction of new steps at back-end-ofline for the fabrication of the memristive element, it suffers from fabrication-induced variability. Moreover, due to intrinsic properties of the memristive technologies, they are more susceptible to variations and defects, so there is a need for high quality test and fault tolerance. In addition, because of the different operation modes (analog storage and computing for local learning) compared to traditional digital memories, they require fundamentally new testing schemes. A hardware implementation of an SNN requites architectural colocalization of the processing and memory (non-Von Neumann architecture). The circuits solutions used to implement silicon neurons are application depended, but the vast majority are built with a temporal integration block, a spike generation block, a refractory period mechanism, and a spike adaptation block. Synapses are required to exhibit plasticity (i.e., modulation in their efficacy) and to support online learning algorithms, that manifest in changes in their strengths. Emerging memory devices can be used as synaptic elements thanks to their tunable conductivity, compatibility with advanced CMOS fabrication process, low power consumption, non-volatility and scalability. The synaptic conductance modulation can be emulated using: (i) the analog approach (cumulative decrease and increase of resistance), where multiple resistance states emulate long-term potentiation and depression; or (ii) the binary approach, uses two distinct resistance states per device associated with a probabilistic programming scheme. In order to rigurously analyse the faults that can occur in a Spiking Neural Network (SNN) and the assessment of fault-tolerance quality of such a network, we have developed pertinent fault models and methodologies for conducting a fault injection campaign and have identified scenarios of faulty operation happening before and after the STDP learning [START_REF] Vatajelu | Special session: Reliability of hardwareimplemented spiking neural networks (snn)[END_REF].

We propose the fault analysis to be performed by two approaches: top-down and bottom-up. In the top-down approach, the correctness of the SNN algorithm is evaluated under the effect of different faults, and the results to be validated by functional evaluation of the SNN architecture. Starting from the behavioral model of the SNN under study, we investigate how the network behaves under different issues such as: defective or dead neuron, defective or dead synapse. We evaluate the functional accuracy of the SNN during inference and learning. The obtained results will answer questions such as: which is more detrimental to the functionality of a Neural Network (NN): defective neuron or defective synapse? How many of these critical components have to fail such that the entire network fails? Which is the more critical defect location: inner or outer layer of a NN? In which state does a certain defect matter most: learning or inference? In the bottomup approach we evaluate the effect of fabrication-induced defects and variability on the operation of the neuron and synapse and a fault modeling campaign is conducted. The faults are injected at system level and the robustness of the SNN is evaluated. This approach requires a complete analysis of possible defects, defect mapping and defect modeling. It is based on a complete analysis of circuit failure modes and a set of technology-dependent fault models, together with a comprehensive evaluation of the application-dependent spatial and temporal dependencies of the circuit failures the functional modules of the SNN: the neuron and synapse. With this analysis we can develop appropriate fault models based on the technology-specific failure mechanisms and defects as well as the SNN functional module and full network.

V. CHALLENGES AND OPPORTUNITIES

The research in this field has received a lot of attention, to offer initial solutions for allowing the adoption of ML in domains where robustness is paramount. However, the complexity of the context motivates towards the development and adoption of solutions that are specifically targeted for this domain, also considering the deep relation between the appli-cation and its data. As reported, a lot of effort is being devoted to design and implement hardware accelerators optimized for executing ML-based applications, creating new challenges and opportunities to explore dependability-related solutions tailored for these new building blocks, to enable their adoption in critical application environments. In all these contexts, considering the complexity of hardware, application and data, it will be pivotal to identify the most appropriate abstraction level for both the robustness analysis and its enforcement, supporting the designer in the achievement of a robust system, from the performance and dependability perspectives.

Fig. 1 .

 1 Fig. 1. The typical topology of a Convolutional Neural Network (CNN).

Fig. 2 .

 2 Fig. 2. The designed Fault Injection flow.

Fig. 3 .

 3 Fig. 3. Error simulation exploiting sabouters.

ACKNOWLEDGMENT

This work has been co-founded by the ANR project RE-TRUSTING, ANR-21-CE24-0015 and by the ANR project EMINENT, ANR-19-CE24-0001.


Josef Houser

Jan Komarek

Gianluca Cioci

Annabelle Varrot

Anne Imberty

Michaela Wimmerova

Structural insights into Aspergillus fumigatus lectin specificity -AFL binding sites are functionally non-equivalent

Keywords: Aspergillus fumigatus, lectin, SPR, protein-saccharide complex, pathogen

The interaction between Aspergillus fumigatus lectin AFL and biologically relevant oligosaccharides was examined. The functional data were combined with detailed structures of the proteinsaccharide complexes to understand the ligand binding at the atomic level.

Introduction

Saccharides play crucial roles not only in energy metabolism and cell structure, but also in signalling, protein modification or host-pathogen interactions. Various techniques for research in glycosciences have been developed so far (Mechref and Novotny, 2002;Meisen et al., 2011;Tissot et al., 2009), including mass spectrometry, immunological methods and the application of carbohydrate-specific hydrolases, among others. Lectins, ubiquitous carbohydrate-binding proteins, are also widely used for detecting and identifying glycans and glycosylation modifications. They are essential for enzymelinked lectin assay (ELLA), lectin blotting or sugar-specific labelling (Tissot et al., 2009). Several structural lectin families have been described (http://www.glyco3d.cermav.cnrs.fr). Most of them share the phenomenon of multivalency, achieved by oligomerization and/or the presence of tandem repeats. One example that combines both approaches is the well known lectin from Aleuria aurantia (AAL). The protein was described in 1980 (Kochibe and Furukawa, 1980) and its structure was solved twenty years later (Fujihashi et al., 2003;Wimmerova et al., 2003). This lectin forms homodimers, each monomer consisting of six tandem repeats folded in a six-bladed β-propeller. Each monomer bears five L-fucose specific binding sites, sharing the overall binding pattern with the occurrence of hydrogen bonds to Arg and Glu/Gln residues, whereas a neighboring Trp/Tyr residue mediates a CH-π interaction with the apolar surface of fucose. The five sites are similar but non-identical. The average affinity for fucose is in the micromolar range (Wimmerova et al., 2003) but it has been proposed that some of the individual sites display much higher affinities (Olausson et al., 2008). Several efforts have been made to understand the structure/function relationship at the binding site level, in order to correlate each binding site composition with its specificity and affinity (Amano et al., 2004;Olausson et al., 2011;Romano et al., 2011). The studies of bacterial structural homologues (Audfray et al., 2011;Kostlanova et al., 2005) are beneficial but, as these lectins are much smaller and bear only two types of binding sites, they cannot completely explain the observed variability in AAL.

Due to a rapid increase in the number of gene sequences available over the last decade, several homologues of the AAL protein have been identified, including lectins from Aspergillus fumigatus and A. oryzae (Ishida et al., 2002) . The crystal structure of AFL from A. fumigatus was recently solved, as the first from a pathogenic mold (Houser et al., 2013). AFL has also a dimeric arrangement of six-bladed β-propellers as AAL, but it differs in the number of active binding sites, with six of them per monomer (Fig. S1). The differences in the amino acid composition of individual binding sites make this protein an ideal target for detailed studies focused on the relationship between the structure and function of the lectin binding site. In addition, while A. aurantia is a harmless fungus, A. fumigatus acts as a worldwide allergen and is also a huge threat to immunocompromised patients, with a high mortality rate (Galimberti et al., 2012). Recently, the presence of AFL in Aspergillus conidia was demonstrated (Houser et al., 2013;Kuboi et al., 2013), with a high probability to be localized at their surface. AFL recognizes fucose, which is widely present on oligosaccharide epitopes on human tissues (Becker & Lowe, 2003) and was proofed to be used by many pathogens for attachment (Imberty & Varrot, 2008). Furthermore, an immunostimulating effect of AFL on human lung epithelial cells was observed. These findings make AFL a promising target for aspergillosis diagnosis and/or treatment. Better understanding of AFL binding properties can thus be useful in both biotechnology and medicine.

In this paper we describe the crystal structures of several complexes of AFL with various human oligosaccharide receptors. Together with surface plasmon resonance measurements, the link between structure and binding properties among AAL family lectins is described.

Material and Methods

Materials

Methyl-α,L-fucopyranoside was purchased from Interchim, Montluçon, France, L-Galactose and blood group H 2 type trisaccharide were purchased from Dextra Laboratories Ltd., Reading, UK. Other oligosaccharides were purchased from Carbohydrate Synthesis Ltd., Oxford, UK. Basic chemicals were purchased from Sigma-Aldrich, St.Louis, USA, Duchefa, Haarlem, Netherlands and Applichem, Darmstadt, Germany.

Protein expression and purification

The protein AFL was produced as described previously (Houser et al., 2013). Briefly, Escherichia coli BL21(DE3)Gold (Stratagene) cells bearing the pET29-afl vector were cultivated in standard low-salt LB medium with 50 µg ml -1 kanamycin. The induction by 1 mM isopropyl β-D-1-thiogalactopyranoside at 30°C for 3 hrs led to overproduction of AFL. Harvested cells were disintegrated in 20 mM Tris/HCl, pH 7.3 by sonication. AFL was isolated from the protein extract by affinity chromatography on a mannoseagarose resin (Sigma-Aldrich) using isocratic elution. Fractions containing pure AFL protein were pooled, desalted by dialysis against ultrapure water and used for further studies or lyophilized.

Surface plasmon resonance

The SPR measurements were performed using a Biacore 3000 (GE Healthcare) instrument at 25°C with running buffer (10 mM HEPES, 150 mM NaCl, 0.005% (v/v) Tween 20, pH 7.4) at a flow rate of 20 µl min -1 . To determine the binding preferences, the direct binding set-up was chosen. The carboxymethyldextran surface of the CM5 chip was activated with EDC (N-ethyl-N-[3dimethylaminopropyl]carbodiimide)/NHS (Nhydroxysuccinimide) solution using the standard manufacturer's protocol. Three different concentrations of AFL (1, 5 and 50 µg ml -1 ) in 5 mM MES/NaOH, pH 6.0 buffer were injected at a flow rate of 5 µl min -1 for 10 minutes into three flow channels. The relative responses of immobilized AFL were 800, 4500 and 11900 RU, respectively. Finally, the sensor surface was blocked with 1 M ethanolamine. The blank channel was treated similarly, except for the lectin injection. 80 µl of carbohydrate solutions with increasing concentrations in the running buffer were injected into the flow cells using Inject mode. The equilibrium response (after subtracting the response of the reference surface) of each experiment was used to create analyte binding curves. Using the software Origin 7.0 (OriginLab Corp.), the data obtained for a particular ligand (all applicable runs on all channels) were simultaneously fitted applying common K d for all datasets. The one-site and two-site steady-state affinity models were used, based on equations: R = R max c(ligand

) / [K d + c(ligand)] and R = R max1 c(ligand) / [K d1 + c(ligand)] + R max2 c(ligand) / [K d2 + c(ligand)],
respectively, where R is the detector response, R max is the maximal response, K d is the dissociation constant and c(ligand) is the actual concentration of injected ligand. The R max1 to R max2 ratio in two-site model was not restricted in order to not influence the values of K d1 and K d2 . Depending on the channel, the experimental R max values typically reached 10 -40 % of the theoretical R max .

Crystallization and data collection

Lyophilized protein was dissolved in ultrapure water and subsequently used for crystallization experiments using the hanging drop method. The initial crystallization conditions were the same as described previously (Houser et al., 2013), the conditions were optimized for each ligand used. The final crystals were obtained from a set of drops under the following conditions: 2 µl of precipitant (200 mM CaCl 2 , 25% PEG 4K and 100 mM Tris, pH 8.5) was mixed with 2 µl of protein solution containing 7-15 mg ml -1 AFL and a dissolved ligand (Fig. S2). The ligand concentrations used were as follows: blood group A trisaccharide (BGA, 2 mM), Lewis Y tetrasaccharide (Le Y , 1 mM), α,L-fucosyl(1-6)-Nacetyl-D-glucosamine (1 mM), L-galactose (L-Gal, 1 mM), methyl-β,L-fucoside (1 mM) or an equimolar mixture (1 mM each) of αFuc(1-2)Gal, αFuc(1-3)GlcNAc, αFuc(1-4)GlcNAc and αFuc(1-6)GlcNAc, respectively. Ligand-free AFL was treated the same way omitting the addition of the ligand. Plates were incubated at 17°C until crystals were formed.

Crystals were cryo-cooled at 100K after soaking for the shortest possible time in reservoir solution supplemented with 15% (v/v) glycerol (AFL/BGA), 20% (v/v) glycerol (AFL/Le Y , AFL/Fucα1-6GlcNAc, AFL/βMeFuc) or 40% (v/v) PEG400 (ligand-free AFL, AFL/L-Gal, AFL/disaccharide mixture), respectively. The Xray diffraction experiments were performed at BESSY II in Berlin, Germany on the 14.1 beamline (ligand-free AFL) and 14.2 beamline (AFL/βMeFuc) (Mueller et al., 2012) or at ESRF (European Synchrotron Radiation Facility) in Grenoble, France on the ID23-1 or ID23-2 beamlines (all other complexes) (Nurizzo et al., 2006).

Structure determination

Collected diffraction images were processed using iMosflm 7 (Leslie & Powell, 2007, Battye et al., 2011) or XDS (Kabsch, 2010), respectively, and converted to structure factors using the program package CCP4 version 6.1 (Winn et al., 2011) with 5% of data reserved for R free calculation. The structures of AFL complexes were determined using the molecular replacement method with Molrep 10.2 (Vagin and Teplyakov, 2010) or Phaser 2.5 (McCoy et al., 2007), respectively, with the structure of AFL/MeSeFuc (4agi) (Houser et al., 2013) without the ligands as the starting model. Refinement of the molecule was performed using Refmac5 (Murshudov et al., 1997) alternated with manual model building in Coot 0.7 (Emsley et al., 2010). Sugar residues and other compounds present were placed manually using Coot. Water molecules were added by Coot and checked manually. The addition of alternative conformations where necessary resulted in final structures that were validated by the ADIT validation server (http://rcsb.pdb.org) and deposited in the PDB Database with accession numbers 4ah5 (AFL/Le Y ), 4ah4 (AFL/BGA), 4agt (AFL/αFuc(1-6)GlcNAc), 4uq7 (AFL/L-Gal), 4c1y (AFL/βMeFuc) and 4aha (AFL with equimolar mixture of αFuc(1-2)Gal, αFuc(1-3)GlcNAc, αFuc(1-4)GlcNAc and αFuc(1-6)GlcNAc).

Analytical ultracentrifugation

Sedimentation analysis was performed using a ProteomeLab XL-A analytical ultracentrifuge (Beckman Coulter) equipped with an An-60 Ti rotor. Before analysis, purified AFL was brought into the experimental buffer by dialysis and the dialysate was used as an optical reference. Sedimentation velocity experiments were performed with 0.2 mg ml -1 AFL. Three different buffers were tested for pH dependence: 20 mM sodium citrate buffer, 150 mM NaCl, pH 4.0; 20 mM Tris/HCl, 150 mM NaCl, pH 7.3 and 20 mM sodium carbonate buffer, 150 mM NaCl, pH 10.0. The influence of ionic strength was analyzed by varying the concentration of NaCl (0 -1000 mM) in the 20mM Tris/HCl buffer, pH 7.3. Sedimentation velocity experiments were conducted in a standard double-sector centerpiece cell loaded with 420 μl of protein sample and 430 μl of reference solution. Data were collected using absorbance optics at 25° C and a rotor speed of 40,000 rpm. Scans were performed at 280 nm at 8 min intervals and 0.003 cm spatial resolution in continuous scan mode. The partial specific volumes of the protein together with solvent density and viscosity were calculated from the amino acid sequence and buffer composition, respectively, using the software Sednterp (http://bitcwiki.sr.unh.edu). The sedimentation profiles were analyzed with the program Sedfit 13.0 (Schuck, 2000). A continuous sizedistribution model for non-interacting discrete species was used to provide a distribution of apparent sedimentation coefficients.

Results

AFL-saccharide interaction

Surface plasmon resonance

The SPR technique was employed in order to determine the binding affinities of AFL towards various biologically relevant saccharides. Immobilization of the protein to the chip surface was chosen to avoid complex interpretation of interactions with a multivalent saccharidemodified surface. The data were processed using the software Origin 7.0. Using the one-site model, dissociation constants towards fucose and all fucosylated compounds (Table 1) were rather similar and close to 0.1 mM. Affinity for L- galactose was lower (K d =0.78 mM), indicating the importance of the C6 methyl group of L-fucose. However, as displayed in Fig. S3, the fit between the experimental data and one-site model of binding is not always good, and a more complex model should been envisaged. In theory, a sixbinding sites model could be used, but this would result in a very large number of variables that could lead to over-fitting problems. Therefore, the use of a two-sites model is rationalized by the occurrence of two sets of binding sites, depending on the presence of a Trp or a Tyr residue (Houser et al., 2013) as already described for the Aleuria aurantia lectin (Amano et al., 2004). This approach usually results in a better fit between the experimental data and model as displayed in Fig. S3, therefore validating the proposition of a second set of binding sites with higher affinities (K d of few M) for fucosylated compounds. The ratio between higher and lower affinity sites was usually close to 1:5 or 2:4, what suggests even more complex behaviour of the system. It was only with L-Gal and βMeFuc that the one-site model fitted the data better to the two-sites model.

AFL displays a slight preference towards the α anomeric fucoside, but the β fucoside is also recognized. L-Gal does not bind so efficiently, confirming the hydrophobic interaction due to the fucose methyl group close to the semi-conserved Leu/Ile/Met residues. The highest affinity is reached for the Lewis X and Lewis Y epitopes, but it could be concluded that AFL has a general fucose specificity, since all tested human fucosylated oligosaccharides are efficiently bound by the lectin.

Structural studies

Structure determination

The structures of sugar-free AFL and AFL cocrystallized with various ligands were solved by molecular replacement using the protein coordinate of chain A of the AFL/selenofucoside structure (4agi) as the search model. Ligand-free AFL crystallized in P2 1 space group, sugar complexes crystallized in the P1, P2 1 or P2 1 2 1 2 1 space groups (see Table 2

for details).

In all structures, the AFL protein adopts the 6bladed β-propeller fold previously determined for the MeSeFuc complex. No significant variations of the backbone conformation were observed between the chains within a particular structure or between the different structures, with rmsd varying from 0.10 to 0.33 Å when compared to the previously published structure (Houser et al., 2013). Two single point mutations L20S and R111C reported previously to be natural variation were clearly detected in all of the present complexes. Some cysteine residues in BGA, Le Y and L-Gal complexes were found to be oxidized, since no DTT or other reducing agents were used during crystallization. Neither cysteines nor their oxidized forms participate in ligand binding. In some complexes additional ions (sodium, chloride, zinc) and organic molecules (PEG) originating from crystallization conditions were detected. They also do not affect the ligand binding or oligomeric state of the protein.

Oligomeric state

AFL is a dimeric protein in solution and crystallizes as a dimer in all complexes, similarly to previously analyzed complex with methyl seleno-α,L-fucopyranoside (Houser et al., 2013). In all structures, the protein associates with the same dimeric interface that involves mainly side chains of Gln7, Tyr109, Asn134, Asn238 and Gln262 residues and a backbone of Asn235, Ser236 and Gly263 residues. One or two dimers are observed in the asymmetric unit depending on the space group (Table 2). Comparison of the AB dimers in the different crystal structures results in rmsd values of 0.20 to 0.24 Å. Comparison with the dimer formed by its structural homologue AAL, the AFL lectin exhibits significant differences. The amino acids involved in the dimeric interface either differ in their sequence or in their position, and that leads to changes in the respective orientation of the two monomers in both dimers (Fig. S4,S5).

The existence of dimeric AFL in solution at neutral pH was originally determined by analytical ultracentrifugation (Houser et al., 2013).

Additional AUC experiments at various pH values were performed to determine the stability of the AFL dimer in solution (Fig. S6). Acidic pH 4, which is reported to affect A. fumigatus' conidia binding (Tiralongo et al., 2009), does not affect the oligomeric form at all, hence there is probably no link between AFL oligomeric state and the inhibition of conidia binding. However, a shift to pH 10, which is beyond the typical pH for A. fumigatus growth (pH 2.1 -8.8) (Kwon-Chung & Sugui, 2013), causes a dissociation of 7% of AFL into monomers. The protein dimer was stable at neutral pH with various concentrations of NaCl (150 -1000 mM; data not shown), which corresponds to the results published previously for lower ionic strength bellow 300 mM NaCl (Houser et al., 2013).

AFL binding sites

AFL contains six binding sites per monomer, each located between two adjacent blades (Houser et al., 2013). According to the nomenclature used previously, site 1 is located between blades I and II and so on, finishing with site 6 between blade VI and blade I. Clear electron density corresponding to AFL ligands was observed in all structures, sometimes in all binding sites, and sometimes in only some of them (Fig. S7). The binding sites in all protein subunits of particular complex display similar behaviour unless stated otherwise.

The molecular structure of AFL co-crystallized with different oligosaccharides revealed only minor changes in side chain positions. The binding mode for the fucosyl part of the oligosaccharides is the same as that observed with MeSeFuc (Fig. S1). Only a minor shift of the fucose position is observed in some complexes with larger oligosaccharides. A summary of the ligands detected in the binding sites of each complex is given in Table 3. In several cases, only the fucose moiety could be identified in the electron density, which indicates a probable flexibility of the remaining part of the oligosaccharide. The AFL structure without any ligand reveals higher flexibility of particular amino acid side chains (especially Arg25 in site 1, Trp141 in site 2 and Trp299 in site5), while the overall shape of binding site is retained (Fig. S8).

All six binding sites share a typical binding motif. Conserved Arg and Glu/Gln residues establish polar interactions with the hydroxyl O-3, O-4 and O-5 of the bound fucose, a Trp/Tyr residue mediates a CH-π interaction with the apolar surface of fucose (C-3, C-4 and C-5). Additional hydrogen bonds may be formed between the saccharide and the other residues. These interactions are different for each binding site and for each ligand. Additional hydrophobic interactions are mainly present between C-6 of the fucose and non-polar amino acids in the binding site, including semi-conserved Leu/Ile/Met in the pocket at the bottom of the site.

AFL/L-Gal complex

AFL was co-crystallized with L-Gal in order to determine the role of the L-Fuc methyl group in sugar binding. The structure revealed electron density corresponding to L-Gal in four out of six binding sites for each monomer (Table 3). In sites 3, 6 and for three out of four chains in site 1, both α and β anomers were detected. Site 2 seems to be more specific for α, L-Gal, since the β anomer could only be seen in chain C. The orientation of L-Gal is identical to that observed for L-Fuc (Fig. 1). The additional OH group points outside the hydrophobic pocket and is stabilized by two bridging water molecules, mainly towards the side chains of conserved amino acids Arg and Tyr/Trp. In site 1, it is further stabilized by a hydrogen bond with a Leu69 mainchain O atom. The position of the amino acid side chains in the vicinity of the ligand are the same as in the complex with MeSeFuc. Only a Leu123 side chain changes its conformation slightly, so that it does not clash with the hydroxyl O6 of L-Gal. However, the displacement of the 120-123 loop observed in chains A and D is probably due to intermonomeric interactions in the crystal. Since no other structural changes were observed, the unfavorable accommodation of O6-L-Gal into the hydrophobic pocket in the binding site together with the restriction of O6 rotational movement (entropic penalty) are the probable causes of its lower affinity towards L-Gal, as determined by SPR.

AFL/βMeFuc complex

AFL is able to recognize βMeFuc, as was proved by glycan array (Houser et al., 2013) and SPR. To more deeply understand the observed difference between the binding of α-and β-linked fucoside, the structure of the AFL/βMeFuc complex was solved. Electron density corresponding to the ligand was detected in all four monomers in the asymmetric unit for sites 3 and 6 and in three out of four monomers in site 1 (Table 3). βMeFuc was also localized in site 5 of chain C, where it is stabilized by intermonomeric interactions enabled by crystal packing. All other sites remain unoccupied or the density for glycerol (cryoprotectant) was detected.

The sugar ligand is stabilized by a Tyr-mediated stacking interaction in sites 1, 3 and 6. This corresponds to a lower affinity as determined by SPR, where the one-site model also fit better than the two-site. The position of βMeFuc in the binding sites is identical to the αMeSeFuc complex, with no additional interaction between the methoxy-group and the protein. Since there is no sterical conflict between βMeFuc and the stacking Trp residue, it is possible that the side chain of Tyr95 and Tyr199 on the opposite side of the binding pocket causes the absence of βMeFuc in site 2 and 4, respectively (Fig. 2). Residues in the equivalent positions of occupied binding sites -Leu39 (site 1), Met140 (site 3) and Val298 (site 6) -do not interfere with βMeFuc, thus enabling its binding.

AFL/disaccharide complexes

The disaccharide αFuc(1-6)GlcNAc, which corresponds to core fucosylation on N-glycans, was co-crystallized with the lectin and the electron density of the fucosyl part was clearly seen in all six binding sites. Due to the inherent flexibility of the 1-6 linkage, the GlcNAc moiety was only visible in site 2 of chain A and site 5 of chain B, when stabilized by interactions with a neighboring molecule due to crystal packing. Only one direct interaction between GlcNAc and protein was observed, linking the O5 atom of GlcNAc and the Tyr95-OH in site 2 of chain A (Fig. 3A). Additional interactions are mediated by water bridges connecting O5 to the side chains of Arg73, Asp89 and Tyr95 and O7 to Asp89 and Thr92. In site 5 of chain B, the only interaction with the protein chain is mediated by a water molecule connecting O1 of αGlcNAc and Trp299-NE1. Some other interactions, either direct or mediated by the solvent, are observed with symmetric molecules, especially at the level of the N-acetyl group. Hence each AFL binding site is able to recognize fucose in the 1-6 linkage, but probably due to the flexibility of this linkage, the GlcNAc residue is mostly moving freely in the solvent and does not bring additional stability to the interaction.

Figure 3 (A) Binding site 2 of chain A of AFL/Fucα1-6GlcNAc complex. (B) Fucα1-4GlcNAc in binding site 4, chain B of AFL complex crystallized with mixture of fucosylated disaccharides. Hydrogen bonds linking a GlcNAc moiety to the protein side chains are highlighted in both panels. Color code for sugar-binding amino acids: greenfucose binding residues, cyan -direct interaction with GlcNAc, yellow -water mediated interaction with GlcNAc.

In order to determine the preferences of particular binding sites, AFL was also co-crystallized with an equimolar mixture of four fucosylated disaccharides: αFuc(1-2)Gal, αFuc(1-3)GlcNAc, αFuc(1-4)GlcNAc and αFuc(1-6)GlcNAc. In all six binding sites of both A and B chains, the electron density for the fucose moiety was clearly detected. In site 2 of chain A and sites 1, 2 and 4 of chain B, the density was clear enough to assign the second saccharide unit (Table 3). In all these cases, only αFuc(1-4)GlcNAc was observed. In addition to the conserved interactions in the fucose binding site, GlcNAc is stabilized by direct hydrogen bonds to Tyr88-OH (site 1) or Tyr168-OH, Tyr171-OH and Asp193-OD2 (site 4) and by water-mediated bridges (Fig. 3B). In the other sites the electron density for the second saccharide is not clear, indicating either a large flexibility of the disaccharide linkage (probable for αFuc(1-6)GlcNAc) or a statistical disorder (different oligosaccharides present in one binding site).

AFL/BGA complex

Clear electron density for the whole blood group A trisaccharide was observed in all sites except for site 3 of chain A and sites 2 and 3 of chain B. In addition to the conserved interaction with fucose, Tyr40 (chain B, Fig. 4D) and Tyr88 (both chains A and B, Fig. 4E) mediate hydrogen bonds to the Gal moiety in sites 6 and 1, respectively. The missing interaction of Gal moiety with Tyr40 in site 6 of chain A (Fig. 4C) is affected by intermolecular interaction as described below. Additional interactions occurred between GalNAc and Asp201 side chain (site 4) in chain B (Fig. 4B) or Asp253 side chain (site 5) in chain A (Fig. 4F). The ligands in sites 1, 2, 4 and 6 of chain A are further stabilized by intermolecular interactions in the crystal, however in sites 1 and 2 this does not seem to markedly influence the oligosaccharide conformation comparing it with the computed theoretical energy minimum for the free ligand (Imberty et al., 1995) (Fig. 5). The greatest difference is exhibited by the GalNAcα1-3Gal linkage for the ligand in site 4 of both chains (Fig. 5B), which could be due to a stabilizing interaction between GalNAc-OH6 and Asp201-OD2 (chain B, Fig. 4B), a water bridge to Lys196-NZ or crystal interactions (chain A, Fig. 4A). For site 6, the interactions of the ligand with the neighboring protein monomer in the crystal are of importance. In chain B, the Gal and GlcNAc moiety is linked to the Tyr40-OH. In chain A, the ligand prefers coordination to Pro200-O and Asp201-OD2 of the neighboring protein molecule in the crystal. This leads to a conformational change of the trisaccharide (Fig. 4C, 4D and5B). Additional stabilizing interactions result in small differences between the observed conformations of the ligand in each particular binding site of the protein, however they generally correspond to the conformations of the particular sugar linkages in PDB accessible structures. (Imberty et al., 1995) shown as empty squares. Values for the particular sugar linkage derived from PDB-deposited structures using the program GlyTorsion are shown as grey crosses.

AFL/Le Y complex

Lewis Y tetrasaccharide (Le Y ), containing one Lfucose bound by an α-1,2 linkage and one bound by an α-1,3 linkage, was determined to be the epitope best recognized by glycan array experiment of the biologically relevant oligosaccharides (Houser et al., 2013). Clear electron density for the complete Le Y was observed in binding site 3 of both chains and in site 6 of chain A. For sites 1 and 2, electron densities for three out of four saccharide units were seen. Site 4 did not recognize this saccharide and in site 5, only the density for L-fucose was detected. Both protein subunits show the same binding mode considering the identity of the ligand bound in particular binding site (Table 3). In chain B some saccharide units in sites 1, 2 and 6 are stabilized by crystal contacts, as well as in site 3 of chain A. For sites 1, 2 and 6 in both chains, α-1,2 linked Lfucose is preferentially recognized by the binding site (Fig. 6). The GlcNAc unit is stabilized by Tyr88 and through water bridges by His23 and Glu41 in site 1, while only water bridges are present in site 2, one linking GlcNAc to Asp89 and Thr92 and the second to Trp141. In the case of site 6 of chain A, the O6 of the GlcNAc unit interacts with Asn22-ND2, two additional water bridges link GlcNAc to Trp271 and Ala302 and one water bridge link O3 of the Gal unit to Tyr40 (Fig. 6D). The  anomeric form of the O1 of GlcNAc establishes hydrogen bonds with Asp280-OD2 and Asn300-ND2. However, the β anomer is usually present in biological oligosaccharides, so these interactions probably do not occur in nature. (Imberty et al., 1995) shown as empty square. Values for the particular sugar linkage derived from PDB-deposited structures using the program GlyTorsion shown as grey crosses

In site 3 of both chains, the α1-3 linked fucose is bound (Fig. 6C). The Gal unit is coordinated by Arg126 and Asn142 residues, while GlcNAc forms one weak hydrogen bond with Tyr192 in chain B. In site 5 only L-fucose could be assigned, while in site 4 no saccharide was found and the site is occupied by a glycerol molecule, which was used as a cryo-protecting agent.

Looking closer at the conformation of the Lewis Y tetrasaccharide upon binding, distortion of the angles between the bound fucose and the second saccharide unit can be observed when compared to the calculated ideal values (Imberty et al., 1995) (Fig. 7). The rest of the saccharide tends to adopt the conformation of the minimal energy state.

Discussion

The biological role of fungal lectins is the subject of debate for many years. It was proposed, that one of the key roles is the interaction with the environment such as the saccharides in organic matter. The pathogenic fungi may use them to interact with the host tissues, that is the first step of infection.

In this paper we investigated the relationship between the structure and the binding properties of the recently described fucose-specific lectin AFL from Aspergillus fumigatus. Several fungal or bacterial homologues of this protein have been described so far (Audfray et al., 2011;Kostlanova et al., 2005;Matsumura et al., 2007), including the closely-related AOL from Aspergillus oryzae (Ishida et al., 2002) and the well-known AAL from Aleuria aurantia (Wimmerova et al., 2003). AFL has been chosen mainly for its probable role in the pathogenicity of A. fumigatus, which is an important opportunistic pathogen causing allergies, bronchopulmonary aspergillosis or aspergilloma (Dagenais and Keller, 2009;Latge, 1999).

We have demonstrated that despite the similarities between AFL and AAL, there are some interesting differences between them. Although both proteins contain tandem repeats, enabling the formation of six bladed β-barrels where the binding sites are located in between adjacent blades, clear differences appear in the dimer interface. Molecules of AFL are shifted with respect to the dimer of AAL, such that the central tunnels are almost aligned in AFL. This results in a larger monomer-monomer interface area (988 Å 2 AAL, 1104 Å 2 AFL) which implies the greater dimer stability of AFL. The residues forming the intermonomer hydrogen bonds are also different, where the number of hydrogen bonds is higher in AFL dimer.

In contrast to AAL with five binding sites (Wimmerova et al., 2003), AFL possesses six binding sites, all able to bind L-fucose and related saccharides. The amino acids that play a role in ligand binding are not strictly conserved in these lectins. Considering the four amino acids directly involved in Fuc binding (through H-bonds or CH-π interaction), AAL sites vary only in the stacking amino acid, which can be either Trp or Tyr. Additional differences occur in AFL, including the replacement of Glu with Gln, involvement of the main chain in the binding and employment of water molecules to stabilize the interaction. These binding modes have not been observed in other lectins from AAL family.

The analysis of the complexes between AFL and various ligands further confirmed the nonequivalency of its binding sites. While α-methylselenofucoside was present in the six binding sites (Houser et al., 2013), L-Gal was only detected in four of them. The binding of L-Gal requires the presence of a glutamate residue for interacting with the O-3 and O-4 of the saccharide. Sites 4 and 5, where L-Gal could not bind, have Gln at this position, but they also present an unfavorable orientation of Tyr168 and Phe274, respectively, which would lead to sterical conflict with O6-L-Gal. This may correlate with the inability to fit SPR data with a two-site model. Therefore, the sites may be more specific for the α,L-fucosylated epitope than the others. Similarly, there are only some binding sites occupied in the complex with βMeFuc. Even though the absence of β-linked fucose can be explained by sterical hindrances, it is noteworthy that only sites with a stacking Tyr residue (sites 1, 3 and 6) are able to accommodate this ligand (Table 3). The only exception is site 5 in chain C, where the ligand is stabilized due to crystal packing. This nicely corresponds to the SPR data, where the one-binding-site model was more suitable for the AFL-βMeFuc interaction. The lower affinity of the interaction is probably due to the fact that stacking with Tyr residues is not as efficient as with Trp residues, as reported previously for homologous lectin RSL from Ralstonia solanacearum (Wimmerova et al., 2012).

The structure of the AFL/Le Y complex revealed an interesting binding pattern, where sites 1, 2 and 6 predominantly bind the α1-2 linked fucose, while site 3 recognizes the α1-3 linked fucose. Site 5 probably binds both Fuc and site 4 is not able to accommodate the whole tetrasaccharide. This clearly demonstrates a high variability among AFL binding sites. To further decipher the preference of each binding site, we crystallized AFL with a mixture of disaccharides, where Fuc is linked to the other saccharide through various linkages. All binding sites were occupied by a ligand, but only three of them preferred one particular disaccharide from the mixture, i.e. αFuc(1-4)GlcNAc, which is a natural epitope in plants (Fitchette-Laine et al., 1997). This preference could be explained by the small differences in the binding of the ligand, where the fucoside part is crucial, while the identity of the second saccharide only marginally affects the binding. Since the complexes were cocrystallized, the observed differences are probably not caused by packing but due to the amino acid composition of the binding sites. In the complex with the human blood group A trisaccharide, the ligand is recognized by each binding site, but a more detailed view reveals the flexibility of the epitope. The creation of additional hydrogen bonds between the protein and the Gal or GalNAc part of the saccharide can change the torsion angles of the oligosaccharide, as can intermonomeric interactions within the crystal. Generally, AFL-sugar binding in particular site is limited only for large epitopes or for ligands with directly modified fucose, while alpha-fucosylated di-and trisaccharides are recognized by all of the sites.

Several methods are commonly used to determine lectin binding affinities. Here we demonstrated that the surface plasmon resonance method is suitable for AFL, but still has some disadvantages. AFL remains active upon immobilization and the response to binding partners is clear and dose dependent. For low-affinity ligands the change in SPR signal is too weak, but Fuc and its derivatives were suitable analytes. The data were much more accurately fitted by the two-site binding model, showing the presence of non-equivalent binding sites. It was reported for the structural homologous protein RSL, that the stacking interaction between the saccharide unit (L-fucose) and the aromatic side chain (Trp) is crucial for high-affinity binding (Wimmerova et al., 2012). With AFL, two sets of binding sites can be distinguished, one containing Tyr and one containing Trp. This correlates well with the observed two-site binding model suggested by SPR and also with the structure of βMeFuc complex. The lower binding affinity and one-site model suitability nicely correspond to the occupancy of Tyr-containing binding sites observed in this case. The values of K d are in the micromolar range for all ligands tested. This is also true for homologous AAL (Wimmerova et al., 2003) and AOL (Matsumura et al., 2009). However, no significance was observed for the existence of the high affinity binding site in AFL. The marginal difference in K d 's with respect to various fucosylated disaccharides may be caused by only infrequent interactions of the second saccharide and AFL side chains. Comparing the previous hemagglutination data (Houser et al., 2013) with SPR results, minimal inhibitory concentrations nicely correlates with the low affinity binding sites K d 's, as expected. Three binding sites of both types seem to be more specific towards αFuc(1-4)GlcNAc than towards the other disaccharides tested, based on the crystal structure. As SPR detected αFuc(1-3)GlcNAc being bound with similar strength, both ligands are probably bound in the sites where only fucose was assigned. Also the binding might slightly differ under SPR and crystallization conditions. Of all the oligosaccharides tested, the Lewis Y tetrasaccharide was the highest affinity ligand overall. This corresponds to our previous finding by glycan array (Houser et al., 2013).

It can be concluded, that AFL evolves into a multispecific lectin being able to recognize Fuc in all possible linkages. These could be found not only in the decomposed plant matter in soil, which is the natural environment for A. fumigatus, but also in various epitopes on the human tissues. Hence AFL may help Aspergillus to invade human hosts through various tissues, especially lungs which are the most prevalent one. Taking all of our findings together, AFL displays a high variability in binding specificity while using a common binding motif. The similarity to the AAL lectin from Aleuria aurantia is obvious, yet the differences in the number of binding sites, their specificity and affinity are interesting. Combining our results with previous work on AFL structural homologues (Audfray et al., 2011;Kostlanova et al., 2005;Matsumura et al., 2007;Romano et al., 2011;Wimmerova et al., 2003) can help to understand the rules used by AAL family proteins in ligand recognition. In addition, AFL being a potential virulence factor of A. fumigatus, it might be an interesting drug target. Therefore the finding of binding site non-equivalency is crucial for meaningful inhibitor development and treatment strategies. 70.44, 117.80 71.21, 90.34, 189.17 47.41, 88.35, 79.78 45.73, 88.38, 78.58 46.42, 47.44, 80.09 47.32, 47.53, 77.36 47.63, 140.22 # Equimolar mixture of αFuc(1-2)Gal, αFuc(1-3)GlcNAc, αFuc(1-4)GlcNAc and αFuc(1-6)GlcNAc disaccharides used for crystallization.

Table 3 Ligands coordinated in individual binding sites of AFL crystal structures.

Only the clearly defined parts of a saccharide are listed. Saccharide units bound to protein directly are in bold, via water molecule underlined and ligands stabilized by crystal packing interactions are in italics. In all cases the interaction cutoff was set to 3.6 Å. # Equimolar mixture of αFuc(1-2)Gal, αFuc(1-3)GlcNAc, αFuc(1-4)GlcNAc and αFuc(1-6)GlcNAc disaccharides used for crystallization.

Chain

Figure 1

 1 Figure 1 Comparison of AFL site 2 in chain A of L-Gal complex (pink) with MeSeFuc complex (cyan). L-Gal (carbons in magenta) adopts the same orientation as MeSeFuc (carbons in blue) and is stabilized by two conserved water molecules (shown as spheres). The minor shift in the position of the Leu123 side chain and different orientation of loop 120-123 can be seen at the top of the figure.

Figure 2

 2 Figure2AFL binding sites with βMeFuc (magenta). In sites 1, 3 and 6, βMeFuc molecule crystallographically assigned in the chain A of the AFL/βMeFuc complex structure is shown (magenta). In sites 2, 4 and 5, where no βMeFuc was detected, shown βMeFuc (orange) was superposed according to the MeSeFuc molecule from the AFL/MeSeFuc complex (4agi). Possible sterical conflicts indicated by black arrow. Tyr95 and Tyr199 that would probably collide with βMeFuc in sites 2 and 4, respectively, and corresponding amino acids in other sites are highlighted in yellow.

Figure 4

 4 Figure4BGA trisaccharide complex with AFL. Sites are labeled according to chain and site number in each panel. Interacting residues are labeled when stabilizing a Gal or GlcNAc moiety directly or via water bridge. Interacting residues from a neighboring monomer in the crystal (shown in yellow) cause different oligosaccharide conformations in site 6 of chain A and chain B, respectively. Color code: fucose -magenta, galactose -blue, GlcNAc -green. Bridging water molecules are represented as red spheres, hydrogen bonds between saccharide and protein or water molecules, respectively, as black dashed lines

Figure 5

 5 Figure 5 Sugar Ramachandran plots for BGA epitope in AFL complex. Ligands in each binding site are shown in different colors. Conformation of theoretical energetic minima(Imberty et al., 1995) shown as empty squares. Values for the particular sugar linkage derived from PDB-deposited structures using the program GlyTorsion are shown as grey crosses.

Figure 4

 4 Figure4LeY oligosaccharides bound in AFL chain A sites 1, 2, 3 and 6. Color code: α1-2 linked Fuc -magenta, α1-3 linked Fuc -orange, Gal -blue, GlcNAc -green, amino acids stabilizing saccharide units other than terminal fucose -cyan; bridging water shown as red spheres, hydrogen bonds shown as black dashed lines.

Figure 7

 7 Figure 7 Sugar Ramachandran plots for Lewis Y epitope in AFL complex. Ligands in each binding site shown in different colors. Conformation of theoretical energetic minimum(Imberty et al., 1995) shown as empty square. Values for the particular sugar linkage derived from PDB-deposited structures using the program GlyTorsion shown as grey crosses

  αGalNAc1-3)Gal αFuc1-2Gal † Ligands from chains C and D shown instead of A and B, respectively.

Table 1

 1 Apparent K d values for interaction of biologically important saccharides with AFL using SPR measurement.Values are given with standard deviation based on measurement on three independent channels with different amounts of AFL immobilized.

	Ligand	K d (one site) [µM]	K d1 (two sites) [µM] K d2 (two sites) [µM]
	Fuc	209 ± 15	408 ± 40	91.9 ± 32.8
	αMeFuc	120 ± 8	958 ± 189	83.0 ± 4.9
	βMeFuc	251 ± 29	#	#
	L-Gal	775 ± 71	#	#
	αFuc(1-2)Gal	82.3 ± 5.2	220 ± 39	14.2 ± 4.3
	αFuc(1-3)GlcNAc	71.7 ± 5.7	278 ± 19	7.28 ± 0.91
	αFuc(1-4)GlcNAc	63.3 ± 4.9	202 ± 8	4.10 ± 0.42
	αFuc(1-6)GlcNAc	84.0 ± 4.7	206 ± 18	15.0 ± 2.1
	αFuc(1-2)[GalNAc(1-3)]Gal	68.0 ± 5.9	183 ± 47	11.5 ± 4.5
	Blood group A trisaccharide
	αFuc(1-2)[Gal(1-3)]Gal	61.7 ± 4.7	181 ± 28	10.3 ± 2.3
	Blood group B trisaccharide
	αFuc(1-2)βGal(1-4)GlcNAc	68.3 ± 7.8	192 ± 34	4.38 ± 1.68
	Blood group H type 2 trisaccharide
	αFuc(1-4)[βGal(1-3)]GlcNAc	101 ± 13	442 ± 79	8.24 ± 1.90
	Lewis a trisaccharide
	αFuc(1-3)[βGal(1-4)]GlcNAc	172 ± 11	246 ± 9	1.80 ± 0.53
	Lewis X trisaccharide
	αFuc(1-2)βGal(1-3)[αFuc(1-4)]GlcNAc	131 ± 7	249 ± 59	26.6 ± 13.3
	Lewis b tetrasaccharide
	αFuc(1-2)βGal(1-4)[αFuc(1-3)]GlcNAc	75.8 ± 3.1	113 ± 6	3.85 ± 1.07
	Lewis Y tetrasaccharide

# Values not determined. Two-site models for βMeFuc and L-Gal were unable to reach meaningful fitting.

Table 2

 2 Data collection and refinement statistics.Values for the outer shell are given in parentheses.

	Ligand	L-Gal	βMeFuc		Fucα1-6GlcNAc	Disaccharide mixture#	Blood group A	Le Y	None
	PDB ID	4UQ7	4C1Y		4AGT	4AHA		4AH4	4AH5	4UOU
	Data collection statistics
	Diffraction source	ESRF beamline ID23-1	BESSY beamline 14.2	II	ESRF beamline ID23-2	ESRF ID23-2	beamline	ESRF beamline ID23-1	ESRF beamline ID23-2	BESSY beamline 14.1	II
	Wavelength (Å)	0.9763	0.9184		0.8726	0.8726		0.9795	0.8726	0.9184
	Space group	P2 1	P2 1 2 1 2 1		P2 1	P2 1		P1	P1	P2 1
	a, b, c (Å)	80.03,

Acknowledgements

We wish to acknowledge the Consortium for Functional Glycomics, Grant number GM62116, for performing the glycan array studies. We also wish to thank to the European Synchrotron Radiation Facility in Grenoble for access to beamline ID23-1 and Helmholtz-Zentrum Berlin for access to BESSY II beamline 14.2. We would like to thank to Ben Watson-Jones for the language correction. The research leading to these results received financial support from the European Union under the Seventh Framework Programme by CEITEC (CZ.1.05/1.1.00/02.0068) project from European Regional Development Fund, SYLICA (Contract No. 286154 under ''Capacities'' specific programme), Czech Ministry of Education (LH13055) and the Czech Science Foundation (GA13-25401S). This work was further supported by CNRS, France, the FINOVI foundation and the French cystic fibrosis Association Vaincre la Mucoviscidose.


Justin Missaghieh--Poncet
email: justin.missaghieh-poncet@univ-pau.fr

On the challenges behind deep geothermal energy: ecological modernization or false solution? A comparative case studies between France and Switzerland

❶ Introduction

➔ Energy transition. Geothermal energy presented as unlimited, "green" and no intermittency energy.

➔ The technology has been developed in sedimentary basins (i.e. Paris Basin).

➔ But needs to adapt to new geological environments. ➔ We are interested in the difficulties of deploying geothermal energy outside of favourable environments

❸ Conceptual framework

➔ The subsoil is becoming politicized (Arnauld De Sartre, Chailleux 2021).

➔ Verticalization territories: Greater awareness of the subsoil's existence and the necessity to 'secure' against seismic risk (Bobbette, Donovan 2019).

➔ Sociology of translation [START_REF] Callon | Éléments pour une sociologie de la traduction: la domestication des coquilles saint-jacques et des marinspêcheurs dans la baie de saint-Brieuc[END_REF]): help to understand the debates between different actors.

➔Figure 1 :

 1 Figure 1: Simplified illustration of a geothermal power plant

Figure 2 :Figure 3 :

 23 Figure 2: Location of case studies ❹ Methodology and Fieldwork

Research project Gouvernance élargie pour les filières d'ingénierie du sous-sol website: https://www.gefiss.