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According to Giraudoux, La Fontaine had to overcome five temptations – bourgeois life, women, high society, etc. – before discovering his true vocation as a fabulist. Could the ivory tower have been a sixth temptation that seduced him just before the fall of Fouquet? La Fontaine’s career would then be divided into two periods: the period of “gallant” disengagement and the period of the discovery, with the Fables, of an “impure” poetry that combined different kinds of knowledge and dialogued with the society of his time. This chronological perspective is not wrong, but it needs to be qualified. It is less a question of evolution than of a double postulation that animated La Fontaine’s career, torn between lightness and seriousness, between the cult of form and scholarly innutrition, between the temptation of confinement and openness to the world.

Collagen VI is an essential component of the extracellular matrix (ECM) composed by α1, α2 and α3 chains and encoded by COL6A1, COL6A2 and COL6A3 genes. Dominant negative pathogenic variants in COL6A genes result in defects in collagen VI protein and are implicated in the pathogenesis of muscular diseases, including Ullrich congenital muscular dystrophy (UCMD). Here, we designed a CRISPR genome editing strategy to tackle a dominant heterozygous deletion c.824_838del in exon 9 of the COL6A1 gene, causing a lack of secreted collagen VI in a patient’s dermal fibroblasts. The evaluation of efficiency and specificity of gene editing in treating patient’s fibroblasts revealed the 32% efficiency of editing the mutated allele but negligible editing of the wild-type allele. CRISPR-treated UCMD skin fibroblasts rescued the secretion of collagen VI in the ECM, which restored the ultrastructure of the collagen VI microfibril network. By using normal melanocytes as surrogates of muscle cells, we found that collagen VI secreted by the corrected patient’s skin fibroblasts recovered the anchorage to the cell surface, pointing to a functional improvement of the protein properties. These results support the application of the CRISPR editing approach to knock out COL6A1 mutated alleles and rescue the UCMD phenotype in patient-derived fibroblasts.

The development of brain-inspired neuromorphic computing architectures as a paradigm for Artificial Intelligence (AI) at the edge is a candidate solution that can meet strict energy and cost reduction constraints in the Internet of Things (IoT) application areas. Toward this goal, we present μBrain: the first digital yet fully event-driven without clock architecture, with co-located memory and processing capability that exploits event-based processing to reduce an always-on system's overall energy consumption (μW dynamic operation). The chip area in a 40 nm Complementary Metal Oxide Semiconductor (CMOS) digital technology is 2.82 mm2 including pads (without pads 1.42 mm2). This small area footprint enables μBrain integration in re-trainable sensor ICs to perform various signal processing tasks, such as data preprocessing, dimensionality reduction, feature selection, and application-specific inference. We present an instantiation of the μBrain architecture in a 40 nm CMOS digital chip and demonstrate its efficiency in a radar-based gesture classification with a power consumption of 70 μW and energy consumption of 340 nJ per classification. As a digital architecture, μBrain is fully synthesizable and lends to a fast development-to-deployment cycle in Application-Specific Integrated Circuits (ASIC). To the best of our knowledge, μBrain is the first tiny-scale digital, spike-based, fully parallel, non-Von-Neumann architecture (without schedules, clocks, nor state machines). For these reasons, μBrain is ultra-low-power and offers software-to-hardware fidelity. μBrain enables always-on neuromorphic computing in IoT sensor nodes that require running on battery power for years.

What is literary truth ? How can it be deployed in a text ? Literary theory of the 20th century has in general endeavoured to deny any relation between truth and literature, reducing the latter to the self-referential mode of Écriture. Literature, on the other hand, has never given up entirely, if only through the problematic mode of doubt or even denial, to question its own ability to grasp and express truth in its own way. Besides the formalist tendency, there are other critical approaches that place more emphasis on the modeling of reality offered by literature and on the cognitive pleasure that it provides. The 12 articles in this dossier confirm the interest in this issue : while adopting sometimes divergent perspectives, they aim to study the different “regimes of truth” that underlie works belonging to different genres, from the 16th to the 21st century. All the writers taken into consideration problematise the relationship between literature and truth and / or set up textual devices aimed at accrediting, revealing or constructing a truth, whether it be unique or plural.

This work presents a dynamically reconfigurable architecture for Neural Network (NN) accelerators implemented in Field-Programmable Gate Array (FPGA) that can be applied in a variety of application scenarios. Although the concept of Dynamic Partial Reconfiguration (DPR) is increasingly used in NN accelerators, the throughput is usually lower than pure static designs. This work presents a dynamically reconfigurable energy-efficient accelerator architecture that does not sacrifice throughput performance. The proposed accelerator comprises reconfigurable processing engines and dynamically utilizes the device resources according to model parameters. Using the proposed architecture with DPR, different NN types and architectures can be realized on the same FPGA. Moreover, the proposed architecture maximizes throughput performance with design optimizations while considering the available resources on the hardware platform. We evaluate our design with different NN architectures for two different tasks. The first task is the image classification of two distinct datasets, and this requires switching between Convolutional Neural Network (CNN) architectures having different layer structures. The second task requires switching between NN architectures, namely a CNN architecture with high accuracy and throughput and a hybrid architecture that combines convolutional layers and an optimized Spiking Neural Network (SNN) architecture. We demonstrate throughput results from quickly reprogramming only a tiny part of the FPGA hardware using DPR. Experimental results show that the implemented designs achieve a 7× faster frame rate than current FPGA accelerators while being extremely flexible and using comparable resources.

Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for clinical acceptance, easy interpretability of the AI models is crucial. The goal of the present study was to investigate whether a multi-parameter sensor could be used during a standardized activity test to interpret functional capacity in the longitudinal follow-up of CR patients. A total of 129 patients were followed for 3 months during CR using 6-min walking tests (6MWT) equipped with a wearable ECG and accelerometer device. Functional capacity was assessed based on 6MWT distance (6MWD). Linear and nonlinear interpretable models were explored to predict 6MWD. The t-distributed stochastic neighboring embedding (t-SNE) technique was exploited to embed and visualize high dimensional data. The performance of support vector machine (SVM) models, combining different features and using different kernel types, to predict functional capacity was evaluated. The SVM model, using chronotropic response and effort as input features, showed a mean absolute error of 42.8 m (±36.8 m). The 3D-maps derived using the t-SNE technique visualized the relationship between sensor-derived biomarkers and functional capacity, which enables tracking of the evolution of patients throughout the CR program. The current study showed that wearable monitoring combined with interpretable ML can objectively track clinical progression in a CR population. These results pave the road towards ambulatory CR.