In-place Update of Suffix Array while Recoding Words

International audienceMotivated by grammatical inference and data compression applications, we propose an algorithm to update a suffix array after the substitution, in the indexed text, of some occurrences of a given word by a new character. Compared to other published index update methods, the problem addressed here may require the modification of a large number of distinct positions over the original text. The proposed algorithm uses the specific internal order of suffix arrays in order to update simultaneously groups of entries, and ensures that only entries to be modified are visited. Experiments confirm a significant execution time speed-up compared to the construction of suffix array from scratch at each step of the application

Exploring glycopeptide-resistance in Staphylococcus aureus: a combined proteomics and transcriptomics approach for the identification of resistance-related markers

BACKGROUND: To unravel molecular targets involved in glycopeptide resistance, three isogenic strains of Staphylococcus aureus with different susceptibility levels to vancomycin or teicoplanin were subjected to whole-genome microarray-based transcription and quantitative proteomic profiling. Quantitative proteomics performed on membrane extracts showed exquisite inter-experimental reproducibility permitting the identification and relative quantification of >30% of the predicted S. aureus proteome. RESULTS: In the absence of antibiotic selection pressure, comparison of stable resistant and susceptible strains revealed 94 differentially expressed genes and 178 proteins. As expected, only partial correlation was obtained between transcriptomic and proteomic results during stationary-phase. Application of massively parallel methods identified one third of the complete proteome, a majority of which was only predicted based on genome sequencing, but never identified to date. Several over-expressed genes represent previously reported targets, while series of genes and proteins possibly involved in the glycopeptide resistance mechanism were discovered here, including regulators, global regulator attenuator, hyper-mutability factor or hypothetical proteins. Gene expression of these markers was confirmed in a collection of genetically unrelated strains showing altered susceptibility to glycopeptides. CONCLUSION: Our proteome and transcriptome analyses have been performed during stationary-phase of growth on isogenic strains showing susceptibility or intermediate level of resistance against glycopeptides. Altered susceptibility had emerged spontaneously after infection with a sensitive parental strain, thus not selected in vitro. This combined analysis allows the identification of hundreds of proteins considered, so far as hypothetical protein. In addition, this study provides not only a global picture of transcription and expression adaptations during a complex antibiotic resistance mechanism but also unravels potential drug targets or markers that are constitutively expressed by resistant strains regardless of their genetic background, amenable to be used as diagnostic targets

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Mise à jour incrémentale de tableau des suffxes en cours de recodage

National audienceDe part sa légèreté, sa puissance et sa simplicité, le tableau des suffixes est une structure de données de plus en plus utilisée dans l'algorithmique du texte, en particulier pour les problèmes nécessitant une indexation. Cependant, l'un des points faibles de cette structure réside dans le fait que sa mise à jour, suite à une modification du texte indexé n'est pas aisée. En particulier, elle se prête assez mal à la suppression ou l'ajout de portions de texte. Motivés par des problématiques d'inférence grammaticale, nous cherchons dans ce travail une solution pour mettre à jour le tableau des suffixes suite à la substitution dans le texte indexé de toutes les occurrences d'un mot par un nouveau caractère unique. De ce travail en cours d'aboutissement, nous présenterons les principaux axes algorithmiques ainsi que les premiers résultats, qui sont encourageants