Planification stratégique d’une mine souterraine en tenant compte de l’incertitude géologique

RÉSUMÉ : La planification à long terme des projets miniers est marquée par de nombreux paramètres incertains. Parmi ceux-ci, l'incertitude géologique est considérée comme étant l'élément problématique principal étant donné qu'il est fréquemment la cause de l'échec d'un projet. Aussi, les coûts engendrés lors de la campagne de forage d'exploration sont faramineux et il est donc nécessaire de bien représenter, interpréter et surtout, utiliser les données recueillies. Conventionnellement, pour les opérations minières souterraines, une méthode d'estimation est utilisée pour reproduire un modèle de gisement, mais elle engendre la destruction d'informations précieuses telles que la variabilité et les valeurs extrêmes. C'est dans ce contexte que ce projet a pris forme, visant à intégrer l'incertitude géologique pendant la phase de planification stratégique minière. L'approche préconisée dans ce mémoire est l'optimisation stochastique. D'abord, l'objectif est de mettre sur pied un modèle mathématique permettant l'ordonnancement des activités minières souterraines en tenant compte de l'incertitude géologique. Il est démontré que cette méthode permet une meilleure gestion des risques géologique, c'est-à-dire qu'elle améliore l'exactitude de la prédiction des objectifs de production. Les résultats obtenus sont concluants puisqu'on obtient une augmentation de la valeur attendue du projet et que l'on note une diminution de l'écart entre les quantités des indicateurs de production par rapport aux objectifs. Toutefois, la taille de ce type de problème rend la résolution difficile. Les prochaines avenues de recherche devraient porter sur la considération des paramètres incertains du problème et sur le développement de stratégies d'accélération afin de diminuer le temps de résolution tout en maintenant la qualité de la solution.----------ABSTRACT : Long term mine planning is characterized by several uncertain parameters. Among these, geological uncertainty is the most critical due to its major impact on project feasibility. Indeed, an unexpected mine closure is frequently explained by an overvalued economic potential of the deposit. Furthermore, costs associated to exploration drilling are substantial and consequently, it is imperative to well represent, interpret and use the data collected. Conventionally, deterministic approaches are selected for underground mine planning where an estimated representation of the orebody is used. However, this type of reproduction leads to destruction of extreme values and misrepresentation of the grade variability. This context inspired the subject of this thesis, where, mainly, it aims to integrate and to manage risks related to geological uncertainty while optimizing long-term underground schedule. The selected approach is a stochastic optimization using a set of simulations corresponding to the deposit. First, the objective consists on developing a mathematical model for mine scheduling while considering geological uncertainty. It also aims for risk management regarding production targets. The method suggested is applied to a case study and results obtained are conclusive. Indeed, it leads to a significant increase of the project NPV and an improvement of risk management regarding production expectations. However, the large size of this problem made the resolution difficult and that is why, further studies should work on acceleration strategic without compromising the results quality

Respective Prognostic Value of Genomic Grade and Histological Proliferation Markers in Early Stage (pN0) Breast Carcinoma

Genomic grade (GG) is a 97-gene signature which improves the accuracy and prognostic value of histological grade (HG) in invasive breast carcinoma. Since most of the genes included in the GG are involved in cell proliferation, we performed a retrospective study to compare the prognostic value of GG, Mitotic Index and Ki67 score.A series of 163 consecutive breast cancers was retained (pT1-2, pN0, pM0, 10-yr follow-up). GG was computed using MapQuant Dx(R).GG was low (GG-1) in 48%, high (GG-3) in 31% and equivocal in 21% of cases. For HG-2 tumors, 50% were classified as GG-1, 18% as GG-3 whereas 31% remained equivocal. In a subgroup of 132 ER+/HER2- tumors GG was the most significant prognostic factor in multivariate Cox regression analysis adjusted for age and tumor size (HR = 5.23, p = 0.02).In a reference comprehensive cancer center setting, compared to histological grade, GG added significant information on cell proliferation in breast cancers. In patients with HG-2 carcinoma, applying the GG to guide the treatment scheme could lead to a reduction in adjuvant therapy prescription. However, based on the results observed and considering (i) the relatively close prognostic values of GG and Ki67, (ii) the reclassification of about 30% of HG-2 tumors as Equivocal GG and (iii) the economical and technical requirements of the MapQuant micro-array GG test, the availability in the near future of a PCR-based Genomic Grade test with improved performances may lead to an introduction in clinical routine of this test for histological grade 2, ER positive, HER2 negative breast carcinoma

Innate lymphoid cells: parallel checkpoints and coordinate interactions with T cells

International audienceProtection of epithelial and mucosal surfaces is required for survival. The recent discovery of a diverse array of innate lymphoid cells that lie immediately beneath these surfaces has unexpectedly uncovered an entire defense system distinct from the adaptive system essential to protect these barriers. This multilayered design provides a robust system through coupling of two highly complementary networks to ensure immune protection. Here, we discuss the similarities in the hardwiring and diversification of innate lymphoid cells and T cells during mammalian immune responses

Tissue-specific transcriptional profiles and heterogeneity of natural killer cells and group 1 innate lymphoid cells

International audienceNatural killer (NK) cells and type 1 innate lymphoid cells (ILC1s) are populations of non-T, non-B lymphocytes in peripheral tissues. Although NK and ILC1 subsets have been described, their identification and characteristics remain unclear. We performed single-cell RNA sequencing and CITE-seq to explore NK and ILC1 heterogeneity between tissues. We observed that although NK1 and NK2 subsets are conserved in spleen and liver, ILC1s are heterogeneous across tissues. We identified sets of genes expressed by related subsets or characterizing unique ILC1 populations in each organ. The syndecan-4 appeared as a marker discriminating murine ILC1 from NK cells across organs. Finally, we revealed that the expressions of EOMES, GZMA, IRF8, JAK1, NKG7, PLEK, PRF1, and ZEB2 define NK cells and that IL7R, LTB, and RGS1 differentiate ILC1s from NK cells in mice and humans. Our data constitute an important resource to improve our understanding of NK-ILC1 origin, phenotype, and biology

Tumor-Infiltrating Natural Killer Cells

International audienceBecause of their potent antitumor activity and their proinflammatory role, natural killer (NK) cells are at the forefront of efforts to develop immuno-oncologic treatments. NK cells participate in immune responses to tumors by killing target cells and producing cytokines. However, in the immunosuppressive tumor microenvironment, NK cells become dysfunctional through exposure to inhibitory molecules produced by cancer cells, leading to tumor escape. We provide an overview of what is known about NK tumor infiltration and surveillance and about the mechanisms by which NK cells become dysfunctional. SIGNIFICANCE: The functions of tumor-infiltrating NK cells may be impaired. This review aims to describe the various mechanisms by which tumors alter NK-cell functions

BubbleGUM: automatic extraction of phenotype molecular signatures and comprehensive visualization of multiple Gene Set Enrichment Analyses

International audienceBackground: Recent advances in the analysis of high-throughput expression data have led to the development of tools that scaled-up their focus from single-gene to gene set level. For example, the popular Gene Set Enrichment Analysis (GSEA) algorithm can detect moderate but coordinated expression changes of groups of presumably related genes between pairs of experimental conditions. This considerably improves extraction of information from high-throughput gene expression data. However, although many gene sets covering a large panel of biological fields are available in public databases, the ability to generate home-made gene sets relevant to one's biological question is crucial but remains a substantial challenge to most biologists lacking statistic or bioinformatic expertise. This is all the more the case when attempting to define a gene set specific of one condition compared to many other ones. Thus, there is a crucial need for an easy-to-use software for generation of relevant home-made gene sets from complex datasets, their use in GSEA, and the correction of the results when applied to multiple comparisons of many experimental conditions. Result: We developed BubbleGUM (GSEA Unlimited Map), a tool that allows to automatically extract molecular signatures from transcriptomic data and perform exhaustive GSEA with multiple testing correction. One original feature of BubbleGUM notably resides in its capacity to integrate and compare numerous GSEA results into an easy-to-grasp graphical representation. We applied our method to generate transcriptomic fingerprints for murine cell types and to assess their enrichments in human cell types. This analysis allowed us to confirm homologies between mouse and human immunocytes. Conclusions: BubbleGUM is an open-source software that allows to automatically generate molecular signatures out of complex expression datasets and to assess directly their enrichment by GSEA on independent datasets. Enrichments are displayed in a graphical output that helps interpreting the results. This innovative methodology has recently been used to answer important questions in functional genomics, such as the degree of similarities between microarray datasets from different laboratories or with different experimental models or clinical cohorts. BubbleGUM is executable through an intuitive interface so that both bioinformaticians and biologists can use it. It is available at http://www.ciml.univ-mrs.fr/ applications/BubbleGUM/index.html

Pollination efficiency in farmland landscapes: exploring the relative roles of spillover, dilution and complementarity between habitats

International audienceContextRecognized as a critical ecosystem service in farmland, pollination is threatened by the decline of pollinators, notably due the homogenization of the landscape and the decline of floral resources. However, there is still a limited understanding of the interplay between landscape features and the pulses of floral resources provided by mass-flowering crops.ObjectiveThe goals of this study were to (i) determine how pollination efficiency varies with the amount of floral resources at field and landscape scales through the oilseed rape (OSR) flowering period and (ii) quantify the magnitude of the pollination processes involved.MethodsPollination efficiency (fruiting success) was measured using OSR plant phytometers placed in grasslands, cereals and OSR fields varying in quantity of floral resources at both field and landscape scales. The individual contributions of different processes to pollination were determined using a bagging experiment on plant phytometers.ResultsPollination efficiency was enhanced during both the temporal period and in landscapes with a high amount of OSR flowers, and semi-natural habitats as a result of higher pollinator presence. The bagging experiment also supported a complementarity between habitats for pollinators, as insect-pollination in grasslands and cereals was higher after OSR flowering, especially in OSR-rich landscapes, in regard to large-insect-pollination.ConclusionsThe floral resource availability drives insect-pollination through attraction, spillover, and spatial and temporal complementarities between habitats. These results suggest that maximizing pollination efficiency in farmland landscapes partly consisting of OSR fields should include a combination of habitats that provide continuous floral resources

Comparative genomics analysis of mononuclear phagocyte subsets confirms homology between lymphoid tissue-resident and dermal XCR1(+) DCs in mouse and human and distinguishes them from Langerhans cells

International audienceDendritic cells (DC) are mononuclear phagocytes which exhibit a branching (dendritic) morphology and excel at naive T cell activation. DC encompass several subsets initially identified by their expression of cell surface molecules and later shown to possess distinct functions. DC subset differentiation is orchestrated by transcription factors, growth factors and cytokines. Identifying DC subsets is challenging as very few cell surface molecules are uniquely expressed on any one of these cell populations. There is no standard consensus to identify mononuclear phagocyte subsets; varying antigens are employed depending on the tissue and animal species studied and between laboratories. This has led to confusion in how to accurately define and classify DCs across tissues and between species. Here we report a comparative genomics strategy that enables universal definition of DC and other mononuclear phagocyte subsets across species. We performed a meta-analysis of several public datasets of human and mouse mononuclear phagocyte subsets isolated from blood, spleen, skin or cutaneous lymph nodes, including by using a novel and user friendly software, BubbleGUM, which generates and integrates gene signatures for high throughput gene set enrichment analysis. This analysis demonstrates the equivalence between human and mouse skin XCR1(+) DCs, and between mouse and human Langerhans cells

Apobec3A Deamination Functions Are Involved in Antagonizing Efficient Human Adenovirus Replication and Gene Expression

ABSTRACT Apobec3A is involved in the antiviral host defense, targeting nuclear DNA, introducing point mutations, and thereby activating DNA damage response (DDR). Here, we found a significant upregulation of Apobec3A during HAdV infection, including Apobec3A protein stabilization mediated by the viral proteins E1B-55K and E4orf6, which subsequently limited HAdV replication and most likely involved a deaminase-dependent mechanism. The transient silencing of Apobec3A enhanced adenoviral replication. HAdV triggered Apobec3A dimer formation and enhanced activity to repress the virus. Apobec3A decreased E2A SUMOylation and interfered with viral replication centers. A comparative sequence analysis revealed that HAdV types A, C, and F may have evolved a strategy to escape Apobec3A-mediated deamination via reduced frequencies of TC dinucleotides within the viral genome. Although viral components induce major changes within infected cells to support lytic life cycles, our findings demonstrate that host Apobec3A-mediated restriction limits virus replication, albeit that HAdV may have evolved to escape this restriction. This allows for novel insights into the HAdV/host-cell interplay, which broaden the current view of how a host cell can limit HAdV infection. IMPORTANCE Our data provide a novel conceptual insight into the virus/host-cell interplay, changing the current view of how a host-cell can defeat a virus infection. Thus, our study reveals a novel and general impact of cellular Apobec3A on the intervention of human adenovirus (HAdV) gene expression and replication by improving the host antiviral defense mechanisms, thereby providing a novel basis for innovative antiviral strategies in future therapeutic settings. Ongoing investigations of the cellular pathways that are modulated by HAdV are of great interest, particularly since adenovirus-based vectors actually serve as COVID vaccine vectors and also frequently serve as tools in human gene therapy and oncolytic treatment options. HAdV constitute an ideal model system by which to analyze the transforming capabilities of DNA tumor viruses as well as the underlying molecular principles of virus-induced and cellular tumorigenesis