Biology subjects

Josse, C.

Publications and source records attributed to Josse, C..

5 recordsLinked to original sources

bioRxiv2023

Tailoring evidence into action: using a codesign approach for biodiversity information in the Tropical Andes

Biodiversity conservation is a complex and transdisciplinary problem that requires engagement and cooperation among scientific, societal, economic, and political institutions. However, historical approaches have often failed to bring together and address the needs of relevant stakeholders in decision-making processes. The Tropical Andes, a biodiversity hotspot where conservation efforts often conflict with socioeconomic issues and policies that prioritize economic development, provides an ideal model to develop and implement more effective approaches. In this study, we present a codesign approach that mainstreams and improves the flow of biodiversity information in the Tropical Andes, while creating tailored outputs that meet the needs of economic and societal stakeholders. We employed a consultative process that brought together biodiversity information users and producers at the local, national, and regional levels through a combination of surveys and workshops. This approach identified priority needs and limitations of the flow of biodiversity information in the region, which led to the co-design of user-relevant biodiversity indicators. By leveraging the existing capacities of biodiversity information users and producers, we were able to co-design multiple biodiversity indicators and prioritize two for full implementation ensuring that the data was findable, accessible, interoperable, and reusable based on the FAIR principles. This approach helped address limitations that were identified in the stakeholder engagement process, including gaps in data availability and the need for more accessible biodiversity information. Additionally, capacity-building workshops were incorporated for all stakeholders involved, which aimed to not only improve the current flow of biodiversity information in the region but also facilitate its future sustainability. Our approach can serve as a valuable blueprint for mainstreaming biodiversity information and making it more inclusive in the future, especially considering the diverse worldviews, values, and knowledge systems between science, policy, and practice.

ecology↗

bioRxiv2023

Assessing Random Forest self-reproducibility for optimal short biomarker signature discovery

Biomarker signature discovery remains the main path to develop clinical diagnostic tools when the biological knowledge on a pathology is weak. Shortest signatures are often preferred to reduce the cost of the diagnostic. The ability to find the best and shortest signature relies on the robustness of the models that can be built on such set of molecules. The classification algorithm that will be used is selected based on the average performance of its models, often expressed via the average AUC. However, it is not garanteed that an algorithm with a large AUC distribution will keep a stable performance when facing data. Here, we propose two AUC-derived hyper-stability scores, the HRS and the HSS, as complementary metrics to the average AUC, that should bring confidence in the choice for the best classification algorithm. To emphasize the importance of these scores, we compared 15 different Random Forests implementation. Additionally, the modelization time of each implementation was computed to further help deciding the best strategy. Our findings show that the Random Forest implementation should be chosen according to the data at hand and the classification question being evaluated. No Random Forest implementation can be used universally for any classification and on any dataset. Each of them should be tested for both their average AUC performance and AUC-derived stability, prior to analysis. Author summaryTo better measure the performance of a Machine Learning (ML) implementation, we introduce a new metric, the AUC hyper-stability, to be used in parallel with the average AUC. This AUC hyper-stability is able to discriminate ML implementations that show the same AUC performance. This metric can therefore help researchers in choosing the best ML method to get stable short predictive biomarker signatures. More specifically, we advocate a tradeoff between the average AUC performance, the hyper-stability scores, and the modeling time.

bioinformatics↗

bioRxiv2020

Are indigenous territories and community-managed areas effective natural climate solutions? A neotropical analysis using matching methods and geographic discontinuity designs.

Indigenous Territories (ITs) with less centralized forest governance than Protected Areas (PAs) may represent cost-effective natural climate solutions to meet the Paris agreement. However, the literature has been limited to examining the effect of ITs on deforestation, despite the influence of anthropogenic degradation. Thus, little is known about the temporal and spatial effect of allocating ITs on carbon stocks dynamics that account for losses from deforestation and degradation. Using Amazon Basin countries and Panama, this study aims to estimate the temporal and spatial effects of ITs and PAs on carbon stocks. To estimate the temporal effects, we use annual carbon density maps, matching analysis, and linear mixed models. Furthermore, we explore the spatial heterogeneity of these estimates through geographic discontinuity designs, allowing us to assess the spatial effect of ITs and PAs boundaries on carbon stocks. The temporal effects highlight that allocating ITs preserves carbon stocks and buffer losses as PAs in Panama and Amazon Basin countries. The geographic discontinuity designs reveal that ITs boundaries secure more extensive carbon stocks than their surroundings, and this difference tends to increase towards the least accessible areas, suggesting that indigenous land use in neotropical forests may have a temporarily and spatially stable impact on carbon stocks. Our findings imply that ITs in neotropical forests support Nationally Determined Contributions (NDCs) under the Paris Agreement. Thus, Indigenous peoples must become recipients of countries results-based payments.

ecology↗

bioRxiv2020

Deeper insights into long-term survival heterogeneity of Pancreatic Ductal Adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses

BackgroundPancreatic ductal adenocarcinoma (PDAC) is categorized as the seventh leading cause of cancer mortality worldwide. Its predictive markers for long-term survival are not well known. Therefore, it is interesting to delineate individual-specific perturbed genes when comparing long-term (LT) and short-term (ST) PDAC survivors, and to exploit the integrative individual- and group-based transcriptome profiling. MethodUsing a discovery cohort of 19 PDAC patients from CHU-Liege (Belgium), we first performed differential gene expression (DGE) analysis comparing LT to ST survivor. Second, we adopted unsupervised systems biology approaches to obtain gene modules linked to clinical features. Third, we created individual-specific perturbation profiles and identified key regulators across the LT patients. Furthermore, we applied two gene prioritization approaches: random walk-based Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics Data (NetICS) to integrate group-based and individual-specific perturbed genes in relation to PDAC LT survival. FindingsWe identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits such as tumor size and chemotherapy. DGE analysis identified differences in genes involved in metabolic and cell cycle activity. Validation of DEGs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Individual-specific omics changes across LT survivors revealed biological signatures such as focal adhesion and extracellular matrix receptors, implying a potential role in molecular-level heterogeneity of LT PDAC survivors. Via NetICS and DADA we not only identified various known oncogenes such as CUL1, SCF62, EGF, FOSL1, MMP9, and TGFB1, but also highlighted novel genes (TAC1, KCNH7, IRS4, DKK4). InterpretationOur proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling. It suggested novel potential transcriptome marks of LT survival heterogeneity in PDAC. FundingTelevie-FRS-FNRS

genomics↗

bioRxiv2017

Transcriptome wide analysis of natural antisense transcripts shows their potential role in breast cancer.

Non-coding RNAs (ncRNA) represent at least 1/5 of the mammalian transcript amount, and about 90% of the genome length is actively transcribed. Many ncRNAs have been demonstrated to play a role in cancer. Among them, natural antisense transcripts (NAT) are RNA sequences which are complementary and overlapping to those of protein-coding transcripts (PCT). NATs were punctually described as regulating gene expression, and are expected to act more frequently in cis than other ncRNAs that commonly function in trans. In this work, 22 breast cancers expressing estrogen receptors and their paired healthy tissues were analyzed by strand-specific RNA sequencing. To highlight the potential role of NATs in gene regulations occurring in breast cancer, three different gene extraction methods were used: differential expression analysis of NATs between tumor and healthy tissues, differential correlation analysis of paired NAT/PCT between tumor and healthy tissues, and NAT/PCT read count ratio variation between tumor and healthy tissues. Each of these methods yielded lists of NAT/PCT pairs that were demonstrated to be enriched in survival-associated genes on an independent cohort (TCGA). This work allows to highlight NAT lists that display a strong potential to affect the expression of genes involved in the breast cancer pathology.

genomics↗