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Although social learning (SL) conceptualization and implementation are flourishing in sustainability sciences, and its non-rigid conceptual fluidity is regarded as an advantage, research must advance the understanding of SL phenomenon patterns based on empirical data, thus contributing to the identification of its forms and triggering mechanisms, particularly those that can address urgent Anthropocene socio-ecological problems. This study aims to discover fundamental patterns along which SL in natural resources management differs by identifying SL archetypes and establishing correlations between the SL process and overall geopolitical conditions. Using a systematic literature review comprising 137 case studies in the five continents, content analysis, and correlations were performed. Results show two main archetypes of social learning (endogenous and exogenous). Their occurrence was linked, to where social learning occurs and how venues/preconditions for social learning are placed. In the Global South, endogenous SL should be better potentialized as a catalyzer of deliberative processes for sustainable natural resources management.

Purpose: Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions. Methods: Preoperative CTs of patients with HGSOC treated at four referral centers were retrospectively acquired and manually segmented. Hand-crafted features and deep radiomics features were extracted respectively by dedicated software (MODDICOM) and a dedicated convolutional neural network (CNN). Features were selected with and without prior harmonization (ComBat harmonization), and models were built using different machine learning algorithms, including clinical variables. Results: We included 218 patients. Radiomic models showed low performance in predicting both BRCA mutation (AUC in test set between 0.46 and 0.59) and 1-year relapse (AUC in test set between 0.46 and 0.56); deep learning models demonstrated similar results (AUC in the test of 0.48 for BRCA and 0.50 for relapse). The inclusion of clinical variables improved the performance of the radiomic models to predict BRCA mutation (AUC in the test set of 0.74). Conclusions: In our multicentric dataset, representative of a real-life clinical scenario, we could not find a good radiomic predicting model for PFS and BRCA mutational status, with both traditional radiomics and deep learning, but the combination of clinical and radiomic models improved model performance for the prediction of BRCA mutation. These findings highlight the need for standardization through the whole radiomic pipelines and robust multicentric external validations of results.

Agroforestry systems (AFS) upscaling has the potential to integrate sustainability and resilience objectives into agriculture. However, this is a daunting task requiring multi-actor collaboration across public and private sectors at multiple governance levels, coupled with innovative approaches to jointly managing AFS knowledge. Understanding such multi-actor interactions from a network perspective may help to unravel how social structures, created by relational patterns enhance or hinder AFS upscaling. Our paper aims to comparatively explore the role of regional actor-networks on AFS upscaling for a selected farming system. By conducting semi-structured interviews, we collected information about the ties of 86 actors supporting cacao agroforestry systems (CAFS) across two regions of Colombia. We use social network analysis (SNA) to comparatively visualize and understand the general structure of these networks, find relational patterns between the diverse categories of actors involved, and identify a set of key players bridging the majority of the actors within these networks. We find highly centralized networks that connect multiple actors by a low number of mostly non-reciprocal ties. Within these networks, we identify a predominance of bridging ties over bonding ties, homophily patterns among research and education institutions, and heterophily configurations among farmer-based organizations. We also find that the composition of the sets of key actors and the platforms where they converge varies substantially from region to region due to decentralized agricultural policies and differing characteristics across regions. Our approach provides key entry points for promoting multi-actor coalitions that can effectively expand the benefits of AFS in tropical agricultural systems.

In the Colombian context, disputes over natural resources, mainly over land, and poor governance are intertwined with armed conflict. Although efforts to address this situation, including the 2016 peace agreement signed between Colombian government and the Revolutionary Armed Forces of Colombia (FARC, by Spanish acronym) are underway, these disputes continue, affecting land use dynamics. Understanding the complexity and trends in land use conflicts, as well as the specific regional characteristics underlying differing land use changes across regions, is critical. This article aims to systematically understand land use dynamics in two contrasting and conflict-affected territories in Colombia, Caquetá and Cesar, thus identifying entry points to address land-use conflicts at the regional level. To address the complexity of each regional case, we apply a methodology based on system thinking to capture the interconnections between socio-economic and environmental system components and their land use dynamics. Results depicted through causal loop diagrams not just show the cascade of environmental, social, and economic failures resulting from land use changes in these two conflict-affected territories but also suggest that land tenure systems innovations and the promotion of sustainable land use interventions at the regional level can reverse the consequences of the land use changes. Thus, future actions addressing land use conflicts must be context-dependent, tackling the root and structural causes.

Tackling deforestation remains a significant challenge in tropical countries and even more so in those affected by armed conflicts. This is partly because of the limited local understanding of the causes of forest cover changes (FCC) and how these causes relate to development. In this study, we use Colombia as a model to contribute to the understanding of the links between the causes of FCC in conflict-affected countries and policies aimed at achieving sustainable development by targeting the agriculture, forestry and other land use (AFOLU) sectors. Specifically, we reviewed studies reporting on causes of FCC from 1995 to 2019 to build a state-of-the-art review. We then identified relevant public policies targeting AFOLU sectors and used them as a proxy for development. Finally, we discussed the links between these public policies and FCC. From the reviewed literature, it is clear that research on FCC in Colombia has focused on understanding the causes of forest cover losses while disregarding forest cover gains. Although cattle ranching and agriculture dominate the literature as proximate causes of deforestation and policy and institutional factors as underlying causes of deforestation, the relative importance of proximate and underlying causes of FCC in Colombia has changed over time. The main categories of policies that have been linked to FCC deal with conflict and post-conflict issues, coca eradication and, more recently, the implementation of the peace agreement. Another set of policies frequently mentioned are those related to productive activities. In Colombia, these policies' effects on forests will depend on how the state will regulate extractive activities in a post-conflict scenario. Therefore, it is imperative to review and update policies to tackle FCC, mainly deforestation, to successfully achieve sustainability targets in Colombia.

Purpose: Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions. Methods: Preoperative CTs of patients with HGSOC treated at four referral centers were retrospectively acquired and manually segmented. Hand-crafted features and deep radiomics features were extracted respectively by dedicated software (MODDICOM) and a dedicated convolutional neural network (CNN). Features were selected with and without prior harmonization (ComBat harmonization), and models were built using different machine learning algorithms, including clinical variables. Results: We included 218 patients. Radiomic models showed low performance in predicting both BRCA mutation (AUC in test set between 0.46 and 0.59) and 1-year relapse (AUC in test set between 0.46 and 0.56); deep learning models demonstrated similar results (AUC in the test of 0.48 for BRCA and 0.50 for relapse). The inclusion of clinical variables improved the performance of the radiomic models to predict BRCA mutation (AUC in the test set of 0.74). Conclusions: In our multicentric dataset, representative of a real-life clinical scenario, we could not find a good radiomic predicting model for PFS and BRCA mutational status, with both traditional radiomics and deep learning, but the combination of clinical and radiomic models improved model performance for the prediction of BRCA mutation. These findings highlight the need for standardization through the whole radiomic pipelines and robust multicentric external validations of results.

Climate disasters affect human security and development, moreso in fragile and conflict-affected contexts where population’ capacities to cope with climate change are compromised. Responses to such crises lie at the nexus of humanitarian assistance, development, and peacebuilding. Yet, there are still too few integrated programmatic responses coordinating peacebuilding and climate actions to ensure a progressive human development. This research develops a multi-scalar model to help actors identify thematic areas to inform synergistic efforts and programs at different scales to better coordinate their actions. Findings suggest that climate action and peacebuilding sectors can coordinate actions around climate and conflict risk assessments, the management of land and water resources, ecosystem restoration, nature-based climate adaptation, climate and conflict smart agriculture, natural resources governance, and sustainable market development. These collaborative efforts have the potential to generate co-benefits, such as increased social cohesion and livelihood creation.

This paper integrates peacebuilding components into sustainability impact assessment (SIA) for application in geographies vulnerable to, recovering from, or experiencing conflict. Based on the understanding that peace is a crucial pre-requisite for sustainable development and vice versa, it argues that integrating peacebuilding into SIA will enhance the development potential; particularly in conflict-affected countries. So far, SIA does not address this nexus of development and peace. Comprehensive SIA prior to implementation (i.e. ex-ante) facilitates reflections on outcomes (positive and negative), helping to design context-specific best-fit interventions. The objective of this study is to develop a set of peacebuilding indicators for future impact assessments. The study finds that existing SIA tools address many aspects, particularly economic and environmental impacts, but, although crucial to enhance societal relations and peace(building), these neglect societal aspects relating to social cohesion, agency, and transformation capacity. Thus, 17 indicators for assessing peacebuilding potential are proposed for integration in SIA in conflict-affected areas. The indicators are grouped into four components aligning with the key areas identified as missing in existing SIA frameworks but also crucial for positive peace: social cohesion, agency, negotiation capacities, and compensations.

Background and Purpose - As a reliable scoring system to detect the risk of symptomatic intracerebral hemorrhage after thrombectomy for ischemic stroke is not yet available, we developed a nomogram for predicting symptomatic intracerebral hemorrhage in patients with large vessel occlusion in the anterior circulation who received bridging of thrombectomy with intravenous thrombolysis (training set), and to validate the model by using a cohort of patients treated with direct thrombectomy (test set). Methods - We conducted a cohort study on prospectively collected data from 3714 patients enrolled in the IER (Italian Registry of Endovascular Stroke Treatment in Acute Stroke). Symptomatic intracerebral hemorrhage was defined as any type of intracerebral hemorrhage with increase of ≥4 National Institutes of Health Stroke Scale score points from baseline ≤24 hours or death. Based on multivariate logistic models, the nomogram was generated. We assessed the discriminative performance by using the area under the receiver operating characteristic curve. Results - National Institutes of Health Stroke Scale score, onset-to-end procedure time, age, unsuccessful recanalization, and Careggi collateral score composed the IER-SICH nomogram. After removing Careggi collateral score from the first model, a second model including Alberta Stroke Program Early CT Score was developed. The area under the receiver operating characteristic curve of the IER-SICH nomogram was 0.778 in the training set (n=492) and 0.709 in the test set (n=399). The area under the receiver operating characteristic curve of the second model was 0.733 in the training set (n=988) and 0.685 in the test set (n=779). Conclusions - The IER-SICH nomogram is the first model developed and validated for predicting symptomatic intracerebral hemorrhage after thrombectomy. It may provide indications on early identification of patients for more or less postprocedural intensive management.

We discuss some issues involved in modeling of complex systems composed of dynamically interacting agents. We describe a prototype of simulation environment created for modeling of such systems with the aim of evaluating strategies of enterprizes in the information economy, but applicable to general multi-agent systems. The case study is presented along with the mathematical description of the multi-agent systems.