Transition versus transformation: What's the difference?

‘Transition’ and ‘transformation’ have become buzzwords in political and scientific discourses. They signal the need for large-scale changes to achieve a sustainable society. We compare how they are applied and interpreted in scientific literatures to explore whether they are distinct concepts and provide complementary insights. Transition and transformation are not mutually exclusive; they provide nuanced perspectives on how to describe, interpret and support desirable radical and non-linear societal change. Their differences may partially result from their etymological origins, but they largely stem from the different research communities concerned with either transition or transformation. Our review shows how the respective approaches and perspectives on understanding and interpreting system change can enrich each other

Steering transformations under climate change

In light of the persistent failure to reduce emissions decisively, facilitate long-term resilience against climate change and account for the connectedness of climate change with other social, environmental and economic concerns, we present a conceptual framework of capacities for transformative climate governance. Transformative climate governance enables climate mitigation and adaptation while purposefully steering societies towards low-carbon, resilient and sustainable objectives. The framework provides a systematic analytical tool for understanding and supporting the already ongoing changes of the climate governance landscape towards more experimental approaches that include multi-scale, cross-sectoral and public-private collaborations. It distinguishes between different types of capacities needed to address transformation dynamics, including responding to disturbances (stewarding capacity), phasing-out drivers of path dependency (unlocking capacity), creating and embedding novelties (transformative capacity) and coordinating multi-actor processes (orchestrating capacity). Our case study of climate governance in Rotterdam, the Netherlands, demonstrates how the framework helps to map the activities by which multiple actors create new types of conditions for transformative climate governance, assess the effectiveness of the capacities and identify capacity gaps. Transformative and orchestrating capacities in Rotterdam emerged through the creation of space and informal networks for strategic and operational innovation, which also propelled new types of governance arrangements and structures. Both capacities support stewarding and unlocking by integrating and mainstreaming different goals, connecting actors to each other for the development of solutions and mediating interests. Key challenges across capacities remain because of limited mainstreaming of long-term and integrated thinking into institutional and regulatory frameworks. As the ongoing changes in climate governance open up multiple questions about actor roles, effective governance processes, legitimacy and how effective climate governance in the context of transformations can be supported, we invite future research to apply the capacities framework to explore these questions

External Validation of Pretreatment Pathological Tumor Extent in Patients with Neoadjuvant Chemoradiotherapy Plus Surgery for Esophageal Cancer

Background: This study was conducted to validate a pretreatment (i.e. prior to neoadjuvant chemoradiotherapy) pathological staging system in the resection specimen after neoadjuvant chemoradiotherapy for esophageal cancer. The study investigated the prognostic value of pretreatment pathological T and N categories (prepT and prepN categories) in both an independent and a combined patient cohort. Methods: Patients with esophageal cancer treated with neoadjuvant chemotherapy and esophagectomy between 2012 and 2015 were included. PrepT and prepN categories were estimated based on the extent of tumor regression and regressional changes of lymph nodes in the resection specimen. The difference in Akaike’s information criterion (ΔAIC) was used to assess prognostic performance. PrepN and ypN categories were combined to determine the effect of nodal sterilization on prognosis. A multivariable Cox regression model was used to identify combined prepN and ypN categories as independent prognostic factors. Results: The prognostic strength of the prepT category was better than the cT and ypT categories (ΔAIC 7.7 vs. 3.0 and 2.9, respectively), and the prognostic strength of the prepN category was better than the cN category and similar to the ypN category (ΔAIC 29.2 vs. − 1.0 and 27.9, respectively). PrepN + patients who became ypN0 had significantly worse survival than prepN0 patients (2-year overall survival 69% vs. 86% in 137 patients; p = 0.044). Similar results were found in a combined cohort of 317 patients (2-year overall survival 62% vs. 85%; p = 0.002). Combined prepN/ypN stage was independently associated with overall survival. Conclusions: These results independently confirm the prognostic value of prepTNM staging. PrepTNM staging is of additional prognostic value to cTNM and ypTNM. PrepN0/ypN0 patients have a better survival than prepN +/ypN0 patients

Supportive evidence for FOXP1, BARX1, and FOXF1 as genetic risk loci for the development of esophageal adenocarcinoma

The Barrett's and Esophageal Adenocarcinoma Consortium (BEACON) recently performed a genome-wide association study (GWAS) on esophageal adenocarcinoma (EAC) and Barrett's esophagus. They identified genome-wide significant association for variants at three genes, namely CRTC1, FOXP1, and BARX1. Furthermore, they replicated an association at the FOXF1 gene that has been previously found in a GWAS on Barrett's esophagus. We aimed at further replicating the association at these and other loci that showed suggestive association with P\ua

Microglia emerge from erythromyeloid precursors via Pu.1- and Irf8-dependent pathways

<p>Microglia are crucial for immune responses in the brain. Although their origin from the yolk sac has been recognized for some time, their precise precursors and the transcription program that is used are not known. We found that mouse microglia were derived from primitive c-kit(+) erythromyeloid precursors that were detected in the yolk sac as early as 8 d post conception. These precursors developed into CD45(+) c-kit(lo) CX(3)CR1(-) immature (A1) cells and matured into CD45(+) c-kit(-) CX(3)CR1(+) (A2) cells, as evidenced by the downregulation of CD31 and concomitant upregulation of F4/80 and macrophage colony stimulating factor receptor (MCSF-R). Proliferating A2 cells became microglia and invaded the developing brain using specific matrix metalloproteinases. Notably, microgliogenesis was not only dependent on the transcription factor Pu.1 (also known as Sfpi), but also required Irf8, which was vital for the development of the A2 population, whereas Myb, Id2, Batf3 and Klf4 were not required. Our data provide cellular and molecular insights into the origin and development of microglia.</p>