Geographers are increasingly interested in islands as sites of critical and creative transformation, their works often characterized by deep commitments to collaborative research and decolonizing agendas. This article considers how four of its authors have come to understand how five other authors channel their academic, activist, and artistic practices for such ends. Our collective aim is to shed light on how islands are productive and powerful spaces for change and our focus is on four decolonizing island geographies-Okinawa, Crete, Kalaallit Nunaat/Greenland, and Trinidad-to which those five collaborating co-authors trace ancestral and cultural ties. We work to shift the centre-stage of research away from certain colonial tendencies in island studies and island geographies and to reframe the Anthropocene in relation to decolonization. So, using slow ontology and engaging in a twist on participatory research, we document and reflect on lived experiences and practices among island academics, activists, and artists as material expressions of the geohumanities.
Salix magnifica Hemsl. (Salicaceae) is illustrated and its full description is given. The history of this willow in cultivation and its distribution are discussed. Morphological characteristics of infraspecific taxa are compared and their distinction given. Two new combinations are made. The names of two taxa are lectotypified. Information about the cultivation of S. magnifica is provided.
5001 Background: The STAMPEDE trials showed that adding abiraterone acetate + prednisolone (AAP) ± enzalutamide (ENZ) to standard of care androgen deprivation therapy (SOC) improves metastasis-free survival (MFS) in high-risk non-metastatic (M0) prostate cancer (PCa) patients (pts). However, variable responses & adverse events underscore the need for prognostic & predictive biomarkers. We evaluated performance of a validated MMAI algorithm (ArteraAI Prostate Test v1.2) to identify pts who benefit most from the addition of AAP ± ENZ (ARPI). Methods: High-risk M0 STAMPEDE pts treated with SOC+ARPI (N=555) or SOC (N=781) with sufficient quality H&E biopsy images & clinical data (T stage, age, PSA) were included. MMAI score association with PCa specific mortality (PCSM, primary outcome measure) & distant metastasis (DM) was analyzed using Fine-Gray regression & cumulative incidence curves, with other cause mortality treated as competing risks. MFS was assessed using Cox regression & Kaplan-Meier curves. An optimal cut-point was identified via grid search to maximize ARPI benefit separation across biomarker positive (pos, MMAI in top quartile) & negative (neg) subgroups. Hazard ratios [95% CI] & p values are reported. Results: PCSM median follow-up was 6.0 years (N=1336). Continuous MMAI scores were statistically significantly associated with poorer PCSM (1.65 [1.43-1.90], p<0.001), MFS (1.42 [1.29-1.56], p<0.001) & DM (1.54 [1.36-1.74], p<0.001). Using clinically-established prognostic cut-offs, 89% of pts were MMAI high-risk. The optimal ARPI MMAI cut-point identified 334 biomarker-pos pts who had significantly higher PCSM than biomarker-neg pts. A statistically significant biomarker-treatment interaction for PCSM (p-int=0.04) revealed that biomarker-pos pts treated with ARPI had improved PCSM (0.42 [0.24-0.74], p=0.003), while biomarker-neg pts did not derive a treatment benefit (0.85 [0.56-1.29], p=0.45). Estimated 5-year PCSM was 9% for biomarker-pos pts receiving ARPI vs. 17% with SOC, compared to 4% & 7% for biomarker-neg pts, respectively, with similar results observed in M0N0 pts (Table 1). Conclusions: For the first time, we demonstrate that a validated MMAI algorithm can identify high-risk non-metastatic PCa pts most likely to benefit from the addition of ARPI. Notably we identify a positive biomarker-treatment interaction in the highest MMAI score quartile, which in cases of clinical equipoise could inform clinical decision-making. We highlight MMAI’s potential to optimize treatment decisions & spare biomarker-neg pts from unnecessary therapy & toxicities. Clinical trial information: NCT00268476 . Estimated 5-yr absolute risk reduction from ARPI vs SOC-treated patients by biomarker groups in M0 (M0N0) pts. Biomarker-neg Biomarker-pos PCSM 3% (1%) 8% (9%) MFS 2% (-1%) 17% (16%) DM 5% (3%) 12% (15%)
Spin and valley degrees of freedom, as carriers of information in next-generation devices, remain a key focus for achieving controllable and nonvolatile electrical modulation. In this work, we design GdIBr/In2Se3 and GdBrI/In2Se3 van der Waals heterostructures that exhibit switchable bipolar magnetic semiconducting and quasi-half-metallic states under opposite ferroelectric polarizations. Notably, in the GdBrI/In2Se3 system, ferroelectric polarization switching enables reversible reorientation of the magnetic moment, transitioning from an in-plane alignment to a 45° canting. These effects originate from the built-in electric field and interfacial charge transfer modulated by ferroelectric polarization. Furthermore, both systems exhibit coexistence of valley polarizations that can be reversibly switched by ferroelectric polarization. This work positions these heterostructures as promising platforms for electrically tunable spintronic and valleytronic functionalities near room temperature.
e13652 Background: Accurate assessment of Ki67 is critical for evaluating cellular proliferation and tumor aggressiveness in breast cancer diagnosis and prognosis. Traditionally, Ki67 immunohistochemistry (IHC) requires appropriate pre-analytical handling, standardized visual scoring, and experienced pathologists for correct interpretation. IHC is a laboratory-intensive procedure that can be affected by inter-observer variability (IOV) and residual heterogeneity. Methods: In this study, we introduce a novel artificial intelligence (AI) model that predicts Ki67 directly from Hematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs) in patients with hormone receptor-positive early-stage breast cancer. Our model utilizes deep-learning techniques to identify histopathological features that correlate with Ki67 in the whole tissue sample. This makes it hotspot-independent and enables accurate Ki67 predictions for heterogeneous tumor regions and a more comprehensive assessment. The AI-model was developed and validated using over 5200 patients from WSG ADAPT HR+/HER2- and PlanB trials with a 60/40 split and externally validated in ABCSG 6 (N = 1115). The primary objective of the Ki67 model is to correctly classify whether a tumor has low Ki67 (< 20%) or high Ki67 (≥20%) based on pathologist-annotated ground truth, defined as baseline Ki67 assessment by IHC. The area-under-the-curve (AUC) and associated 95%-confidence intervals (CI) were used to evaluate discrimination. To illustrate clinical utility, an exploratory decision threshold was estimated by maximizing the Youden Index in each validation dataset, providing estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: The AI-powered Ki67 classifier model demonstrated an AUC of 0.811 (95% CI: 0.791-0.827) in the test split and 0.842 (95% CI: 0.818-0.868) in external validation. Using the exploratory optimal threshold of 0.418 in the test split, the sensitivity and specificity for determining high versus low Ki-67 expression were 77% and 70% respectively. Using the exploratory optimal threshold of 0.616 in the external validation dataset, sensitivity was 73% and specificity was 81%. In the test split and external validation dataset, the PPV was 72% and 64% respectively, and NPV was 75% and 87% respectively. Conclusions: This novel approach to classifying Ki67 directly from H&E-stained slides offers a promising automated solution to streamline diagnostic workflows and enables more accurate, reproducible Ki67 assessment. Ongoing efforts are focused on refining and validating this model to enhance its clinical utility and applicability.
