11 research outputs found
Social Media, Gender and the Mediatisation of War: Exploring the German Armed Forces’ Visual Representation of the Afghanistan Operation on Facebook
Studies on the mediatisation of war point to attempts of governments to regulate the visual perspective of their involvements in armed conflict – the most notable example being the practice of ‘embedded reporting’ in Iraq and Afghanistan. This paper focuses on a different strategy of visual meaning-making, namely, the publication of images on social media by armed forces themselves. Specifically, we argue that the mediatisation of war literature could profit from an increased engagement with feminist research, both within Critical Security/Critical Military Studies and within Science and Technology Studies that highlight the close connection between masculinity, technology and control. The article examines the German military mission in Afghanistan as represented on the German armed forces’ official Facebook page. Germany constitutes an interesting, and largely neglected, case for the growing literature on the mediatisation of war: its strong antimilitarist political culture makes the representation of war particularly delicate. The paper examines specific representational patterns of Germany’s involvement in Afghanistan and discusses the implications which arise from what is placed inside the frame of visibility and what remains out of its view
Building a better foundation: improving root-trait measurements to understand and model plant and ecosystem processes
Trait-based approaches provide a useful framework to investigate plant strategies for resource acquisition, growth, and competition, as well as plant impacts on ecosystem processes. Despite significant progress capturing trait variation within and among stems and leaves, identification of trait syndromes within fine-root systems and between fine roots and other plant organs is limited. Here we discuss three underappreciated areas where focused measurements of fine-root traits can make significant contributions to ecosystem science. These include assessment of spatiotemporal variation in fine-root traits, integration of mycorrhizal fungi into fine-root-trait frameworks, and the need for improved scaling of traits measured on individual roots to ecosystem-level processes. Progress in each of these areas is providing opportunities to revisit how below-ground processes are represented in terrestrial biosphere models. Targeted measurements of fine-root traits with clear linkages to ecosystem processes and plant responses to environmental change are strongly needed to reduce empirical and model uncertainties. Further identifying how and when suites of root and whole-plant traits are coordinated or decoupled will ultimately provide a powerful tool for modeling plant form and function at local and global scales
Environmental impact and performance assessment of a new Zigbee‐based shotgun training system
Maspin expression in CIN 3, microinvasive squamous cell carcinoma, and invasive squamous cell carcinoma of the uterine cervix
Jealous and Violent: Constraining and Celebrating Wifely Jealousy in Mid-to-late Ming China
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Gliomas are the most common primary brain malignancies, with different
degrees of aggressiveness, variable prognosis and various heterogeneous
histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic
core, active and non-enhancing core. This intrinsic heterogeneity is also
portrayed in their radio-phenotype, as their sub-regions are depicted by
varying intensity profiles disseminated across multi-parametric magnetic
resonance imaging (mpMRI) scans, reflecting varying biological properties.
Their heterogeneous shape, extent, and location are some of the factors that
make these tumors difficult to resect, and in some cases inoperable. The amount
of resected tumor is a factor also considered in longitudinal scans, when
evaluating the apparent tumor for potential diagnosis of progression.
Furthermore, there is mounting evidence that accurate segmentation of the
various tumor sub-regions can offer the basis for quantitative image analysis
towards prediction of patient overall survival. This study assesses the
state-of-the-art machine learning (ML) methods used for brain tumor image
analysis in mpMRI scans, during the last seven instances of the International
Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we
focus on i) evaluating segmentations of the various glioma sub-regions in
pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue
of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO
criteria, and iii) predicting the overall survival from pre-operative mpMRI
scans of patients that underwent gross total resection. Finally, we investigate
the challenge of identifying the best ML algorithms for each of these tasks,
considering that apart from being diverse on each instance of the challenge,
the multi-institutional mpMRI BraTS dataset has also been a continuously
evolving/growing dataset