Cystatin C deficiency in human atherosclerosis and aortic aneurysms

The pathogenesis of atherosclerosis and abdominal aortic aneurysm involves breakdown of the elastic laminae. Elastolytic cysteine proteases, including cathepsins S and K, are overexpressed at sites of arterial elastin damage, but whether endogenous local inhibitors counterbalance these proteases is unknown. We show here that, whereas cystatin C is normally expressed in vascular wall smooth muscle cells (SMCs), this cysteine protease inhibitor is severely reduced in both atherosclerotic and aneurysmal aortic lesions. Furthermore, increased abdominal aortic diameter among 122 patients screened by ultrasonography correlated inversely with serum cystatin C levels. In vitro, cytokine-stimulated vascular SMCs secrete cathepsins, whose elastolytic activity could be blocked when cystatin C secretion was induced by treatment with TGF-β1. The findings highlight a potentially important role for imbalance between cysteine proteases and cystatin C in arterial wall remodeling and establish that cystatin C deficiency occurs in vascular disease

February 1967 Conference Issue

Massachusetts Turf and Lawn Grass CouncilBetter Turf Through Research and Educatio

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training

Vilhelm Lundstedt’s ‘Legal Machinery’ and the Demise of Juristic Practice

This article aims to contribute to the academic debate on the general crisis faced by law schools and the legal professions by discussing why juristic practice is a matter of experience rather than knowledge. Through a critical contextualisation of Vilhelm Lundstedt’s thought under processes of globalisation and transnationalism, it is argued that the demise of the jurist’s function is related to law’s scientification as brought about by the metaphysical construction of reality. The suggested roadmap will in turn reveal that the current voiding of juristic practice and its teaching is part of the crisis regarding what makes us human

Gatecliff Shelter

552 p. : ill., maps ; 26 cm.Includes bibliographical references (p. 530-552)

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts

All-flavor constraints on nonstandard neutrino interactions and generalized matter potential with three years of IceCube DeepCore data

We report constraints on nonstandard neutrino interactions (NSI) from the observation of atmospheric neutrinos with IceCube, limiting all individual coupling strengths from a single dataset. Furthermore, IceCube is the first experiment to constrain flavor-violating and nonuniversal couplings simultaneously. Hypothetical NSI are generically expected to arise due to the exchange of a new heavy mediator particle. Neutrinos propagating in matter scatter off fermions in the forward direction with negligible momentum transfer. Hence the study of the matter effect on neutrinos propagating in the Earth is sensitive to NSI independently of the energy scale of new physics. We present constraints on NSI obtained with an all-flavor event sample of atmospheric neutrinos based on three years of IceCube DeepCore data. The analysis uses neutrinos arriving from all directions, with reconstructed energies between 5.6 GeV and 100 GeV. We report constraints on the individual NSI coupling strengths considered singly, allowing for complex phases in the case of flavor-violating couplings. This demonstrates that IceCube is sensitive to the full NSI flavor structure at a level competitive with limits from the global analysis of all other experiments. In addition, we investigate a generalized matter potential, whose overall scale and flavor structure are also constrained

Searching for High-energy Neutrino Emission from Galaxy Clusters with IceCube

Galaxy clusters have the potential to accelerate cosmic rays (CRs) to ultrahigh energies via accretion shocks or embedded CR acceleration sites. The CRs with energies below the Hillas condition will be confined within the cluster and eventually interact with the intracluster medium gas to produce secondary neutrinos and gamma rays. Using 9.5 yr of muon neutrino track events from the IceCube Neutrino Observatory, we report the results of a stacking analysis of 1094 galaxy clusters with masses ≳10$^{14}$ M⊙ and redshifts between 0.01 and ∼1 detected by the Planck mission via the Sunyaev–Zel’dovich effect. We find no evidence for significant neutrino emission and report upper limits on the cumulative unresolved neutrino flux from massive galaxy clusters after accounting for the completeness of the catalog up to a redshift of 2, assuming three different weighting scenarios for the stacking and three different power-law spectra. Weighting the sources according to mass and distance, we set upper limits at a 90% confidence level that constrain the flux of neutrinos from massive galaxy clusters (≳10$^{14}$ M⊙) to be no more than 4.6% of the diffuse IceCube observations at 100 TeV, assuming an unbroken E−$^{2.5}$ power-law spectrum