The strangest proton?

We present an improved determination of the strange quark and antiquark parton distribution functions of the proton by means of a global QCD analysis that takes into account a comprehensive set of strangeness-sensitive measurements: charm-tagged cross sections for fixed-target neutrino–nucleus deep-inelastic scattering, and cross sections for inclusive gauge-boson production and W-boson production in association with light jets or charm quarks at hadron colliders. Our analysis is accurate to next-to-next-to-leading order in perturbative QCD where available, and specifically includes charm-quark mass corrections to neutrino–nucleus structure functions. We find that a good overall description of the input dataset can be achieved and that a strangeness moderately suppressed in comparison to the rest of the light sea quarks is strongly favored by the global analysis

Circulating AQP4 Levels in Patients with Cerebral Amyloid Angiopathy-Associated Intracerebral Hemorrhage

Cerebral amyloid angiopathy (CAA) is a major cause of lobar intracerebral hemorrhage (ICH) in elderly patients. Growing evidence suggests a potential role of aquaporin 4 (AQP4) in amyloid-beta-associated diseases, including CAA pathology. Our aim was to investigate the circulating levels of AQP4 in a cohort of patients who had suffered a lobar ICH with a clinical diagnosis of CAA. AQP4 levels were analyzed in the serum of 60 CAA-related ICH patients and 19 non-stroke subjects by enzyme-linked immunosorbent assay (ELISA). The CAA-ICH cohort was divided according to the time point of the functional outcome evaluation: mid-term (12 +/- 18.6 months) and long-term (38.5 +/- 32.9 months) after the last ICH. Although no differences were found in AQP4 serum levels between cases and controls, lower levels were found in CAA patients presenting specific hemorrhagic features such as >= 2 lobar ICHs and >= 5 lobar microbleeds detected by magnetic resonance imaging (MRI). In addition, CAA-related ICH patients who presented a long-term good functional outcome had higher circulating AQP4 levels than subjects with a poor outcome or controls. Our data suggest that AQP4 could potentially predict a long-term functional outcome and may play a protective role after a lobar ICH

Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The 'typical' lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

The strangest proton?

We present an improved determination of the strange quark and antiquark parton distribution functions of the proton by means of a global QCD analysis that takes into account a comprehensive set of strangeness-sensitive measurements: charm-tagged cross sections for fixed-target neutrino–nucleus deep-inelastic scattering, and cross sections for inclusive gauge-boson production and W-boson production in association with light jets or charm quarks at hadron colliders. Our analysis is accurate to next-to-next-to-leading order in perturbative QCD where available, and specifically includes charm-quark mass corrections to neutrino–nucleus structure functions. We find that a good overall description of the input dataset can be achieved and that a strangeness moderately suppressed in comparison to the rest of the light sea quarks is strongly favored by the global analysis

The strangest proton?

We present an improved determination of the strange quark and antiquark parton distribution functions of the proton by means of a global QCD analysis that takes into account a comprehensive set of strangeness-sensitive measurements: charm-tagged cross sections for fixed-target neutrino–nucleus deep-inelastic scattering, and cross sections for inclusive gauge-boson production and W-boson production in association with light jets or charm quarks at hadron colliders. Our analysis is accurate to next-to-next-to-leading order in perturbative QCD where available, and specifically includes charm-quark mass corrections to neutrino–nucleus structure functions. We find that a good overall description of the input dataset can be achieved and that a strangeness moderately suppressed in comparison to the rest of the light sea quarks is strongly favored by the global analysis