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Age-related macular degeneration (AMD) is the leading cause of blindness in the Western world. AMD is a multifactorial disorder but complement-mediated inflammation at the level of the retina plays a pivotal role. Oral zinc supplementation can reduce the progression of AMD but the precise mechanism of this protective effect is as yet unclear. We investigated whether zinc supplementation directly affects the degree of complement activation in AMD and whether there is a relation between serum complement catabolism during zinc administration and the complement factor H (CFH) gene or the Age-Related Maculopathy susceptibility 2 (ARMS2) genotype. In this open-label clinical study, 72 randomly selected AMD patients in various stages of AMD received a daily supplement of 50 mg zinc sulphate and 1 mg cupric sulphate for three months. Serum complement catabolism-defined as the C3d/C3 ratio-was measured at baseline, throughout the three months of supplementation and after discontinuation of zinc administration. Additionally, downstream inhibition of complement catabolism was evaluated by measurement of anaphylatoxin C5a. Furthermore, we investigated the effect of zinc on complement activation in vitro. AMD patients with high levels of complement catabolism at baseline exhibited a steeper decline in serum complement activation (p<0.001) during the three month zinc supplementation period compared to patients with low complement levels. There was no significant association of change in complement catabolism and CFH and ARMS2 genotype. In vitro zinc sulphate directly inhibits complement catabolism in hemolytic assays and membrane attack complex (MAC) deposition on RPE cells. This study provides evidence that daily administration of 50 mg zinc sulphate can inhibit complement catabolism in AMD patients with increased complement activation. This could explain part of the mechanism by which zinc slows AMD progression. The Netherlands National Trial Register NTR2605.

This study aimed to systematically investigate the relation between gross motor skills and aspects of executive functioning (i.e. verbal working memory, visuospatial working memory, response inhibition and interference control) in 8-10 year old children. Additionally, the role of information processing (speed and variability) and lapses of attention in the relation between gross motor skills and executive functions was investigated. Data of 732 Dutch children from grade 3 and 4 were analyzed (50.0% boys, 50.4% grade 3, age = 9.16 ± 0.64 years). Gross motor skills were assessed using three items of the Körper Koordinationstest für Kinder and one item of the Bruininks-Oseretsky test of Motor Proficiency, Second Edition. Executive functions were assessed using the Wechsler Digit Span task (verbal working memory), the Visuospatial Memory task (visuospatial working memory), the Stop Signal task (response inhibition) and a modified version of the Flanker task (interference control). Information processing and lapses of attention were obtained by applying an ex-Gaussian analysis on go trials of the Stop Signal task. Multilevel regression analysis showed that gross motor skills were significantly related to verbal working memory, visuospatial working memory and response inhibition, but not to interference control. Lapses of attention was a significant predictor for all executive functions, whereas processing speed was not. Variability in processing speed was only predictive for visuospatial working memory. After controlling for information processing and lapses of attention, gross motor skills were only significantly related to visuospatial working memory and response inhibition. The results suggest that after controlling for information processing and lapses of attention, gross motor skills are related to aspects of executive functions that are most directly involved in, and share common underlying processes with, gross motor skills.

In this paper we present a computer aided detection (CAD) system for automated measurement of the fetal head circumference (HC) in 2D ultrasound images for all trimesters of the pregnancy. The HC can be used to estimate the gestational age and monitor growth of the fetus. Automated HC assessment could be valuable in developing countries, where there is a severe shortage of trained sonographers. The CAD system consists of two steps: First, Haar-like features were computed from the ultrasound images to train a random forest classifier to locate the fetal skull. Secondly, the HC was extracted using Hough transform, dynamic programming and an ellipse fit. The CAD system was trained on 999 images and validated on an independent test set of 335 images from all trimesters. The test set was manually annotated by an experienced sonographer and a medical researcher. The reference gestational age (GA) was estimated using the crown-rump length measurement (CRL). The mean difference between the reference GA and the GA estimated by the experienced sonographer was 0.8 ± 2.6, -0.0 ± 4.6 and 1.9 ± 11.0 days for the first, second and third trimester, respectively. The mean difference between the reference GA and the GA estimated by the medical researcher was 1.6 ± 2.7, 2.0 ± 4.8 and 3.9 ± 13.7 days. The mean difference between the reference GA and the GA estimated by the CAD system was 0.6 ± 4.3, 0.4 ± 4.7 and 2.5 ± 12.4 days. The results show that the CAD system performs comparable to an experienced sonographer. The presented system shows similar or superior results compared to systems published in literature. This is the first automated system for HC assessment evaluated on a large test set which contained data of all trimesters of the pregnancy.

To understand how compliance develops both in everyday and corporate environments, it is crucial to understand how different mechanisms work together to shape individuals’ (non)compliant behavior. Existing compliance studies typically focus on a subset of theories (i.e., rational choice theories, social theories, legitimacy theories, capacity theories, and opportunity theories) to understand how key variables from one or several of these theories shape individual compliance. The present study provides a first integrated understanding of compliance, rooted in complexity science, in which key elements from these theories are considered simultaneously, and their relations to compliance and each other are explored using network analysis. This approach is developed by analyzing online survey data (N = 562) about compliance with COVID-19 mitigation measures. Traditional regression analysis shows that elements from nearly all major compliance theories (except for social theories) are associated with compliance. The network analysis revealed groupings and interconnections of variables that did not track the existing compliance theories and point to a complexity overlooked in existing compliance research. These findings demonstrate a fundamentally different perspective on compliance, which moves away from traditional narrow, non-network approaches. Instead, they showcase a complexity science understanding of compliance, in which compliance is understood as a network of interacting variables derived from different theories that interact with compliance. This points to a new research agenda that is oriented on mapping compliance networks, and testing and modelling how regulatory and management interventions interact with each other and compliance within such networks.

Antibodies play a critical role in protection against influenza; yet titers and viral neutralization represent incomplete correlates of immunity. Instead, the ability of antibodies to leverage the antiviral power of the innate immune system has been implicated in protection from and clearance of influenza infection. Here, post-hoc analysis of the humoral immune response to influenza is comprehensively profiled in a cohort of vaccinated older adults (65 + ) monitored for influenza infection during the 2012/2013 season in the United States (NCT: 01427309). While robust humoral immune responses arose against the vaccine and circulating strains, influenza-specific antibody effector profiles differed in individuals that later became infected with influenza, who are deficient in NK cell activating antibodies to both hemagglutinin and neuraminidase, compared to individuals who remained uninfected. Furthermore, NK cell activation was strongly associated with the NK cell senescence marker CD57, arguing for the need for selective induction of influenza-specific afucosylated NK activating antibodies in older adults to achieve protection. High dose vaccination, currently used for older adults, was insufficient to generate this NK cell-activating humoral response. Next generation vaccines able to selectively bolster NK cell activating antibodies may be required to achieve protection in the setting of progressively senescent NK cells. Antibodies play a crucial role in protection from influenza virus infection, but functional details, particularly in older adults, are incomplete. Here the authors show that NK cell-activating antibodies are associated with protection from influenza infection in vaccinated older adults.

To prevent floods from becoming disasters, social vulnerability must be integrated into flood risk management. We advocate that the welfare of different societal groups should be included by adding recovery capacity, impacts of beyond-design events, and distributional impacts.To prevent floods from becoming disasters, social vulnerability must be integrated into flood risk management. This Comment advocates that the welfare of different societal groups should be included by adding recovery capacity, impacts of beyond-design events, and distributional impacts.

Characterizing and validating which mutations influence development of cancer is challenging. Artificial intelligence (AI) has delivered significant advances in protein structure prediction, but its utility for identifying cancer drivers is less explored. We evaluate multiple computational methods for identifying cancer driver mutations. For re-identifying known drivers, methods incorporating protein structure or functional genomic data outperform methods trained only on evolutionary data. We validate variants of unknown significance (VUSs) annotated as pathogenic by testing their association with overall survival in two cohorts of patients with non-small cell lung cancer ( N  = 7965 and 977). VUSs identified as pathogenic drivers by AI in KEAP1 and SMARCA4 are associated with worse survival, unlike “benign” VUSs. “Pathogenic” VUSs also exhibit mutual exclusivity with known oncogenic alterations at the pathway level, further suggesting biological validity. AI predictions thus contribute to a more comprehensive understanding of tumor genetics as validated by real-world data. It remains unclear whether machine learning methods can accurately identify cancer driver alterations. Here, the authors compare machine learning-based approaches to other computational methods to determine their utility for annotating variants of unknown significance and identifying driver alterations in real-world cancer patient data, demonstrating superior performance.

The animal-pathogenic oomycete Saprolegnia parasitica causes serious losses in aquaculture by infecting and killing freshwater fish. Like plant-pathogenic oomycetes, S. parasitica employs similar infection structures and secretes effector proteins that translocate into host cells to manipulate the host. Here, we show that the host-targeting protein SpHtp3 enters fish cells in a pathogen-independent manner. This uptake process is guided by a gp96-like receptor and can be inhibited by supramolecular tweezers. The C-terminus of SpHtp3 (containing the amino acid sequence YKARK), and not the N-terminal RxLR motif, is responsible for the uptake into host cells. Following translocation, SpHtp3 is released from vesicles into the cytoplasm by another host-targeting protein where it degrades nucleic acids. The effector translocation mechanism described here, is potentially also relevant for other pathogen–host interactions as gp96 is found in both animals and plants. The pathogenic oomycete Saprolegnia parasitica secretes effector proteins that translocate into host cells through unclear mechanisms. Here, Trusch et al. show that the uptake of effector protein SpHtp3, resulting in RNA degradation, depends on a gp96-like host receptor and a second effector protein.

Oomycetes in the class Saprolegniomycetidae of the Eukaryotic kingdom Stramenopila have evolved as severe pathogens of amphibians, crustaceans, fish and insects, resulting in major losses in aquaculture and damage to aquatic ecosystems. We have sequenced the 63 Mb genome of the fresh water fish pathogen, Saprolegnia parasitica. Approximately 1/3 of the assembled genome exhibits loss of heterozygosity, indicating an efficient mechanism for revealing new variation. Comparison of S. parasitica with plant pathogenic oomycetes suggests that during evolution the host cellular environment has driven distinct patterns of gene expansion and loss in the genomes of plant and animal pathogens. S. parasitica possesses one of the largest repertoires of proteases (270) among eukaryotes that are deployed in waves at different points during infection as determined from RNA-Seq data. In contrast, despite being capable of living saprotrophically, parasitism has led to loss of inorganic nitrogen and sulfur assimilation pathways, strikingly similar to losses in obligate plant pathogenic oomycetes and fungi. The large gene families that are hallmarks of plant pathogenic oomycetes such as Phytophthora appear to be lacking in S. parasitica, including those encoding RXLR effectors, Crinkler's, and Necrosis Inducing-Like Proteins (NLP). S. parasitica also has a very large kinome of 543 kinases, 10% of which is induced upon infection. Moreover, S. parasitica encodes several genes typical of animals or animal-pathogens and lacking from other oomycetes, including disintegrins and galactose-binding lectins, whose expression and evolutionary origins implicate horizontal gene transfer in the evolution of animal pathogenesis in S. parasitica.

The eukaryotic oomycetes, or water molds, contain several species that are devastating pathogens of plants and animals. During infection, oomycetes translocate effector proteins into host cells, where they interfere with host-defense responses. For several oomycete effectors (i.e., the RxLR-effectors) it has been shown that their N-terminal polypeptides are important for the delivery into the host. Here we demonstrate that the putative RxLR-like effector, host-targeting protein 1 (SpHtp1), from the fish pathogen Saprolegnia parasitica translocates specifically inside host cells. We further demonstrate that cell-surface binding and uptake of this effector protein is mediated by an interaction with tyrosine-O-sulfate-modified cell-surface molecules and not via phospholipids, as has been reported for RxLR-effectors from plant pathogenic oomycetes. These results reveal an effector translocation route based on tyrosine-O-sulfate binding, which could be highly relevant for a wide range of host-microbe interactions.