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Sex-biased infections are a recurrent observation in vertebrates. In many species, males are more parasitized than females. Two potentially complementary mechanisms are often suggested to explain this pattern: sexual differences in susceptibility mainly caused by the effect of sex hormones on immunity and differential exposure to parasites. Exposure is mostly a consequence of host behavioural traits, but vector-borne parasitic infections involve another degree of complexity due to the active role of vectors in transmission. Blood-sucking insects may make choices based on cues produced by hosts. Regarding malaria, several studies highlighted a male-biased infection by Plasmodium sp in great tits (Parus major). We hypothesize that the mosquito vector, Culex pipiens, might at least partially cause this bias by being more attracted to male birds. Intrinsic variation associated to bird sex would explain a preference of mosquitoes for males. To test this hypothesis, we provide uninfected mosquitoes with a choice between uninfected male and female nestlings. Mosquito choice is assessed by sex typing of the ingested blood. We did not observe any preference for a given sex. This result does not support our prediction of a preference of mosquitoes for male great tits during the nestling period. In conclusion, mosquitoes do not seem to have an intrinsic preference for male nestlings. However, sexually divergent traits (e.g. behaviour, odour, metabolic rate) present in adults may play a role in the attraction of mosquitoes and should be investigated.

[This corrects the article DOI: 10.1371/journal.pone.0216360.].

Cytosolic carboxypeptidase 1 (CCP1) is a metallopeptidase that removes C-terminal and side-chain glutamates from tubulin. The Purkinje cell degeneration (pcd) mouse lacks CCP1 due to a mutation. Previously, elevated levels of peptides derived from cytosolic and mitochondrial proteins were found in adult pcd mouse brain, raising the possibility that CCP1 functions in the degradation of intracellular peptides. To test this hypothesis, we used a quantitative peptidomics technique to compare peptide levels in wild-type and pcd mice, examining adult heart, spleen, and brain, and presymptomatic 3 week-old amygdala and cerebellum. Contrary to adult mouse brain, young pcd brain and adult heart and spleen did not show a large increase in levels of intracellular peptides. Unexpectedly, levels of peptides derived from secretory pathway proteins were altered in adult pcd mouse brain. The pattern of changes for the intracellular and secretory pathway peptides in pcd mice was generally similar to the pattern observed in mice lacking primary cilia. Collectively, these results suggest that intracellular peptide accumulation in adult pcd mouse brain is a secondary effect and is not due to a role of CCP1 in peptide turnover.

In March 2020, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. At the genus rank, 20 new genera were added, two were deleted, one was moved, and three were renamed. At the species rank, 160 species were added, four were deleted, ten were moved and renamed, and 30 species were renamed. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV.

In March 2022, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by two new families (bunyaviral Discoviridae and Tulasviridae), 41 new genera, and 98 new species. Three hundred forty-nine species were renamed and/or moved. The accidentally misspelled names of seven species were corrected. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV.

In March 2021, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by four families (Aliusviridae, Crepuscuviridae, Myriaviridae, and Natareviridae), three subfamilies (Alpharhabdovirinae, Betarhabdovirinae, and Gammarhabdovirinae), 42 genera, and 200 species. Thirty-nine species were renamed and/or moved and seven species were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV.

We propose a learning approach to corner detection for event-based cameras that is stable even under fast and abrupt motions. Event-based cameras offer high temporal resolution, power efficiency, and high dynamic range. However, the properties of event-based data are very different compared to standard intensity images, and simple extensions of corner detection methods designed for these images do not perform well on event-based data. We first introduce an efficient way to compute a time surface that is invariant to the speed of the objects. We then show that we can train a Random Forest to recognize events generated by a moving corner from our time surface. Random Forests are also extremely efficient, and therefore a good choice to deal with the high capture frequency of event-based cameras ---our implementation processes up to 1.6Mev/s on a single CPU. Thanks to our time surface formulation and this learning approach, our method is significantly more robust to abrupt changes of direction of the corners compared to previous ones. Our method also naturally assigns a confidence score for the corners, which can be useful for postprocessing. Moreover, we introduce a high-resolution dataset suitable for quantitative evaluation and comparison of corner detection methods for event-based cameras. We call our approach SILC, for Speed Invariant Learned Corners, and compare it to the state-of-the-art with extensive experiments, showing better performance.

Event-based cameras have recently drawn the attention of the Computer Vision community thanks to their advantages in terms of high temporal resolution, low power consumption and high dynamic range, compared to traditional frame-based cameras. These properties make event-based cameras an ideal choice for autonomous vehicles, robot navigation or UAV vision, among others. However, the accuracy of event-based object classification algorithms, which is of crucial importance for any reliable system working in real-world conditions, is still far behind their frame-based counterparts. Two main reasons for this performance gap are: 1. The lack of effective low-level representations and architectures for event-based object classification and 2. The absence of large real-world event-based datasets. In this paper we address both problems. First, we introduce a novel event-based feature representation together with a new machine learning architecture. Compared to previous approaches, we use local memory units to efficiently leverage past temporal information and build a robust event-based representation. Second, we release the first large real-world event-based dataset for object classification. We compare our method to the state-of-the-art with extensive experiments, showing better classification performance and real-time computation.