An Early Reduction in Treg Cells Correlates with Enhanced Local Inflammation in Cutaneous Leishmaniasis in CCR6-Deficient Mice

Resistance to Leishmania major infection is dependent on the development of a cell-mediated Th1 immune response in resistant C57BL/6 mice whereas Th2-prone BALB/c mice develop non-healing lesions after infection. The chemokine receptor CCR6 is shared by anti-inflammatory regulatory T cells and pro-inflammatory Th17 cells. In a recent study we showed that C57BL/6 mice deficient in CCR6 exhibited enhanced footpad swelling and impaired T helper cell migration indicated by reduced recruitment of total T helper cells into the skin after infection and a reduced delayed type hypersensitivity reaction. Based on these findings we tested whether the lack of CCR6 alters Treg or Th17 cell responses during the course of Leishmania major infection. When we analyzed T cell subsets in the lymph nodes of CCR6-deficient mice, Th17 cell numbers were not different. However, reduced numbers of Treg cells paralleled with a stronger IFNγ response. Furthermore, the early increase in IFNγ-producing cells correlated with increased local tissue inflammation at later time points. Our data indicate an important role of CCR6 for Treg cells and a redundant role for Th17 cells in a Th1 cell-driven anti-parasitic immune response against Leishmania major parasites in resistant C57BL/6 mice

Climate model and proxy data constraints on ocean warming across the Paleocene-Eocene Thermal Maximum

Constraining the greenhouse gas forcing, climatic warming and estimates of climate sensitivity across ancient large transient warming events is a major challenge to the palaeoclimate research community. Here we provide a new compilation and synthesis of the available marine proxy temperature data across the largest of these hyperthermals, the Paleocene-Eocene Thermal Maximum (PETM). This includes the application of consistent temperature calibrations to all data, including the most recent set of calibrations for archaeal lipid-derived palaeothermometry. This compilation provides the basis for an informed discussion of the likely range of PETM warming, the biases present in the existing record and an initial assessment of the geographical pattern of PETM ocean warming. To aid interpretation of the geographic variability of the proxy-derived estimates of PETM warming, we present a comparison of this data with the patterns of warming produced by high pCO2 simulations of Eocene climates using the Hadley Centre atmosphere-ocean general circulation model (AOGCM) HadCM3L. On the basis of this comparison and taking into account the patterns of intermediate-water warming we estimate that the global mean surface temperature anomaly for the PETM is within the range of 4 to 5°C

Deep Visual-Genetic Biometrics for Taxonomic Classification of Rare Species

Visual as well as genetic biometrics are routinely employed to identify species and individuals in biological applications. However, no attempts have been made in this domain to computationally enhance visual classification of rare classes with little image data via genetics. In this paper, we thus propose aligned visual-genetic learning as a new application domain with the aim to implicitly encode cross-modality associations for improved performance. We demonstrate for the first time that such alignment can be achieved via deep embedding models and that the approach is directly applicable to boosting long-tailed recognition (LTR), particularly for rare species. We experimentally demonstrate the efficacy of the concept via application to microscopic imagery of 30k+ planktic foraminifer shells across 32 species when used together with independent genetic data samples. Most importantly for practitioners, we show that visual-genetic alignment can significantly benefit visual-only recognition of the rarest species. Technically, we pre-train a visual ResNet50 deep learning model using triplet loss formulations to create an initial embedding space. We re-structure this space based on genetic anchors embedded via a Sequence Graph Transform (SGT) and linked to visual data by cross-domain cosine alignment. We show that an LTR approach improves the state-of-the-art across all benchmarks and that adding our visual-genetic alignment improves per-class and particularly rare tail class benchmarks significantly further. Overall, visual-genetic LTR training raises rare per-class accuracy from 37.4% to benchmark-beating 59.7%. We conclude that visual-genetic alignment can be a highly effective tool for complementing visual biological data containing rare classes. The concept proposed may serve as an important future tool for integrating genetics and imageomics towards a more complete scientific representation of taxonomic spaces and life itself. Code, weights, and data splits are published for full reproducibility

Early Memories of Individuals on the Autism Spectrum Assessed Using Online Self-Reports

"When I was one and a half years old, I was on a ferry lying on red seats" - while several autobiographical accounts by people with autism reveal vivid memories of early childhood, the vast amount of experimental investigations found deficits in personal autobiographic memory in autism. To assess this contradiction empirically, we implemented an online questionnaire on early childhood events to compare people on the autism spectrum (AS) and non-autistic people with respect to their earliest autobiographical episodic memories and the earliest semantic know event as told by another person. Results indicate that people on the AS do not differ from non-autistic people in the age of their earliest know events but remember events from an earlier age in childhood and with more sensory details, contradicting the assumption of an overall deficit in personal episodic memory in autism. Furthermore, our results emphasize the supporting influence of language for memory formation and give evidence for an important role of sensory features in memories of people on the AS.publishe