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Humans can easily abstract incoming visual information into discrete semantic categories. Previous research employing functional MRI (fMRI) in humans has identified cortical organizing principles that allow not only for coarse-scale distinctions such as animate versus inanimate objects but also more fine-grained distinctions at the level of individual objects. This suggests that fMRI carries rather fine-grained information about individual objects. However, most previous work investigating fine-grained category representations either additionally included coarse-scale category comparisons of objects, which confounds fine-grained and coarse-scale distinctions, or only used a single exemplar of each object, which confounds visual and semantic information. To address these challenges, here we used multisession human fMRI (female and male) paired with a broad yet homogenous stimulus class of 48 terrestrial mammals, with two exemplars per mammal. Multivariate decoding and representational similarity analysis revealed high imagespecific reliability in low- and high-level visual regions, indicating stable representational patterns at the image level. In contrast, analyses across exemplars of the same animal yielded only small effects in the lateral occipital complex (LOC), indicating rather subtle category effects in this region. Variance partitioning with a deep neural network and shape model showed that acrossexemplar effects in the early visual cortex were largely explained by low-level visual appearance, while representations in LOC appeared to also contain higher category-specific information. These results suggest that representations typically measured with fMRI are dominated by image-specific visual or coarse-grained category information but indicate that commonly employed fMRI protocols may reveal subtle yet reliable distinctions between individual objects. [ABSTRACT FROM AUTHOR]

Reversible protein patterning on model membranes is important to reproduce spatiotemporal protein dynamics in vitro. An engineered version of iLID, disiLID, with a disordered domain as a membrane tether improves the recruitment of Nano under blue light and the reversibility in the dark, which enables protein patterning on membranes with higher spatiotemporal precision. [ABSTRACT FROM AUTHOR]

African swine fever (ASF) is an internationally-spreading viral pig disease that severely damages agricultural pork production and trade economy as well as social welfare in disease-affected regions. A comprehensive understanding of ASF risk factors is imperative for efficient disease control. As the absence of effective ASF vaccines limits disease management options, the identification and minimisation of ASF-associated risk factors is critical to preventing ASF outbreaks. Here, we compile currently known potential ASF risk factors identified through a systematic literature review. We found 154 observation-based and 1239 potential ASF risk factors, which we were able to group into the following defined risk categories: 'ASF-virus', 'Biosecurity', 'Disease control', 'Environment', 'Husbandry', 'Movement', 'Network', 'Pig', 'Society' and 'Surveillance'. Throughout the epidemiological history of ASF there have been similar risk categories, such as 'Environment'-related risk factors, predominantly reported in the literature irrespective of the ASF situation at the time. While ASF risk factor reporting has markedly increased since 2010, the majority of identified risk factors overall have referred to domestic pigs. The reporting of risk factors for ASF in wild boar mostly commenced from 2016 onwards. The compendium of ASF risk factors presented herein defines our current knowledge of ASF risk factors, and critically informs ASF-related problem solving. [ABSTRACT FROM AUTHOR]

To facilitate cross‐sector integration of surveillance data it is necessary to improve and harmonize the meta‐information provided in surveillance data reports. Cross‐sector integration of surveillance results in sector‐specific reports is frequently difficult as reports with a focus on a single sector often lack aspects of the relevant meta‐information necessary to clarify the surveillance context. Such reporting deficiencies reduce the value of surveillance reports to the One Health community. The One Health Consensus Report Annotation Checklist (OH‐CRAC), described in this paper along with potential application scenarios, was developed to improve the current practice of annotating data presented in surveillance data reports. It aims to provide guidance to researchers and reporting officers on what meta‐information should be collected and provided to improve the completeness and transparency of surveillance data reports. The OH‐CRAC can be adopted by all One Health‐related sectors and due to its cross‐sector design, it supports the mutual mapping of surveillance meta‐information from sector‐specific surveillance reports on federal, national and international levels. To facilitate the checklist completion, OH‐CRAC is also available as an online resource that allows the collection of surveillance meta‐information in an easy and user‐friendly manner. Completed OH‐CRAC checklists can be attached as annexes to the corresponding surveillance data reports or even to individual data files regardless of the data source. In this way, reports and data become better interpretable, usable and comparable to information from other sectors, improving their value for all surveillance actors and providing a better foundation for advice to risk managers. [ABSTRACT FROM AUTHOR]

Mental imagery provides an essential simulation tool for remembering the past and planning the future, with its strength affecting both cognition and mental health. Research suggests that neural activity spanning prefrontal, parietal, temporal, and visual areas supports the generation of mental images. Exactly how this network controls the strength of visual imagery remains unknown. Here, brain imaging and transcranial magnetic phosphene data show that lower resting activity and excitability levels in early visual cortex (V1-V3) predict stronger sensory imagery. Further, electrically decreasing visual cortex excitability using tDCS increases imagery strength, demonstrating a causative role of visual cortex excitability in controlling visual imagery. Together, these data suggest a neurophysiological mechanism of cortical excitability involved in controlling the strength of mental images., Competing Interests: RK, JB, JP No competing interests declared, (© 2020, Keogh et al.)