Not seeing the forest for the trees: combination of path integration and landmark cues in human virtual navigation.

Introduction: In order to successfully move from place to place, our brain often combines sensory inputs from various sources by dynamically weighting spatial cues according to their reliability and relevance for a given task. Two of the most important cues in navigation are the spatial arrangement of landmarks in the environment, and the continuous path integration of travelled distances and changes in direction. Several studies have shown that Bayesian integration of cues provides a good explanation for navigation in environments dominated by small numbers of easily identifiable landmarks. However, it remains largely unclear how cues are combined in more complex environments. Methods: To investigate how humans process and combine landmarks and path integration in complex environments, we conducted a series of triangle completion experiments in virtual reality, in which we varied the number of landmarks from an open steppe to a dense forest, thus going beyond the spatially simple environments that have been studied in the past. We analysed spatial behaviour at both the population and individual level with linear regression models and developed a computational model, based on maximum likelihood estimation (MLE), to infer the underlying combination of cues. Results: Overall homing performance was optimal in an environment containing three landmarks arranged around the goal location. With more than three landmarks, individual differences between participants in the use of cues are striking. For some, the addition of landmarks does not worsen their performance, whereas for others it seems to impair their use of landmark information. Discussion: It appears that navigation success in complex environments depends on the ability to identify the correct clearing around the goal location, suggesting that some participants may not be able to see the forest for the trees.

The Virtual Navigation Toolbox: Providing tools for virtual navigation experiments.

Spatial navigation research in humans increasingly relies on experiments using virtual reality (VR) tools, which allow for the creation of highly flexible, and immersive study environments, that can react to participant interaction in real time. Despite the popularity of VR, tools simplifying the creation and data management of such experiments are rare and often restricted to a specific scope-limiting usability and comparability. To overcome those limitations, we introduce the Virtual Navigation Toolbox (VNT), a collection of interchangeable and independent tools for the development of spatial navigation VR experiments using the popular Unity game engine. The VNT's features are packaged in loosely coupled and reusable modules, facilitating convenient implementation of diverse experimental designs. Here, we depict how the VNT fulfils feature requirements of different VR environments and experiments, guiding through the implementation and execution of a showcase study using the toolbox. The presented showcase study reveals that homing performance in a classic triangle completion task is invariant to translation velocity of the participant's avatar, but highly sensitive to the number of landmarks. The VNT is freely available under a creative commons license, and we invite researchers to contribute, extending and improving tools using the provided repository.

Combined sodium glucose co-transporter-2 inhibitor and angiotensin-converting enzyme inhibition upregulates the renin-angiotensin system in chronic kidney disease with type 2 diabetes: Results of a randomized, double-blind, placebo-controlled exploratory trial.

AIM: Sodium glucose co-transporter-2 inhibitors (SGLT-2i) improve cardiorenal outcomes in patients with chronic kidney disease (CKD), with and without type 2 diabetes. The molecular mechanisms underlying these pleiotropic effects remain unclear, yet it is speculated that SGLT-2i elicit a neurohormonal modulation resulting in renin-angiotensin system (RAS) activation. We hypothesized that combined SGLT-2 and angiotensin-converting enzyme inhibition (ACEi) favours RAS regulation towards the beneficial angiotensin-(1-7)-driven axis. MATERIALS AND METHODS: This randomized controlled prospective study investigated the effect of 12 weeks treatment with the SGLT-2i empagliflozin on top of ACEi on the molecular RAS dynamics in 24 diabetic and 24 non-diabetic patients with CKD. Systemic RAS peptides were quantified by mass spectrometry. RESULTS: In patients with type 2 diabetes, combined SGLT-2i and ACEi significantly upregulated plasma renin activity [pre-treatment median and interquartile range 298.0 (43.0-672.0) pmol/L versus post-treatment 577.0 (95.0-1543.0) pmol/L; p = .037] and angiotensin I levels [pre-treatment 289.0 (42.0-668.0) pmol/L versus post-treatment 573.0 (93.0-1522.0) pmol/L; p = .037], together with a significant increase of angiotensin-(1-7) levels [pre-treatment 14.0 (2.1-19.0) pmol/L versus post-treatment 32.0 (5.7-99.0) pmol/L; p = .012]. Empagliflozin treatment resulted in a 1.5 to 2-fold increase in main RAS peptides in patients with diabetes compared with placebo. No significant effect of empagliflozin on top of ACEi on RAS peptides was found in patients with CKD without diabetes. CONCLUSION: A distinct RAS modulation by SGLT-2i occurs in diabetic kidney disease reflected by enhancement of the beneficial angiotensin-(1-7) providing a molecular background for this renoprotective therapeutic approach.

Citizen science and online data: Opportunities and challenges for snake ecology and action against snakebite.

The secretive behavior and life history of snakes makes studying their biology, distribution, and the epidemiology of venomous snakebite challenging. One of the most useful, most versatile, and easiest to collect types of biological data are photographs, particularly those that are connected with geographic location and date-time metadata. Photos verify occurrence records, provide data on phenotypes and ecology, and are often used to illustrate new species descriptions, field guides and identification keys, as well as in training humans and computer vision algorithms to identify snakes. We scoured eleven online and two offline sources of snake photos in an attempt to collect as many photos of as many snake species as possible, and attempt to explain some of the inter-species variation in photograph quantity among global regions and taxonomic groups, and with regard to medical importance, human population density, and range size. We collected a total of 725,565 photos-between 1 and 48,696 photos of 3098 of the world's 3879 snake species (79.9%), leaving 781 "most wanted" species with no photos (20.1% of all currently-described species as of the December 2020 release of The Reptile Database). We provide a list of most wanted species sortable by family, continent, authority, and medical importance, and encourage snake photographers worldwide to submit photos and associated metadata, particularly of "missing" species, to the most permanent and useful online archives: The Reptile Database, iNaturalist, and HerpMapper.

Crowdbreaks: Tracking Health Trends Using Public Social Media Data and Crowdsourcing.

In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community. Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labeling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labeling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work describes the technical aspects of the platform, thereby covering the functionalities at its current state and exploring its future use cases and extensions.

Wet Markets and Food Safety: TripAdvisor for Improved Global Digital Surveillance.

BACKGROUND: Wet markets are markets selling fresh meat and produce. Wet markets are critical for food security and sustainable development in their respective regions. Due to their cultural significance, they attract numerous visitors and consequently generate tourist-geared information on the Web (ie, on social networks such as TripAdvisor). These data can be used to create a novel, international wet market inventory to support epidemiological surveillance and control in such settings, which are often associated with negative health outcomes. OBJECTIVE: Using social network data, we aimed to assess the level of wet markets' touristic importance on the Web, produce the first distribution map of wet markets of touristic interest, and identify common diseases facing visitors in these settings. METHODS: A Google search was performed on 31 food market-related keywords, with the first 150 results for each keyword evaluated based on their relevance to tourism. Of all these queries, wet market had the highest number of tourism-related Google Search results; among these, TripAdvisor was the most frequently-occurring travel information aggregator, prompting its selection as the data source for this study. A Web scraping tool (ParseHub) was used to extract wet market names, locations, and reviews from TripAdvisor. The latter were searched for disease-related content, which enabled assignment of GeoSentinel diagnosis codes to each. This syndromic categorization was overlaid onto a mapping of wet market locations. Regional prevalence of the most commonly occurring symptom group - food poisoning - was then determined (ie, by dividing the number of wet markets per continent with more than or equal to 1 review containing this syndrome by the total number of wet markets on that continent with syndromic information). RESULTS: Of the 1090 hits on TripAdvisor for wet market, 36.06% (393/1090) conformed to the query's definition; wet markets were heterogeneously distributed: Asia concentrated 62.6% (246/393) of them, Europe 19.3% (76/393), North America 7.9% (31/393), Oceania 5.1% (20/393), Africa 3.1% (12/393), and South America 2.0% (8/393). Syndromic information was available for 14.5% (57/393) of wet markets. The most frequently occurring syndrome among visitors to these wet markets was food poisoning, accounting for 54% (51/95) of diagnoses. Cases of this syndrome were identified in 56% (22/39) of wet markets with syndromic information in Asia, 71% (5/7) in Europe, and 71% (5/7) in North America. All wet markets in South America and Oceania reported food poisoning cases, but the number of reviews with syndromic information was very limited in these regions (n=2). CONCLUSIONS: The map produced illustrates the potential role of touristically relevant social network data to support global epidemiological surveillance. This includes the possibility to approximate the global distribution of wet markets and to identify diseases (ie, food poisoning) that are most prevalent in such settings.

Image statistics of the environment surrounding freely behaving hoverflies.

Natural scenes are not as random as they might appear, but are constrained in both space and time. The 2-dimensional spatial constraints can be described by quantifying the image statistics of photographs. Human observers perceive images with naturalistic image statistics as more pleasant to view, and both fly and vertebrate peripheral and higher order visual neurons are tuned to naturalistic image statistics. However, for a given animal, what is natural differs depending on the behavior, and even if we have a broad understanding of image statistics, we know less about the scenes relevant for particular behaviors. To mitigate this, we here investigate the image statistics surrounding Episyrphus balteatus hoverflies, where the males hover in sun shafts created by surrounding trees, producing a rich and dense background texture and also intricate shadow patterns on the ground. We quantified the image statistics of photographs of the ground and the surrounding panorama, as the ventral and lateral visual field is particularly important for visual flight control, and found differences in spatial statistics in photos where the hoverflies were hovering compared to where they were flying. Our results can, in the future, be used to create more naturalistic stimuli for experimenter-controlled experiments in the laboratory.

The problem of home choice in skyline-based homing.

Navigation in cluttered environments is an important challenge for animals and robots alike and has been the subject of many studies trying to explain and mimic animal navigational abilities. However, the question of selecting an appropriate home location has, so far, received only little attention. This is surprising, since the choice of a home location might greatly influence an animal's navigation performance. To address the question of home choice in cluttered environments, a systematic analysis of homing trajectories was performed by computer simulations using a skyline-based local homing method. Our analysis reveals that homing performance strongly depends on the location of the home in the environment. Furthermore, it appears that by assessing homing success in the immediate vicinity of the home, an animal might be able to predict its overall success in returning to it from within a much larger area.

17ß-estradiol protects 7-day old rats from acute brain injury and reduces the number of apoptotic cells.

OBJECTIVE: To test a possible neuroprotective activity of 17ß-estradiol in the neonatal rat brain exposed to hypoxic-ischemia (controlled hypoxia after unilateral carotid artery ligation). METHODS: Seven-day-old Wistar rats underwent ligation of the left common carotid artery followed by 80 minutes hypoxia in 8% oxygen inducing an ipsilateral brain damage. Seven days later (d14), brains were analyzed quantitatively using a macroscopic and microscopic score for structural damage, hemisphere volumes were calculated, and immunohistochemistry for cleaved-caspase-3 (marker for apoptotic cells) was performed. Animals from the study group (n = 19) received 17ß-estradiol (0.05 µg/g body weight intraperitoneally) before (-64, -40, and -16 hours) and after (+3 hours) the hypoxia (hour 0: start of the hypoxia) and the control group (n = 21) received mock treatment. RESULTS: Of the 21 pups, 13 in the NaCl group had macroscopically a severe brain damage and 7 of 19 animals in the study group encountered only discrete to mild lesions. Microscopic brain damage in the study group was significantly lower (score 1.5 ± 0.7 vs 2.8 ± 0.8, P < .05). The determined volumes of the affected hemisphere were significantly lower in the NaCl group than in the treatment group. The numbers of apoptotic cells in both hemispheres was equal in the estradiol group, but in the control group, there were significantly more apoptotic cells in the affected hemisphere (control group: ipsilateral: 1435 ± 653 vs contralateral: 143 ± 57 cells, P < .05). DISCUSSION: 17ß-Estradiol protects newborn rat brains from hypoxic-ischemic injury, in terms of both microscopic cell injury and apoptosis.

Aggregation and vesiculation of membrane proteins by curvature-mediated interactions.

Membrane remodelling plays an important role in cellular tasks such as endocytosis, vesiculation and protein sorting, and in the biogenesis of organelles such as the endoplasmic reticulum or the Golgi apparatus. It is well established that the remodelling process is aided by specialized proteins that can sense as well as create membrane curvature, and trigger tubulation when added to synthetic liposomes. Because the energy needed for such large-scale changes in membrane geometry significantly exceeds the binding energy between individual proteins and between protein and membrane, cooperative action is essential. It has recently been suggested that curvature-mediated attractive interactions could aid cooperation and complement the effects of specific binding events on membrane remodelling. But it is difficult to experimentally isolate curvature-mediated interactions from direct attractions between proteins. Moreover, approximate theories predict repulsion between isotropically curving proteins. Here we use coarse-grained membrane simulations to show that curvature-inducing model proteins adsorbed on lipid bilayer membranes can experience attractive interactions that arise purely as a result of membrane curvature. We find that once a minimal local bending is realized, the effect robustly drives protein cluster formation and subsequent transformation into vesicles with radii that correlate with the local curvature imprint. Owing to its universal nature, curvature-mediated attraction can operate even between proteins lacking any specific interactions, such as newly synthesized and still immature membrane proteins in the endoplasmic reticulum.