For their analysis, the authors compiled information on African ape occurrence held in the IUCN SSC A.P.E.S. database, a repository that includes a remarkable amount of information on population status, threats and conservation for several hundred sites, collected over 20 years.

The first-of-its-kind study quantifies the joint effects of climate, land-use, and human population changes across African ape ranges for the year 2050 under best- and worst-case scenarios. “Best case” implies slowly declining carbon emissions and that appropriate mitigation measures will be put in place. “Worst case” assumes that emissions continue to increase unchecked — business as usual.

Under the best-case scenario, the authors predict that great apes will lose 85 percent of their range, of which 50 percent will be outside national parks and other areas protected by legislation. Under the worst-case scenario, they predict a 94 percent loss, of which 61 percent will be in areas that are not protected.

This paper examines whether great apes can or cannot disperse away from where they are currently found, and the best- and worst-case scenarios in each case. For example, mountains are currently less suitable than lowland areas for some great ape species. However, climate change will render some lowlands less suitable — warmer, drier, perhaps less food available — but the nearby mountains will take on the characteristics that those lowlands currently have. If great apes are able to physically move from the lowlands to the mountains, they may be able to survive, and even increase their range (depending on the species, and whether it is the best- or worst-case scenario). However, they may not be able to travel (disperse) away from the lowlands in the time remaining between today and 2050.

Joana Carvalho, postdoctoral researcher in the Faculty of Science, Liverpool John Moores University, lead author of the study says: “By integrating future climate and land-use changes as well as human population scenarios, this study provides strong evidence for synergistic interactions among key global drivers constraining African ape distribution.”

Carvalho adds: “Importantly, massive range loss is widely expected outside protected areas, which reflects the insufficiency of the current network of protected areas in Africa to preserve suitable habitats for great apes and effectively connect great ape populations.”

Fiona Maisels of the Wildlife Conservation Society (WCS), and a co-author of the study, said: “As climate change forces the different types of vegetation to essentially shift uphill, it means that all animals — not only great apes — that depend on particular habitat types will be forced to move uphill along with the vegetation, or become locally extinct. And when the hills are low, many species, great and small, will not be able go higher than the land allows, and huge numbers of animals and plants will simply vanish.”

The authors argue that effective conservation strategies require careful planning for each species that focuses on both existing and proposed protected areas — the creation and management of which can be informed by these habitat suitability models. Additionally, efforts to maintain connectivity between the habitats predicted to be suitable in the future will be crucial for the survival of African apes. Conservation planners urgently need to integrate land-use planning and climate change mitigation and adaptation measures into government policy of great ape range countries.

The study highlights the need for urgent action to combat both biodiversity loss and climate change if great apes are to continue into the future. Governments must protect and conserve the habitats of great apes — where they are now, and where they will need to move. Governments attending the upcoming Convention on Biological Diversity CoP 15 in September and the UN Climate Change Conference in November should adopt meaningful commitments to protect and conserve great apes and their habitats and combat climate change.

The results of the study corroborate other recent studies showing that African ape populations and their habitats are declining dramatically. All African great apes are classified either as Endangered (mountain gorillas, bonobos, Nigeria-Cameroon chimpanzees, eastern chimpanzees, and central chimpanzees) or Critically Endangered (Cross River gorillas, Grauer’s gorillas, western lowland gorillas, and western chimpanzees) on the IUCN Red List of Threatened Species, and all are regarded as flagship species for conservation.

Hjalmar Kuehl, from iDiv in Leipzig, Germany, and senior author of the study said: “There must be global responsibility for stopping the decline of great apes. Global consumption of natural resources extracted from ape range countries is a major driver of great ape decline. All nations benefitting from these resources have a responsibility to ensure a better future for great apes, their habitats and the people living therein by developing more sustainable economies.”

We have become accustomed to online algorithms trying to guess our preferences for everything from movies and music to news and shopping. This is based not only on what we have searched for, looked at, or listened to, but also on how these activities compare to others. Collaborative filtering, as the technique is called, uses hidden patterns in our behavior and the behavior of others to predict which things we may find interesting or appealing.

But what if the algorithms could use responses from our brain rather than just our behavior? It may sound a bit like science fiction, but a project combining computer science and cognitive neuroscience showed that brain-based collaborative filtering is indeed possible. By using an algorithm to match an individual’s pattern of brain responses with those of others, researchers from the University of Copenhagen and the University of Helsinki were able to predict a person’s attraction to a not-yet-seen face.

Previously the researchers had placed EEG electrodes onto the heads of study participants and showed them images of various faces, and thereby demonstrated that machine learning can use electrical activity from the brain to detect which faces the subjects found most attractive.

“Through comparing the brain activity of others, we’ve now also found it possible to predict faces each participant would find appealing prior to seeing them. In this way, we can make reliable recommendations for users — just as streaming services suggest new films or series based on the history of the users,” explains senior author Dr. Tuukka Ruotsalo of the University of Copenhagen’s Department of Computer Science.

Towards mindful computing and greater self-awareness

Industries and service providers are more and more often giving personalized recommendations and we are now starting to expect individually tailored content from them. Consequently, researchers and industries are interested in developing more accurate techniques of satisfying this demand. However, the current collaborative filtering techniques which are based on explicit behaviour in terms of ratings, click behaviour, content sharing etc. are not always reliable methods of revealing our real and underlying preferences.

“Due to social norms or other factors, users may not reveal their actual preferences through their behaviour online. Therefore, explicit behaviour may be biased. The brain signals we investigated were picked up very early after viewing, so they are more related to immediate impressions than carefully considered behaviour,” explains co-author Dr. Michiel Spapé.

“The electrical activity in our brains is an alternative and rather untapped source of information. In the longer term, the method can probably be used to provide much more nuanced information about people’s preferences than is possible today. This could be to decode the underlying reasons for a person’s liking of certain songs — which could be related to the emotions that they evoke,” explains Tuukka Ruotsalo.

But researchers don’t just see the new method as a useful way for advertisers and streaming services to sell products or retain users. As lead author Keith Davis points out:

“I consider our study as a step towards an era that some refer to as “mindful computing,” in which, by using a combination of computers and neuroscience techniques, users will be able to access unique information about themselves. Indeed, Brain-Computer Interfacing as it is known, could become a tool for understanding oneself better.”

Nevertheless, there is still a way to go before the technique can be applied beyond the laboratory. The researchers point out that brain-computer interface devices must become cheaper and easier to use before they find themselves in the hands or strapped to the heads of casual users. Their best guess is that this will take at least 10 years.

The researchers also underscore that the technology comes with a significant challenge for protecting brain-based data from misuse and that it is important for the research community to carefully consider data privacy, ownership and the ethical use of raw data collected by EEG.

ABOUT THE EXPERIMENT

In the experiment, participants were shown a large number of images of human faces and asked to look for those that they found attractive. While doing so, their brain signals were recorded. This data was used to train a machine learning model to distinguish between the brain activity when the participant saw a face that they found attractive versus when they saw a face that they did not find attractive.

With a different machine learning model, the brain-based data from a larger number of participants was used to calculate which new facial images each participant would find attractive. Thus, the prediction was based partly on individual participant’s own brain signals and partly on how other participants responded to the images.

Lead author of the study Dr Saidul Islam, from the University of Technology Sydney, said while previous research has revealed how virus aerosols travel through the upper airways including the nose, mouth and throat — this study was the first to examine how they flow through the lower lungs.

“Our lungs resemble tree branches that divide up to 23 times into smaller and smaller branches. Due to the complexity of this geometry it is difficult to develop a computer simulation, however we were able to model what happens in the first 17 generations, or branches, of the airways,” said Dr Islam.

“Depending on our breathing rate, between 32% and 35% of viral particles are deposited in these first 17 branches. This means around 65% of virus particles escape to the deepest regions of our lungs, which includes the alveoli or air sacs,” he said.

The alveolar system is critical to our ability to absorb oxygen, so significant amounts of virus in this region, along with inflammation caused by our body’s immune response, can cause severe damage, reducing the amount of oxygen in the blood and increasing the risk of death.

The study also revealed that more virus particles are deposited in the right lung, especially the right upper lobe and the right lower lobe, than in the left lung. This is due to the highly asymmetrical anatomical structure of the lungs and the way air flows through the different lobes.

The research is backed up by a recent study of chest CT scans of COVID-19 patients showing greater infection and disease in the regions predicted by the model.

The researchers modelled three different flow rates — 7.5, 15 and 30 litres per minute. The model showed greater virus deposition at lower flow rates.

As well as improving our understanding of coronavirus transmission, the findings have implications for the development of targeted drug delivery devices that can deliver medicine to the areas of the respiratory system most affected by the virus.

“Normally when we inhale drugs from a drug delivery device most of it is deposited in the upper airways, and only a minimum amount of drugs can reach the targeted position of the lower airways. However, with diseases like COVID-19 we need to target the areas most affected,” said Dr Islam.

“We are working to develop devices that can target specific regions, and we also hope to build age and patient specific whole lung models to increase understanding of how SARS CoV-2 aerosols affect individual patients,” said co-author and group leader of the UTS Computer Simulations and Modelling group, Dr Suvash Saha.

The World Health Organisation recently updated its advice about the importance of aerosol transmission, warning that because aerosols can remain suspended in the air, crowded indoor settings and areas with poor ventilation pose a significant risk for transmission of Covid-19.

“When we use an aerosol deodorant, the smallest particles of that liquid fall on us under extreme pressure in the form of gas. Similarly, when an infected person speaks, sings, sneezes or coughs, the virus is spread through the air and can infect those nearby,” said Dr Saha.

The study has further applications, with researchers using portable devices to examine air quality — including PM2.5 and PM10 concentration and gasses such as carbon dioxide, formaldehyde and sulphur dioxide — in spaces such as train carriages. The researchers can then use this data to model the impact on our lungs.

The study, SARS CoV-2 aerosol: How far it can travel to the lower airways, was recently published in the journal Physics of Fluids.