Driverless cars going nowhere?

No more accidents caused by human error. More independence for those who can’t drive (Making a car for blind drivers; Google’s driverless car). A chauffeur experience. An extra-private uber ride. The dream of 100% autonomous cars became real in the 1980s, with Carnegie Mellon’s Navlab series. Now in 2015, Google, Uber and Audi among others have been testing self-driving cars on public roads. Since Consumer Watchdog became involved, Google has begun releasing its testing data in monthly reports (June 2015 report) which show few incidents (14 in 1.7 million miles) which were all caused by other humans on the road erring (Google driverless cars in accidents again, humans at fault — again). Statistically, human error by drivers causes 94% of all crashes (The View from the Front Seat of the Google Self-Driving Car). Driverless cars could make public roads much safer, and crashes less frequent. Service provider Uber wants in, and has enticed many of Carnegie Mellon’s prized robotics researchers away to create the first driverless ride service (Carnegie Mellon Reels After Uber Lures Away Researchers). The cars “see” the road using LIDAR, a combination of light and radar (How a driverless car sees the road). By using scores of laser rendered images of pedestrians or cyclists, for example, the cars “recognise” these road users, before even other human drivers. The cars see traffic lights, cone diversions, and can cope with unusual situations- including a family of ducks crossing the road. This odd example illustrates how sensitive the cars are.

LIDAR data of pedestrians

Some opponents of driverless cars have said that though the cars follow the rules of the road, some of the 14 accidents could have been caused because the cars do not act like a human controlled car would, or like people expect a robot car to. In other words, people expect robot cars to move out of lights immediately, or that another human car would equally move with general haste. This seems like a poor excuse. To use the last accident (Youtube clip) Google’s driverless car was in as an example, (The View from the Front Seat of the Google Self-Driving Car, Chapter 2) though there was a green light, the robot car didn’t go, as the lane beyond the intersection was blocked with traffic. Using this argument, as to why the car behind robocar did not brake at all, and instead rear ended Google, is that the passenger expected the car to move forward regardless of the blocked lane, as a “normal” driver would have done. Not only that, the Google car was not first in the lane. There were three “normal” cars in front of it. So there is no way it could have moved forward anyway, without itself rear ending. The autonomous car came to a natural stop, giving plenty of notice. The argument that the “unusual behaviour” of robocars driving like responsible humans will cause accidents is invalid, as by that logic conscientious drivers cause accidents.

Another roadblock for driverless cars are the ethical dilemmas that have been raised. Since the success (or failure, depending on your expectations) of the recent DARPA finals (DARPA Robotics Challenge: Amazing Moments, Lessons Learned, and What’s NextThe DARPA Robotics Challenge Was A Bust) a looming question has been how to keep robots ethical, when faced with lesser-of-two-evils kind of problems. The A-robot created by Alan Winfield was programmed to save H-robots (to simulate humans in danger). Through dozens of tests the A-robot saved its charge each time. But then the A-robot was presented with a moral dilemma: two H-robots wandering into danger simultaneously. In almost half of the trials, the A-robot dithered helplessly and let both perish. To correct this extra rules about how to make such choices would be needed. And what if one H-robot were an adult and the other a child, which should the A-robot save first? On ethical matters like these, a consensus even among humans is difficult (Machine ethics: The robot’s dilemma). This applies to driverless cars too- if a pedestrian steps out unexpectedly, should the car be programmed to swerve, endangering its passengers and other cars? Other issues have also been raised- who is liable for the crash of an autonomous car? There’s the risk of hacking and the added cost of these features too. Hopefully as the technology improves for the cars to be used more in snow and fog, these other issues can be resolved too, including our apparent perception of them (What’s putting the brakes on driverless cars?).


CRISPR, at the flick of a switch?


CRISPR is a revolutionary genome editing technique borrowed from bacteria. Within their genomes, there is an area where DNA is repeated, in several identical segments. These segments are broken up by “spacers”, where the DNA is unique. So as a code, it would look something like: abba25abba34abba87, the “abba” representing identical segments, the unique number codes representing the unique DNA spacers. When a virus invades a microbe, the host cell grabs some of the virus’s genetic material, makes an incision in its own DNA, and inserts the virus DNA piece into a spacer. These spacers are like a police sketch, so if a virus invades a bacteria and its genetic profile is 45dkrn7kdjs62566ldsn, the bacteria can recognise and destroy it.

Located nearby are the Cas genes, which produce enzymes. The spacer information is copied into an RNA molecule to be used by a Cas enzyme. The two patrol the host bacteria. If they meet genetic material from a virus that matches the CRISPR RNA molecule, the molecule holds it in place while the Cas enzyme shreds the viral material. With CRISPR, these bacteria can change their genetic code, making cuts and incorporating DNA into spacers. Adaptations to CRISPR have created a game-changing tool in genome editing research. CRISPR allows efficient, multiple and cheaper edits, and after recent work by Nihongaki et al., may be accomplished at the literal flick of a switch.


Optogenetics allows neurons to be manipulated, by forcing them to respond to light. For example, in 2011 aggression and mating impulses in mice could be controlled to the extent that a blue light stimulus initiated attack on an inflated laboratory glove. Attempts to put CRISPR under optogenetic control have recently succeeded. This method of control could become the standard, as chemical methods (doxycycline, rapamycin) are not always ideal. Endogenous target pathways, free diffusion and the difficulty in quickly removing chemicals makes precise spatio-temporal studies challenging. Photoactivatable Cas9 (paCas9) was created by fusing two Cas9 fragments with Magnets (photoinducible dimerization domains). In blue light, paCas9 creates targeted sequence modifications and is switched off when the light is. This kind of control enables greater understanding of gene networks and could facilitate biomedical applications.


Breakthrough DNA Editor Borne of Bacteria

Photoactivatable CRISPR-Cas9 for optogenetic genome editing

Pros and Cons of ZNFs,TALENs, and CRISPR/Cas

Sex and violence linked in the brain


Alzheimer’s Disease: Amyloid-β and ApoE


Investigation of prion diseases alongside neurodegenerative diseases such as Alzheimer’s has been shaping research, with a number of promising leads.

Both types of diseases are thought to involve proteins in the nervous system that change shape and clump together. In prion diseases, abnormal prion proteins attach to normal prion proteins in a patient’s brain. This attachment transforms normal prions into abnormal ones. Abnormal prions continue to multiply in this way, forming clumps that damage and destroy neurons, causing the symptoms of prion disease. In Alzheimer’s, clumps composed mainly of amyloid-β proteins accumulate within and around nerve cells. Unsuccessful efforts to target these proteins has now shone the spotlight on the lipoprotein ApoE and other possibilities.


Amyloid clumps surround neurons in the AD brain


Amyloid-β and prions

Research has shown amyloid-β proteins misfold and multiply much the same way as prion proteins. However Alzheimer’s involves gradual dementia, memory loss and personality changes, while Prion diseases vary widely and progress faster. Differences in prion illnesses are thought to depend on exactly which abnormal shape the prion takes.

That observation has led researchers to investigate whether similar differences in the folding of amyloid-β proteins may explain differences in how Alzheimer’s develops and how fast it progresses. In an analysis published this year, it was found that patients’ conditions worsened faster when a particular shape of amyloid-β (known as amyloid-β42) was present.


In humans, there are three common variants of the APOE gene, numbered 2, 3 and 4. The APOE4 allele is associated with a greatly increased risk of Alzheimer’s disease. APOE4 remains the leading genetic risk factor for Alzheimer’s, the most common form of dementia. Inheriting one copy of APOE4 raises a person’s risk of developing the disease fourfold. With two copies, the risk increases 12-fold. Interest in the lipoprotein is picking up, in part because attempts to target amyloid-β have repeatedly disappointed in major clinical trials. In both animals and humans, ApoE4 promotes amyloid-β deposition in the brain. ApoE2,  considered the protective form, decreases the build-up. When neurons are under stress, they make ApoE as part of a repair mechanism. The ApoE4 form can break down into toxic fragments that damage mitochondria.

Changing a harmful form of ApoE into a less damaging one might prove a promising therapeutic approach. Small ‘corrector’ molecules that modify the structure of ApoE4 protein to one more like that of ApoE3 have been identified, thereby reducing abnormal fragmentation. In cell culture, low concentrations of these corrector molecules can reduce mitochondrial and neuronal damage.

Other possibilities

The protein encoded by the gene TOMM40, called Tom40, is needed for healthy mitochondria. This gene is located on chromosome 19, beside ApoE, and can be linked to Alzheimer’s. Tom40 could be used to develop therapies and improved tests for Alzheimer’s risk. Only about 25% of people carry an APOE4 allele, so genotyping both APOE and TOMM40 could provide information about more of the population.

Finally, a current 5 year trial is investigating whether a drug known as Pioglitazone can delay disease onset in individuals at high risk of Alzheimer’s. Previous evidence has suggested Pioglitazone may prevent or reverse Alzheimer’s-related pathology and symptoms, possibly by stimulating mitochondria to divide.


Alzheimer’s disease: The forgetting gene

Alzheimer’s research takes a leaf from the prion notebook





Animals, Behaviour

Chink in the armour: How small spiders kill larger prey

If you’re going to attack an animal larger than you, you had better do so carefully!

The  recluse spider (order = Araneae) hunts the much larger armoured harvestman  (order = Opiliones) in addition to crickets. Larger spiders than the recluse avoid the armoured harvestman, even when no other food source is presented.

In a recent paper by Segovia et al, a series of experiments investigate what it is that enables this many-legged David to take on Goliath.

The team had a number of hypotheses to test, based on hunting strategies of other related taxonomic groups. Firstly, it was thought that recluse spiders might select hunting grounds by detecting prey chemicals (like lycosid and oxyopid spiders). This was shown not to be the case.

It was thought that recluse spiders might sense their prey by using vibrations, but this was also proven false. Recluse spiders do use this sense to communicate with each other, but not to source prey. Next the group showed even the presence of a silk web was superfluous to the recluse spider.

Full-size image (114 K)

Spiders use tactile touch to identify weak points to attack. In 20 predation events, with 176 bites total, these percentages (sum of data from both sides of body) show the specific targeted nature of attacks.

Having disproved these initial hypotheses, the team analysed behavioural videos, and discovered there was a reliable pattern. Spiders would touch prey with their legs, and quickly bite specific parts of the preys body (pictured).

It is thought that these tactile cues allow the spider to target weak areas. Larger spiders like C. ornatus and E. cyclothorax do not exhibit this touch behaviour and bite prey indiscriminately, which may be the reason they avoid this prey.

This study by Segovia et al has demonstrated how a delicate predator can subdue a well-defended and heavy-bodied prey by finding the weak spots. It shows the importance of avoiding generalizations when studying prey–predator interactions, since several of their results contrast with what would be expected.


Júlio M.G. Segovia, Kleber Del-Claro, Rodrigo Hirata Willemart. (2015). Delicate fangs, smart killing: the predation strategy of the recluse spider. Animal Behaviour. 101, 169–177.

Animals, Behaviour

Goshawks won’t eat just anyone

Predator-prey interactions

How do top predators impact prey populations? This is more complex then it may seem. More predators logically leads to less prey, but it also leads to increased competition for resources among a now larger predator population. Because of this feedback loop, successful predators can destabilise not only a prey population, but their own.

These interactions are often studied by measuring species abundance to predict how stable the populations are and to test if intervention may be required. However, a recent paper by Hoy et al showed prey selectivity by predators may lessen their impact.

Tagging and recoveries 


This study took place over 1979-2012. Tawny owls are preyed upon by a superpredator, the Northern Goshawk. During the study tawny owls were tagged to record their sex and year of birth. This information was used to estimate the prey population- how many males/females, how many juveniles (less than a year old), /young adults (1-9 years old)/elderly (over 9 years old). The group recorded tags that were recovered in goshawk nest sites, to identify if kills were evenly spread across ages and gender.

Age and sex selective pattern

Hoy et al found that predation of juvenile owls was disproportionately high across both sexes. Of the adults, females were predated much more than males. Predation risk appeared to increase with age also.

Why this pattern? 

Juveniles call loudly during the day, and draw more attention to themselves. They are also poor fliers at this young age. Female-biased predation is actually unusual. In many species (including tawny owls) females are bigger than males, so while they offer a greater metabolic reward they are also a greater risk, leading to male biased predation across many taxonomic groups. In this case it is thought that female carers of young decline in flying ability, as the energy spent in reproduction means less can be invested in plumage, caring means less time to exercise flying muscles, and the loud calls of juveniles make their carers conspicuous too. The cumulative costs of many breeding years is thought to be the reason the elderly are more vulnerable.

What do different selection patterns mean for a prey population?


Modelling different predation patterns

The most “valuable” individuals in a population are those capable of breeding. In tawny owls, these are the adult birds between the ages of 1 and 9. During this investigation the team built up a model of what the owl population looked like. They then predicted what the impact would be on population size if predators chose prey according to 5 different patterns:

1) The observed pattern, i.e. mostly juveniles and elderly females
2) Goshawk predators chose prey evenly across all ages
3) Only juveniles (less than a year old)
4) Only young breeding adults (ages 1-9)
5) Only the elderly (9+)

As the  graph shows, the negative impact on owl population size by predators varied, from 10-60%. The observed pattern (grey bar in graph) of predation from the field studies was shown to have a relatively small impact.

In conclusion

The selective predation of non-breeders may mitigate the overall impact of predators on prey population dynamics. Investigating other predators for selective predation can improve future predictions of the overall impact of predation.


Hoy, S. R., Petty, S. J., Millon, A., Whitfield, D. P., Marquiss, M., Davison, M., Lambin, X. (2014), Age and sex-selective predation moderate the overall impact of predators. Journal of Animal Ecology. doi: 10.1111/1365-2656.12310

Animals, Behaviour

Thieving orang-utan mums


Social tool use

Social tool use is the use of manipulation or cooperation to achieve a desired result, and is an important part of primate interactions.

Why orang-utans? Why mother-offspring pairs? 

Orang-utan adult pairs have been found to exchange tokens when an individual possesses tokens that were useless for it but that the other individual could exchange for food. Orang-utans were much more likely to donate tokens compared to chimpanzees, gorillas and bonobos when tested. Thus, orang-utans seem to be a promising species to explore social tool use. In this study by Völtera et al mother–offspring pairs were chosen because while mothers are physically stronger than their young, there is great tolerance between them.

The hypothesis

Creating a situation in which mothers have no direct physical control over their offspring may transform the mother’s social tool use from manipulative to a cooperative activity.



Mothers manipulate their offspring to obtain a reward

Initial experiments performed by the group established that the orang-utan mothers manipulated their offspring to retrieve out-of-reach food rewards (offspring retrieve food and mothers steal and eat it) and tools (offspring retrieve tool and mothers steal it, unlock food reward and eat it). 


Mothers cooperate with their offspring

Völtera et al then set up an experiment in which mothers had a tool but only offspring could use it to release a food reward for both. In this experiment mothers cooperated with their offspring by passing them the tool. In a variation on this experiment only the offspring received a food reward. In this case mothers’ motivation for cooperation declined, however most mothers continued to pass the tool over to their offspring.


In summary, these behavioural experiments showed how orang-utan mothers switched from manipulation to cooperation to achieve their goals, some even continuing cooperation when the benefit was not mutual.


Christoph J. Völtera, Federico Rossanoa, Josep Calla. (2015). From exploitation to cooperation: social tool use in orang-utan mother–offspring dyads. Animal Behaviour. 100, 126–134.


Vultures don’t believe in a 5-second rule


Toxic grub

After death a countdown begins, something like the 5-second rule. Internal microorganisms begin decomposing their host, excreting toxins as they break down tissue.  As time passes these toxins continue to build, making the carcass hazardous (just think of your leftover turkey). When the countdown is up, surprisingly there is still an interested party – the scavengers. Vultures and other scavengers consume these risky food sources, often at advanced stages of decomposition. In fact vultures often purposefully wait for decay to set in, so they can better access carcasses with tough skins. A recent paper by Roggenbuck et al  has uncovered some of the adaptations involved in keeping them safe from their toxic grub.


Once the exterior has been broken through, by waiting for decay, or a stronger beaked raptor, vultures can feed, ingesting bacteria present along with carrion. Furthermore many insert their heads inside the body cavities of carcasses, exposing head and neck to pathogenic bacteria. However most of these bacteria never make it to colonise the hindgut, and are degraded by gastric acid long before. This powerful filtration process can be seen in the figure below, which shows the diversity of bacteria on the skin, with significantly fewer in the hindgut.



Some hardy bacteria can survive the gastric acid stage. The hindgut is usually dominated by Clostridia and Fusobacteria, which are pathogenic to most vertebrates. It’s believed that Clostridia and Fusobacteria outcompete other bacterial groups, taking control of the protein-rich anaerobic hindgut (bacterial prime real estate).

An analysis of microbial DNA from vulture hindgut identified genes encoding tissue-degrading enzymes. Bacteria like Clostridia and Fusobacteria could therefore help in the breakdown of carrion by producing these enzymes. Other animals like crocodiles or hyenas lack in these bacterial populations, despite similar carrion diets. Therefore it is suspected the vulture hindgut is specially adapted to encourage these mutualistic dynamics.



Michael Roggenbuck, Ida Bærholm Schnell, Nikolaj Blom, Jacob Bælum, Mads Frost Bertelsen, Thomas Sicheritz Pontén, Søren Johannes Sørensen, M. Thomas P. Gilbert, Gary R. Graves & Lars H Hansen. (2014). The microbiome of New World vultures. Nature Communications. 5.

Adam Kane, Andrew L. Jackson, Darcy L. Ogada, Ara Monadjem, Luke McNally. (2014). Vultures acquire information on carcass location from scavenging eagles. Proceedings. Biological Sciences/Royal Society. 281.