Tyler O'Neal, Staff Editor APPLICATIONS

Tokyo Tech researchers show why multipartite viruses infect plants rather than animals

Being in-between living and non-living, viruses are, in general, strange. Among viruses, multipartite viruses are among the most peculiar—their genome is not packed into one, but many, particles. Multipartite viruses primarily infect plants rather than animals. A recent paper by researchers from the Tokyo Institute of Technology (Tokyo Tech) uses mathematical and computational models to explain this observation.

Multipartite viruses have a strange lifestyle. Their genome is split up into different viral particles that, in principle, propagate independently. Completing the replication cycle, however, requires the full genome such that persistent infection of a host requires the concurrent presence of all types of particles (see Fig. 1). The origin of multipartite viruses is an evolutionary puzzle. Apart from why they can have such a costly lifestyle, the most peculiar thing about them is that almost all known multipartite viruses infect either plants or fungi—very few viral species infect animals.

So far, most theoretical research has been trying focusing on explaining how it is viable to have the genome split into different particles. This paper provides a theoretical explanation of why multipartite viruses primarily infect plants. Figure 1. The Susceptible-Infectious-Recovered model to understand multipartite viruses. Many infectious diseases are modeled by the Susceptible-Infectious-Recovered model. In this model, when a susceptible individual (someone who don't have the disease) meets an infectious individual (someone who have the disease and can spread it), the susceptible can become infectious. After some time the infectious can become recovered (someone who don't have the disease and can't get it). This model has to be modified so that the infection step (when susceptible becomes infectious) describes the accumulation of virus particles until the individual has a complete viral genome.{module In-article}

There have been great efforts to understand the mechanisms that give multipartite viruses an advantage that can compensate for their peculiar and costly lifestyle, and this is not yet a solved problem. Also, our understanding of why most multipartite viruses infect only plants is limited. In a recent work, published in Physical Review Letters, Petter Holme of the World Research Hub Initiative, Tokyo Tech, and colleagues from China and the USA, have explained why multipartite viruses primarily infect plants. In their work, the authors formulated a minimal network-epidemiological model.

They used mathematical models and supercomputer simulations to show that multipartite viruses colonize a structured population (representing the interaction patterns among plants) with less resistance, compared to a well-mixed population (representing the interaction patterns among animals). This is thus an explanation of why multipartite viruses infect plants rather than animals.

The researchers from Tokyo Tech continue to investigate the epidemiology of different types of infectious diseases by theoretical methods. At the moment, they are interested in the more common disease spreading scenarios such as how influenza spreads in cities and how that could be mitigated.

Can we peek at Schrodinger's cat without disturbing it?

Tyler O'Neal, Staff Editor APPLICATIONS

Researchers describe a way of measuring a quantum system while keeping its superposition intact

CAPTION Since the cat in the box (top left) is in a superposition that means it can be in numerous different states (e.g. dead and/or alive) and is marked with a quantum tag. The photo taken of the cat is entangled with the situation inside of the box. We can decide the fate of the cat by processing the photo in a certain way (bottom left), or we can keep it in superposition by restoring the quantum tag using a different process (bottom right). CREDIT Associate Professor Holger F. Hofmann and Emma Buchet/Hiroshima University

Quantum physics is difficult and explaining it even more so. Associate Professor Holger F. Hofmann from Hiroshima University and Kartik Patekar from the Indian Institute of Technology Bombay have tried to solve one of the biggest puzzles in quantum physics: how to measure the quantum system without changing it? 

Their new paper published this month has found that by reading the information observed from a quantum system away from the system itself researchers can determine its state, depending on the method of analysis. Although the analysis is completely removed from the quantum system, it is possible to restore the initial superposition of possible outcomes by a careful reading of the quantum data.

"Normally we would search for something by looking. But in this case looking changes the object, this is the problem with quantum mechanics. We can use complicated maths to describe it, but how can we be sure that mathematics describes what is really there? When we measure something there is a trade-off and the other possibilities of what it could be are lost. You cannot find out about anything without an interaction, you pay a price in advance." explains Hofmann. {module In-article}

During Patekar's month-long stay at Hiroshima University when he was an undergraduate student, the two physicists tried to imagine ways of measuring the system without "paying the price" i.e. keeping the system's superposition or meaning that the system can exist in all states. In order to understand their results Hofmann describes their findings using the well-known physics story of Schrödinger's cat: 

Schrödinger's cat is in a box and the scientists don't know whether it is dead or alive. A camera is set up looking into the box that takes a photo from a position outside of the box. The photo taken of the cat comes out blurry; we can see there is a cat but not whether it is dead or alive. The flash from the camera has also removed a "quantum tag" marking the superposition of the cat. This photo is now entangled with the fate of the cat i.e. we can decide what happened to the cat by processing this photo in a certain way.

The photo could then be taken away from the box and processed on a computer or in a darkroom. Depending on what method is used to process the photo, we can find out either if the cat is alive or dead, or what the flash did to the cat, restoring the quantum tag. The choice of the reader determines what we know about the cat. We can find out if it's dead/alive or restore the quantum tag that was removed when the picture was taken, but not both.

This is only a step forward in our understanding of quantum mechanics. Today its full application remains confined to expert-level systems like quantum supercomputers, although some of its aspects can also be used in precise measurements, and for secure communication using quantum cryptography. 

"This is a key part of my research. I really wanted to understand why this quantum weirdness is there. I focused on measurements because that's where the weirdness comes from!" says Hofmann.

Using machine learning, German scientists create method to better measure animal behavior

Tyler O'Neal, Staff Editor APPLICATIONS

A new toolkit goes beyond existing machine learning methods by measuring body posture in animals with high speed and accuracy. Developed by researchers from the Centre for the Advanced Study of Collective Behaviour at the University of Konstanz and the Max Planck Institute of Animal Behavior, this deep learning toolkit, called DeepPoseKit, combines previous methods for pose estimation with state-of-the-art developments in computer science. These newly-developed deep learning methods can correctly measure body posture from previously-unseen images after being trained with only 100 examples and can be applied to study wild animals in challenging field settings. Published today in the open-access journal eLife, the study is advancing the field of animal behavior with next-generation tools while at the same time providing an accessible system for non-experts to easily apply machine learning to their behavioral research.

Animals must interact with the physical world in order to survive and reproduce, and studying their behavior can reveal the solutions that have evolved for achieving these ultimate goals. Yet behaviour is hard to define just by observing it directly: biases and limited processing power of human observers inhibit the quality and resolution of behavioral data that can be collected from animals.

Machine learning has changed that. Various tools have been developed in recent years that allow researchers to automatically track the locations of animals’ body parts directly from images or videos – without the need for applying intrusive markers on animals or manually scoring behavior. These methods, however, have shortcomings that limit performance. “Existing tools for measuring body posture with deep learning were either slower and more accurate or faster and less accurate – but we wanted to achieve the best of both worlds.” says lead author Jake Graving, a graduate student in the Max Planck Institute of Animal Behavior.

In the new study, researchers present an approach that overcomes this speed-accuracy trade-off. These new methods use an efficient, state-of-the-art deep learning model to detect body parts in images, and a fast algorithm for calculating the location of these detected body parts with high accuracy. Results from this study also demonstrate that these new methods can be applied across species and experimental conditions – from flies, locusts, and mice in controlled laboratory settings to herds of zebras interacting in the wild. Dr. Blair Costelloe, co-author of the paper, who studies zebras in Kenya says: “The posture data we can now collect for the zebras using DeepPoseKit allows us to know exactly what each individual is doing in the group and how they interact with the surrounding environment. In contrast, existing technologies like GPS will reduce this complexity down to a single point in space, which limits the types of questions you can answer.”

A deep learning toolkit, called DeepPoseKit, can automatically detect animal body parts directly from images or video with high speed and accuracy – without attaching physical markers. The method can be used for animals in laboratory settings (e.g. flies and locusts) or in the wild (e.g. zebras). Credit: Jake Graving{module In-article}

Due to its high performance and easy-to-use software interface (the code is publicly available on Github, https://github.com/jgraving/deepposekit), the researchers say that DeepPoseKit can immediately benefit scientists across a variety of fields – such as neuroscience, psychology, and ecology – and levels of expertise. Work on this topic can also have applications that affect our daily lives, such as improving similar algorithms for gesture recognition used on smartphones or diagnosing and monitoring movement-related diseases in humans and animals.

“In just a few short years deep learning has gone from being a sort of niche, hard-to-use method to one of the most democratized and widely-used software tools in the world,” says Iain Couzin, senior author on the paper who leads the Centre for the Advanced Study of Collective Behaviour at the University of Konstanz and the Department of Collective Behaviour at the Max Planck Institute of Animal Behavior. “Our hope is that we can contribute to behavioral research by developing easy-to-use, high-performance tools that anybody can use.” Tools like these are important for studying behavior because, as Graving puts it: “They allow us to start with first principles, or ‘how is the animal moving its body through space?’, rather than subjective definitions of what constitutes a behaviour. From there we can begin to apply mathematical models to the data and develop general theories that help us to better understand how individuals and groups of animals adaptively organize their behaviour.”