Fungi-produced pigment shows promise as semiconductor material

Researchers at Oregon State University are looking at a highly durable organic pigment, used by humans in artwork for hundreds of years, as a promising possibility as a semiconductor material.

Findings suggest it could become a sustainable, low-cost, easily fabricated alternative to silicon in electronic or optoelectronic applications where the high-performance capabilities of silicon aren't required.

Optoelectronics is technology working with the combined use of light and electronics, such as solar cells, and the pigment being studied is xylindein.

"Xylindein is pretty, but can it also be useful? How much can we squeeze out of it?" said Oregon State University physicist Oksana Ostroverkhova. "It functions as an electronic material but not a great one, but there's optimism we can make it better."

Xylindien is secreted by two wood-eating fungi in the Chlorociboria genus. Any wood that's infected by the fungi is stained a blue-green color, and artisans have prized xylindein-affected wood for centuries.

The pigment is so stable that decorative products made half a millennium ago still exhibit its distinctive hue. It holds up against prolonged exposure to heat, ultraviolet light and electrical stress.

"If we can learn the secret for why those fungi-produced pigments are so stable, we could solve a problem that exists with organic electronics," Ostroverkhova said. "Also, many organic electronic materials are too expensive to produce, so we're looking to do something inexpensively in an ecologically friendly way that's good for the economy."

With current fabrication techniques, xylindein tends to form non-uniform films with a porous, irregular, "rocky" structure.

"There's a lot of performance variation," she said. "You can tinker with it in the lab, but you can't really make a technologically relevant device out of it on a large scale. But we found a way to make it more easily processed and to get a decent film quality."

Ostroverkhova and collaborators in OSU's colleges of Science and Forestry blended xylindein with a transparent, non-conductive polymer, poly(methyl methacrylate), abbreviated to PMMA and sometimes known as acrylic glass. They drop-cast solutions both of pristine xylindein and a xlyindein-PMMA blend onto electrodes on a glass substrate for testing.

They found the non-conducting polymer greatly improved the film structure without a detrimental effect on xylindein's electrical properties. And the blended films actually showed better photosensitivity.

"Exactly why that happened, and its potential value in solar cells, is something we'll be investigating in future research," Ostroverkhova said. "We'll also look into replacing the polymer with a natural product -- something sustainable made from cellulose. We could grow the pigment from the cellulose and be able to make a device that's all ready to go.

"Xylindein will never beat silicon, but for many applications, it doesn't need to beat silicon," she said. "It could work well for depositing onto large, flexible substrates, like for making wearable electronics."

This research, whose findings were recently published in MRS Advances, represents the first use of a fungus-produced material in a thin-film electrical device.

"And there are a lot more of the materials," Ostroverkhova said. "This is just first one we've explored. It could be the beginning of a whole new class of organic electronic materials."

The National Science Foundation supported this research.

Source: sciencedaily.com

Researchers reverse cognitive impairments in mice with dementia

Reversing memory deficits and impairments in spatial learning is a major goal in the field of dementia research. A lack of knowledge about cellular pathways critical to the development of dementia, however, has stood in the way of significant clinical advance. But now, researchers at the Lewis Katz School of Medicine at Temple University (LKSOM) are breaking through that barrier. They show, for the first time in an animal model, that tau pathology -- the second-most important lesion in the brain in patients with Alzheimer's disease -- can be reversed by a drug.

"We show that we can intervene after disease is established and pharmacologically rescue mice that have tau-induced memory deficits," explained senior investigator Domenico Praticò, MD, Scott Richards North Star Foundation Chair for Alzheimer's Research, Professor in the Departments of Pharmacology and Microbiology, and Director of the Alzheimer's Center at Temple at LKSOM. The study, published online in the journal Molecular Neurobiology, raises new hope for human patients affected by dementia.

The researchers landed on their breakthrough after discovering that inflammatory molecules known as leukotrienes are deregulated in Alzheimer's disease and related dementias. In experiments in animals, they found that the leukotriene pathway plays an especially important role in the later stages of disease.

"At the onset of dementia, leukotrienes attempt to protect nerve cells, but over the long term, they cause damage," Dr. Praticò said. "Having discovered this, we wanted to know whether blocking leukotrienes could reverse the damage, whether we could do something to fix memory and learning impairments in mice having already abundant tau pathology."

To recapitulate the clinical situation of dementia in humans, in which patients are already symptomatic by the time they are diagnosed, Dr. Praticò and colleagues used specially engineered tau transgenic mice, which develop tau pathology -- characterized by neurofibrillary tangles, disrupted synapses (the junctions between neurons that allow them to communicate with one another), and declines in memory and learning ability -- as they age. When the animals were 12 months old, the equivalent of age 60 in humans, they were treated with zileuton, a drug that inhibits leukotriene formation by blocking the 5-lipoxygenase enzyme.

After 16 weeks of treatment, animals were administered maze tests to assess their working memory and their spatial learning memory. Compared with untreated animals, tau mice that had received zileuton performed significantly better on the tests. Their superior performance suggested a successful reversal of memory deficiency.

To determine why this happened, the researchers first analyzed leukotriene levels. They found that treated tau mice experienced a 90-percent reduction in leukotrienes compared with untreated mice. In addition, levels of phosphorylated and insoluble tau, the form of the protein that is known to directly damage synapses, were 50 percent lower in treated animals. Microscopic examination revealed vast differences in synaptic integrity between the groups of mice. Whereas untreated animals had severe synaptic deterioration, the synapses of treated tau animals were indistinguishable from those of ordinary mice without the disease.

"Inflammation was completely gone from tau mice treated with the drug," Dr. Praticò said. "The therapy shut down inflammatory processes in the brain, allowing the tau damage to be reversed."

The study is especially exciting because zileuton is already approved by the Food and Drug Administration for the treatment of asthma. "Leukotrienes are in the lungs and the brain, but we now know that in addition to their functional role in asthma, they also have a functional role in dementia," Dr. Praticò explained.

"This is an old drug for a new disease," he added. "The research could soon be translated to the clinic, to human patients with Alzheimer's disease."

Source: sciencedaily.com

Could robots be counselors? Early research shows positive user experience

New research has shown for the first time that a social robot can deliver a 'helpful' and 'enjoyable' motivational interview (MI) -- a counselling technique designed to support behaviour change.

Many participants in the University of Plymouth study praised the 'non-judgemental' nature of the humanoid NAO robot as it delivered its session -- with one even saying they preferred it to a human.

Led by the School of Psychology, the study also showed that the robot achieved a fundamental objective of MI as it encouraged participants, who wanted to increase their physical activity, to articulate their goals and dilemmas aloud.

MI is a technique that involves the counsellor supporting and encouraging someone to talk about their need for change, and their reasons for wanting to change.

The role of the interviewer in MI is mainly to evoke a conversation about change and commitment, and the robot was programmed with a set script designed to elicit ideas and conversation on how someone could increase their physical activity.

When finished answering each question, the participant taped the top of NAO's head to continue, with some sessions lasting up to an hour.

Lead academic Professor Jackie Andrade explained that, because they are perceived as nonjudgmental, robots may have advantages over more humanoid avatars for delivering virtual support for behavioral change.

"We were pleasantly surprised by how easily the participants adapted to the unusual experience of discussing their lifestyle with a robot," she said. "As we have shown for the first time that a motivational interview delivered by a social robot can elicit out-loud discussion from participants.

"In addition, the participants perceived the interaction as enjoyable, interesting and helpful. Participants found it especially useful to hear themselves talking about their behaviour aloud, and liked the fact that the robot didn't interrupt, which suggests that this new intervention has a potential advantage over other technology-delivered adaptations of MI.

"Concern about being judged by a human interviewer came across strongly in praise for the non-judgemental nature of the robot, suggesting that robots may be particularly helpful for eliciting talk about sensitive issues.

"The next stage is to undertake a quantitative study, where we can measure whether participants felt that the intervention actually increased their activity levels."

Source: sciencedaily.com

Activity simulator could eventually teach robots tasks like making coffee or setting the table

Recently, computer scientists have been working on teaching machines to do a wider range of tasks around the house. Researchers demonstrate 'VirtualHome,' a system that can simulate detailed household tasks and then have artificial 'agents' execute them, opening up the possibility of one day teaching robots to do such tasks.

For many people, household chores are a dreaded, inescapable part of life that we often put off or do with little care -- but what if a robot maid could help lighten the load?

In a new paper spearheaded by MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the University of Toronto, researchers demonstrate "VirtualHome," a system that can simulate detailed household tasks and then have artificial "agents" execute them, opening up the possibility of one day teaching robots to do such tasks.

The team trained the system using nearly 3,000 programs of various activities, which are further broken down into subtasks for the computer to understand. A simple task like "making coffee," for example, would also include the step "grabbing a cup." The researchers demonstrated VirtualHome in a 3-D world inspired by the Sims video game.

The team's AI agent can execute 1,000 of these interactions in the Sims-style world, with eight different scenes including a living room, kitchen, dining room, bedroom, and home office.

"Describing actions as computer programs has the advantage of providing clear and unambiguous descriptions of all the steps needed to complete a task," says PhD student Xavier Puig, who was lead author on the paper. "These programs can instruct a robot or a virtual character, and can also be used as a representation for complex tasks with simpler actions."

The project was co-developed by CSAIL and the University of Toronto alongside researchers from McGill University and the University of Ljubljana. It will be presented at the Computer Vision and Pattern Recognition (CVPR) conference, which takes place this month in Salt Lake City.

How it works

Unlike humans, robots need more explicit instructions to complete easy tasks -- they can't just infer and reason with ease.

For example, one might tell a human to "switch on the TV and watch it from the sofa." Here, actions like "grab the remote control" and "sit/lie on sofa" have been omitted, since they're part of the commonsense knowledge that humans have.

To better demonstrate these kinds of tasks to robots, the descriptions for actions needed to be much more detailed. To do so, the team first collected verbal descriptions of household activities, and then translated them into simple code. A program like this might include steps like: walk to the television, switch on the television, walk to the sofa, sit on the sofa, and watch television.

Once the programs were created, the team fed them to the VirtualHome 3-D simulator to be turned into videos. Then, a virtual agent would execute the tasks defined by the programs, whether it was watching television, placing a pot on the stove, or turning a toaster on and off.

The end result is not just a system for training robots to do chores, but also a large database of household tasks described using natural language. Companies like Amazon that are working to develop Alexa-like robotic systems at home could eventually use data like this to train their models to do more complex tasks.

The team's model successfully demonstrated that, their agents could learn to reconstruct a program, and therefore perform a task, given either a description: "pour milk into glass," or a video demonstration of the activity.

"This line of work could facilitate true robotic personal assistants in the future," says Qiao Wang, a research assistant in arts, media, and engineering at Arizona State University. "Instead of each task programmed by the manufacturer, the robot can learn tasks just by listening to or watching the specific person it accompanies. This allows the robot to do tasks in a personalized way, or even some day invoke an emotional connection as a result of this personalized learning process."

In the future, the team hopes to train the robots using actual videos instead of Sims-style simulation videos, which would enable a robot to learn simply by watching a YouTube video. The team is also working on implementing a reward-learning system in which the agent gets positive feedback when it does tasks correctly.

"You can imagine a setting where robots are assisting with chores at home and can eventually anticipate personalized wants and needs, or impending action," says Puig. "This could be especially helpful as an assistive technology for the elderly, or those who may have limited mobility."

Source: sciencedaily.com