Scientists have built tiny droplet-based microbial factories that produce hydrogen, instead of oxygen, when exposed to daylight in air.
The findings of the international research team based at the University of Bristol and Harbin Institute of Technology in China, are published today in Nature Communications.
Normally, algal cells fix carbon dioxide and produce oxygen by photosynthesis. The study used sugary droplets packed with living algal cells to generate hydrogen, rather than oxygen, by photosynthesis.
Hydrogen is potentially a climate-neutral fuel, offering many possible uses as a future energy source. A major drawback is that making hydrogen involves using a lot of energy, so green alternatives are being sought and this discovery could provide an important step forward.
The team, comprising Professor Stephen Mann and Dr Mei Li from Bristol’s School of Chemistry together with Professor Xin Huang and colleagues at Harbin Institute of Technology in China, trapped ten thousand or so algal cells in each droplet, which were then crammed together by osmotic compression. By burying the cells deep inside the droplets, oxygen levels fell to a level that switched on special enzymes called hydrogenases that hijacked the normal photosynthetic pathway to produce hydrogen. In this way, around a quarter of a million microbial factories, typically only one-tenth of a millimetre in size, could be prepared in one millilitre of water.
To increase the level of hydrogen evolution, the team coated the living micro-reactors with a thin shell of bacteria, which were able to scavenge for oxygen and therefore increase the number of algal cells geared up for hydrogenase activity.
Although still at an early stage, the work provides a step towards photobiological green energy development under natural aerobic conditions.
Professor Stephen Mann, Co-Director of the Max Planck Bristol Centre for Minimal Biology at Bristol, said: “Using simple droplets as vectors for controlling algal cell organization and photosynthesis in synthetic micro-spaces offers a potentially environmentally benign approach to hydrogen production that we hope to develop in future work.”
Professor Xin Huang at Harbin Institute of Technology added: “Our methodology is facile and should be capable of scale-up without impairing the viability of the living cells. It also seems flexible; for example, we recently captured large numbers of yeast cells in the droplets and used the microbial reactors for ethanol production.”
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Researchers from Boston University and University of Virginia published a new paper in the Journal of Marketing that examines how consumers respond to AI recommenders when focused on the functional and practical aspects of a product (its utilitarian value) versus the experiential and sensory aspects of a product (its hedonic value).
The study, forthcoming in the the Journal of Marketing, is titled “Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The ‘Word-of-Machine’ Effect” and is authored by Chiara Longoni and Luca Cian.
More and more companies are leveraging technological advances in AI, machine learning, and natural language processing to provide recommendations to consumers. As these companies evaluate AI-based assistance, one critical question must be asked: When do consumers trust the “word of machine,” and when do they resist it?
A new Journal of Marketing study explores reasons behind the preference of recommendation source (AI vs. human). The key factor in deciding how to incorporate AI recommenders is whether consumers are focused on the functional and practical aspects of a product (its utilitarian value) or on the experiential and sensory aspects of a product (its hedonic value).
Relying on data from over 3,000 study participants, the research team provides evidence supporting a word-of-machine effect, defined as the phenomenon by which the trade-offs between utilitarian and hedonic aspects of a product determine the preference for, or resistance to, AI recommenders. The word-of-machine effect stems from a widespread belief that AI systems are more competent than humans at dispensing advice when functional and practical qualities (utilitarian) are desired and less competent when the desired qualities are experiential and sensory-based (hedonic). Consequently, the importance or salience of utilitarian attributes determine preference for AI recommenders over human ones, while the importance or salience of hedonic attributes determine resistance to AI recommenders over human ones.
The researchers tested the word-of-machine effect using experiments designed to assess people’s tendency to choose products based on consumption experiences and recommendation source. Longoni explains that “We found that when presented with instructions to choose products based solely on utilitarian/functional attributes, more participants chose AI-recommended products. When asked to only consider hedonic/experiential attributes, a higher percentage of participants chose human recommenders.”
When utilitarian features are most important, the word-of-machine effect was more distinct. In one study, participants were asked to imagine buying a winter coat and rate how important utilitarian/functional attributes (e.g., breathability) and hedonic/experiential attributes (e.g., fabric type) were in their decision making. The more utilitarian/functional features were highly rated, the greater the preference for AI over human assistance, and the more hedonic/experiential features were highly rated, the greater the preference for human over AI assistance.
Another study indicated that when consumers wanted recommendations matched to their unique preferences, they resisted AI recommenders and preferred human recommenders regardless of hedonic or utilitarian preferences. These results suggest that companies whose customers are known to be satisfied with “one size fits all” recommendations (i.e., not in need of a high level of customization) may rely on AI-systems. However, companies whose customers are known to desire personalized recommendations should rely on humans.
Although there is a clear correlation between utilitarian attributes and consumer trust in AI recommenders, companies selling products that promise more sensorial experiences (e.g., fragrances, food, wine) may still use AI to engage customers. In fact, people embrace AI’s recommendations as long as AI works in partnership with humans. When AI plays an assistive role, “augmenting” human intelligence rather than replacing it, the AI-human hybrid recommender performs as well as a human-only assistant.
Overall, the word-of-machine effect has important implications as the development and adoption of AI, machine learning, and natural language processing challenges managers and policy-makers to harness these transformative technologies. As Cian says, “The digital marketplace is crowded and consumer attention span is short. Understanding the conditions under which consumers trust, and do not trust, AI advice will give companies a competitive advantage in this space.”
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Researchers have used cutting edge technology to bioprint miniature human kidneys in the lab, paving the way for new treatments for kidney failure and possibly lab-grown transplants.
The study, led by the Murdoch Children’s Research Institute (MCRI) and biotech company Organovo and published in Nature Materials, saw the research team also validate the use of 3D bioprinted human mini kidneys for screening of drug toxicity from a class of drugs known to cause kidney damage in people.
The research showed how 3D bioprinting of stem cells can produce large enough sheets of kidney tissue needed for transplants.
Like squeezing toothpaste out of a tube, extrusion-based 3D bioprinting uses a ‘bioink’ made from a stem cell paste, squeezed out through a computer-guided pipette to create artificial living tissue in a dish.
MCRI researchers teamed up with San Diego based Organovo Inc to create the mini organs.
MCRI Professor Melissa Little, a world leader in modelling the human kidney, first began growing kidney organoids in 2015. But this new bio-printing method is faster, more reliable and allows the whole process to be scaled up. 3D bioprinting could now create about 200 mini kidneys in 10 minutes without compromising quality, the study found.
From larger than a grain of rice to the size of a fingernail, bioprinted mini-kidneys fully resemble a regular-sized kidney, including the tiny tubes and blood vessels that form the organ’s filtering structures called nephrons.
Professor Little said by using mini-organs her team hope to screen drugs to find new treatments for kidney disease or to test if a new drug was likely to injure the kidney.
“Drug-induced injury to the kidney is a major side effect and difficult to predict using animal studies. Bioprinting human kidneys are a practical approach to testing for toxicity before use,” she said.
In this study, the toxicity of aminoglycosides, a class of antibiotics that commonly damage the kidney, were tested.
“We found increased death of particular types of cells in the kidneys treated with aminoglycosides,” Professor Little said.
“By generating stem cells from a patient with a genetic kidney disease, and then growing mini kidneys from them, also paves the way for tailoring treatment plans specific to each patient, which could be extended to a range of kidney diseases.”
Professor Little said the study showed growing human tissue from stem cells also brought the promise of bioengineered kidney tissue.
“3D bioprinting can generate larger amounts of kidney tissue but with precise manipulation of biophysical properties, including cell number and conformation, improving the outcome,” she said.
Currently, 1.5 million Australians are unaware they are living with early signs of kidney disease such as decreased urine output, fluid retention and shortness of breath.
Professor Little said prior to this study the possibility of using mini kidneys to generate transplantable tissue was too far away to contemplate.
“The pathway to renal replacement therapy using stem cell-derived kidney tissue will need a massive increase in the number of nephron structures present in the tissue to be transplanted,” she said.
“By using extrusion bioprinting, we improved the final nephron count, which will ultimately determine whether we can transplant these tissues into people.”
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Harrisburg, PA – Today, Department of Community and Economic Development (DCED) Secretary Dennis Davin highlighted the efforts of Letterkenny Army Depot for its pivot to produce 120,000 protective medical gowns for WellSpan Health through a Public Private Partnership in response to the COVID-19 pandemic’s impact on the ability for medical professionals to acquire personal protective equipment (PPE). A Public Private Partnership allows members of the Army’s Organic Industrial Base facilities like Letterkenny Army Depot to manufacture or sell products or services to the private sector. The Army depot also developed cloth face mask prototypes for WellSpan and shared the designs with other community partners.
Letterkenny is one of several military installations supported through DCED’s Pennsylvania Military Community Enhancement Commission (PMCEC). The commission provides funding to communities to support and enhance the value of their local military installations.
“Earlier this year when COVID-19 was beginning to overwhelm healthcare organizations, we were facing a major supply shortage for critical PPE for some of our most frontline and essential workers,” said Sec. Davin. “Letterkenny didn’t hesitate when approached for help by medical leaders in their community, showcasing qualities we know represent the committed members of our state’s military entities—ambition, dependability, and teamwork.”
In early April, Chambersburg Hospital became aware of Letterkenny’s upholstery shop in Franklin County through a local news special highlighting how the Army depot adjusted operations to create around 14,000 cloth masks for its employees and other Department of Defense installations. Within a day, WellSpan and Letterkenny were connected and began evaluating the capabilities of the Army depot’s upholstery shop, which traditionally makes items like canvas tents and kitchen and vinyl products. In York, WellSpan facilities were in dire need of isolation medical gowns and reached out to Letterkenny for assistance. Letterkenny immediately entered into the Public Private Partnership and reallocated resources to staff the upholstery shop to begin making gowns.
Pennsylvania has 13 military installations supported through PMCEC. Letterkenny serves as the Department of Defense Center of Industrial and Technical Excellence for Air Defense & Tactical Missile Ground Support Equipment, Mobile Electric Power Generation Equipment, Patriot Missile Recertification and Route Clearance Vehicles.
For the most up-to-date information on COVID-19, visit the Department of Health website. To receive the latest updates follow the Department of Health on Facebook and Twitter and Governor Wolf on Facebook and Twitter.
Casey Smith, DCED, [email protected]
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Wow! This new kit from BBC Learning, SiFive, and Tynker comes with lessons narrated by Jodie Whittaker – the newest Doctor Who – herself!
Ayrshare, the social media API platform, recently announced the public launch of the Ayrshare Application Programming Interface, or API, to automate social media posting. The API addresses the needs of companies and platforms that programmatically post content to multiple social media networks.
“For companies that are publicly facing, social media is important to build their brand, engage their community, and drive new sales,” said Geoffrey Bourne, co-founder of Ayrshare. “We are now seeing a fresh wave of adoption of API-first workflows which generate content dynamically and connect directly to the social media destinations. Ayrshare is the first API offering to tackle this challenge head-on.”
The Ayrshare API provides a range of benefits. For instance, platforms using Ayrshare can enable their users to distribute text, images, and videos created on the platform to multiple social destinations including Facebook, Twitter, Instagram, LinkedIn, and others. They can deploy the Ayrshare solution in a few hours, versus an in-house build process that typically takes weeks of approvals, requires complex and disparate implementations, and continuous maintenance and upgrades.
Developers are increasingly looking for APIs that remove the hassles of researching, onboarding, integrating, and supporting multiple integrations, so they can focus on their core product offering. Ayrshare addresses these needs and offers detailed technical documentation, simple API calls, and a free plan to get started.
Features of the Ayrshare API include integration with six social media networks, sending real-time and scheduled posts, deleting posts, getting history, managing images, link shortening, auto hash-tags, auto-reposting, and more.
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Author: <a href="https://www.programmableweb.com/user/%5Buid%5D">ProgrammableWeb PR</a>
A common insecticide that is a major hazard for honeybees is now effectively detected in honey thanks to a simple new method.
Researchers at the University of Waterloo developed an environmentally friendly, fully automated technique that extracts pyrethroids from the honey. Pyrethroids are one of two main groups of pesticides that contribute to colony collapse disorder in bees, a phenomenon where worker honeybees disappear, leaving the queen and other members of the hive to die. Agricultural producers worldwide rely on honeybees to pollinate hundreds of billions of dollars worth of crops.
Extracting the pyrethroids with the solid phase microextraction (SPME) method makes it easier to measure whether their levels in the honey are above those considered safe for human consumption. It can also help identify locations where farmers use the pesticide and in what amounts. The substance has traditionally been difficult to extract because of its chemical properties.
“Pyrethroids are poorly soluble in water and are actually suspended in honey,” said Janusz Pawliszyn, a professor of chemistry at Waterloo. “We add a small amount of alcohol to dissolve them prior to extraction by the automated SPME system.”
Farmers spray the pesticides on crops. They are neurotoxins, which affect the way the brain and nerves work, causing paralysis and death in insects.
“It is our hope that this very simple method will help authorities determine where these pesticides are in use at unsafe levels to ultimately help protect the honeybee population,” said Pawliszyn.
The Canadian Food Inspection Agency tests for chemical residues in food in Canada. Maximum residue limits are regulated under the Pest Control Products Act. The research team found that of the honey products they tested that contained the pesticide, all were at allowable levels.
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The gas clouds in which stars are born and evolve are vast regions of the Universe that are extremely rich in matter, and hence in physical processes. All these processes are intertwined on different size and time scales, making it almost impossible to fully understand such stellar nurseries. However, the scientists in the ORION-B* programme have now shown that statistics and artificial intelligence can help to break down the barriers still standing in the way of astrophysicists.
With the aim of providing the most detailed analysis yet of the Orion molecular cloud, one of the star-forming regions nearest the Earth, the ORION-B team included in its ranks scientists specialising in massive data processing. This enabled them to develop novel methods based on statistical learning and machine learning to study observations of the cloud made at 240,000 frequencies of light**.
Based on artificial intelligence algorithms, these tools make it possible to retrieve new information from a large mass of data such as that used in the ORION-B project. This enabled the scientists to uncover a certain number of ‘laws’ governing the Orion molecular cloud.
For instance, they were able to discover the relationships between the light emitted by certain molecules and information that was previously inaccessible, namely, the quantity of hydrogen and of free electrons in the cloud, which they were able to estimate from their calculations without observing them directly. By analysing all the data available to them, the research team was also able to determine ways of further improving their observations by eliminating a certain amount of unwanted information.
The ORION-B teams now wish to put this theoretical work to the test, by applying the estimates and recommendations obtained and verifying them under real conditions. Another major theoretical challenge will be to extract information about the speed of molecules, and hence visualise the motion of matter in order to see how it moves within the cloud.
*- Standing for Outstanding Radio-Imaging of OrioN B. The scientists involved are from the Laboratoire d’Etudes du Rayonnement et de la Matière en Astrophysique et Atmosphères (Observatoire de Paris — PSL/CNRS/Sorbonne Université/Université de Cergy-Pontoise), Institut de Radioastronomie Millimétrique (IRAM), Centre de Recherche en Informatique, Signal et Automatique de Lille (CNRS/Université de Lille/Centrale Lille), Institut de Recherche en Astrophysique et Planétologie (CNRS/Université Toulouse III Paul Sabatier), Institut de Recherche en Informatique de Toulouse (CNRS/Toulouse INP/Université Toulouse III Paul Sabatier), Institut Fresnel (CNRS/Aix-Marseille Université/Centrale Marseille), Laboratoire d’Astrophysique de Bordeaux (CNRS/Université de Bordeaux), du Laboratoire de Physique de l’Ecole Normale Supérieure (CNRS/ENS Paris/Sorbonne Université/Université de Paris), Laboratoire Grenoble Images Parole Signal Automatique (CNRS/Université Grenoble Alpes), Instituto de Física Fundamental (CSIC) (Spain), National Radio Astronomy Observatory (United States), Chalmers University of Technology (Sweden), Cardiff University (United Kingdom), Harvard University (United States), Pontificia Universidad Católica de Chile (Chile).
**- The observations were made using one of IRAM’s radio telescopes, the 30-metre antenna located in Spain’s Sierra Nevada.
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