Monthly Archives: July 2021

Cell Biology

Bioengineering discovery paves way for improved production of bio-based goods using Bakers yeast – Newswise

Newswise Scientists have uncovered a way to control many genes in engineered yeast cells, opening the door to more efficient and sustainable production of bio-based products.

The study, published in Nucleic Acids Research by researchers from DSMs Rosalind Franklin Biotechnology Center in Delft, the Netherlands, and the University of Bristol, has shown how to unlock CRISPRs potential for regulating many genes simultaneously.

Bakers yeast, or Saccharomyces cerevisiae to give it its full name, is considered as a workhorse for biotechnology. Not only has it been used for producing bread and beer for thousands of years, but today it can also be engineered to produce an array of other useful compounds that form the basis of pharmaceuticals, fuels, and food additives. However, achieving optimal production of these products is difficult, requiring the complex biochemical networks inside the cell to be rewired and extended through the introduction of new enzymes and the tuning of gene expression levels.

Klaudia Ciurkot, first author of the study and an EU-funded industrial PhD student based at DSM stated: To overcome the challenges of optimising S. cerevisiae cells for bio-production, we explored the use of a less widely employed CRISPR technology based on the Cas12a protein. Unlike the Cas9 protein that is more commonly used, Cas12a can be rapidly programmed to interact with sequences that are responsible for controlling gene expression and easily targeted to many different sequences at the same time. This made it an ideal platform for carrying out the complex gene regulation often required for producing industrially relevant compounds.

She went on to add: What was particularly exciting for me was that this study is the first to demonstrate Cas12as ability to control gene expression in S. cerevisiae and through joint research across DSM and the University of Bristol, we were able to figure out the rules for how this system is best designed and used.

Thomas Gorochowski, a co-author on the work and Royal Society University Research Fellow based in the School of Biological Sciences at the University of Bristol further stated: It is hugely exciting that Cas12a has been shown to work so well for gene regulation in the yeast S. cerevisiae, an organism that has huge industrial importance. In addition, the systematic approach we have taken to pull apart and analyse the many difficult aspects of the system, act as a firm foundation for future optimisation.

In addition to analysing how the Cas12a-based system is best engineered, the scientists went on to show its use in robustly controlling the production of -carotene an industrially important compound used in production of food additives and nutraceuticals.

Ren Verwaal, senior author and Senior Scientist at DSM ended by stating: By demonstrating the capabilities of this system to control the biosynthesis of -carotene, we have opened the gates to its broader application for other key bio-based products. I cannot wait to see how our system is used to develop more sustainable production platforms for everyday products we all rely on.

The study was funded by the European Unions Horizon 2020 Research and Innovation Programme (ITN SynCrop) under the Marie Skodowska-Curie grant agreement No 764591, BrisSynBio, a BBSRC/EPSRC Synthetic Biology Research Centre, the Royal Society, and supported by the Bristol BioDesign Institute (BBI).

Paper

Efficient multiplexed gene regulation inSaccharomyces cerevisiaeusing dCas12a inNucleic Acids Research by Klaudia Ciurkot, Thomas E. Gorochowski, Johannes A. Roubos and Ren Verwaal.

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Bioengineering discovery paves way for improved production of bio-based goods using Bakers yeast - Newswise

Cell Biology

Cross-Resistance: One Cancer Therapy Can Undermine the Next – The Scientist

Targeted therapy and immunotherapy are often employed as a one-two punch to treat certain cancers, but sometimes this approach falls short. In a study published on July 15 in Nature Cancer, researchers found that dendritic cells, cells crucial for activating the immune system during immunotherapy, were less active and less numerous in mouse models of melanoma that had become resistant to targeted therapy, explaining why these tumors were less sensitive to immunotherapy. Stimulating dendritic cells restored the tumors response to immunotherapy.

This study provides mechanistic insight into a phenomenon that many melanoma experts have observed firsthand in the clinic and that has recently been described in retrospective studies: poor response to immunotherapy following the development of resistance to [targeted] therapy, Brent Hanks, a medical oncologist at Duke University who was not involved in this study, tells The Scientistin an email.Overall, this is an important contribution to melanoma research that may have implications in the management of other . . . cancers as well.

Indeed, it was early clinical data that sparked the interest of Anna Obenauf, a cancer researcher at the Research Institute of Molecular Biology in Vienna, Austria, who led the international team behind the new study. This is a clinical puzzle in a way, because how can these two different types of therapies be connected to each other, and this resistance to one lead to cross-resistance to the other? While targeted therapy blocks specific molecular pathways within cancer cells to stop proliferation, immunotherapy works by stimulating immune cells to eradicate tumor cells.

Their work showing that you can reverse the phenotype by adding in these dendritic cellstimulating agents was a nice proof of principle to show that it really was those cells that were being restricted.

Brian Ruffell, Moffitt Cancer Center

Obenauf and her colleagues started by recapitulating these clinical observations in a mouse model. Using two murine melanoma cell lines, the researchers established tumors in mice, which they treated with dabrafenib, a targeted therapy approved for use in the treatment of melanoma patients who have a mutation in the BRAF gene. Dabrafenib interrupts the MAP kinase pathway by inhibiting the B-Raf enzyme. While the tumors initially responded to the therapy, the cancer eventually relapsed and became resistant. Taking cells from the treatment-sensitive tumors and the treatment-resistant tumors, the researchers established cell lines. These cells were again injected into mice, which were treated with anti-PD-1 or anti-CTL-4 checkpoint inhibitors, immunotherapies aimed at releasing the brake on the immune system. Anti-PD-1 and anti-CTL-4 checkpoint inhibitors are also approved for treating certain patients with melanoma.

Using this approach, the researchers could implant resistant tumors into mice that had not been exposed first to the targeted therapy. This allowed the team to assess whether the targeted therapy has a direct effect on immune cells that could lead to immunotherapy resistance, or if something else is going on within the tumor. It turned out to be the latter. [Treatment-resistant] tumors are indeed cross-resistant to checkpoint inhibitors, says Obenauf.

Immunotherapies usually act by promoting T cell responses, so the group looked more closely at how the mices T cells behaved. While T cells were able to kill treatment-resistant tumor cells in vitro,when the researchers used a mouse model lacking endogenous T cells and added T cells they could track using luciferase, they saw that the T cells couldnt infiltrate the resistant tumor; the tumor kept growing. That has led us to the question [of] whether the tumor microenvironment is mediating resistance, Obenauf recalls.

So the researchers created mix-and-match melanoma mice. When they placed treatment-resistant tumor cells within a large treatment-sensitive tumor, the resistant tumor cells were killed. It seemed that treatment-sensitive tumors had an immune-permissive tumor microenvironment, Obenauf explains. Conversely, when the researchers placed treatment-sensitive cells within a large treatment-resistant tumor, the cells survived, apparently shielded from T cellmediated killing.

A tumor naive to targeted therapy (top) contains many more immune cells (red and green) than one that has acquired resistance (bottom).

IMP/Izabela Krecioch

RNA sequencing and flow cytometry analysis revealed that dendritic cells, a cell type crucial for activating the immune system during immunotherapy, were less abundant in mice with treatment-resistant tumors. When the researchers co-cultured dendritic cells with T cells, they saw that the dendritic cells from resistant tumors didnt activate T cells or spur them to proliferate as dendritic cells from sensitive tumors did. Collaborating with a team at the University of Sydney in Australia, the group acquired biopsies from patients with melanoma who were treated with a targeted therapy. Once the patients had become resistant to the treatment, their tumors contained fewer dendritic cells than before.

Collectively, the results suggest that a drop in dendritic cells generates an immune-evasive tumor microenvironment that is poorly responsive to subsequent checkpoint inhibitor immunotherapy, Hanks explains.

Notably, the effect was reversible. After treating the mouse models with experimental immunostimulants that mature and expand dendritic cell pools, the researchers saw greater numbers of T cells infiltrating the animals tumors, which shrunk as a result. Their work showing that you can reverse the phenotype by adding in these dendritic cellstimulating agents was a nice proof of principle to show that it really was those cells that were being restricted, Brian Ruffell, a cancer immunologist at the Moffitt Cancer Center who was not involved in this study, tells The Scientist.

I think this [study] really breaks down some of the biology of why youd want to treat patients with immunotherapy before you come in and allow resistant clones to develop from targeted therapy, Ruffell adds. From a basic science point of view, it really helps to add to the growing body of literature that we need to study all therapies in the context of immunotherapy or the immune system.

To understand how cells were developing cross-resistance, Obenauf and colleagues analyzed the transcriptomes of cells from both mice and patient samples that had grown resistant to a therapy that targeted the MAP kinase signaling pathway. A hyperactive MAP kinase pathway leads to uncontrolled cell proliferation but is turned down by the inhibitor, and tumors shrink in response. When tumors relapse and become resistant to inhibitors, the MAP kinase pathway is frequently re-activated.

We can very strongly conclude that that pathway reactivation is whats driving the immune therapy resistance.

Kristian Hargadon, Hampden-Sydney College

In their samples, Obenaufs team identified a signature of genes that are differentially expressed in targeted therapyresistant tumors versus sensitive tumors. Using a computational analysis to find the regulators that govern this genetic signature, the scientists found that the MAP kinase signaling pathway was turned on again in resistant tumors and apparently now driving immune evasion. It was surprising that the differences between the [treatment-sensitive] and the [treatment-resistant] tumors were predicted to be driven by the MAP [kinase] pathway, Obenauf says, because the [treatment sensitive] tumors, despite the MAPK pathway being already hyperactive, were so sensitive to immunotherapy, whereas the [treatment resistant] tumors, where the MAPK pathway is being re-activated, were so resistant to immunotherapy.

It turned out that the MAP kinase pathway in resistant tumors more strongly drives gene expression of target genes than it does in sensitive tumors. Components of the pathway also had access to new gene regulatory sites, meaning that they could drive the expression of different genes. The MAP kinase pathway, the same pathway that is very important for tumor initiation, is rewired and enhanced in this process of therapy resistance to establish a very different immune phenotype, says Obenauf.

We can very strongly conclude that that pathway reactivation is whats driving the immune therapy resistance, saysKristian Hargadon, a biologist at Hampden-Sydney College not connected with the study. And that is something that people would not have expected up until this point, yet now that explains a lot of previous observations.

Pulling all these strands together, the team treated mice that had targeted treatmentresistant tumors with a MEK inhibitor, which inhibits the MAP kinase pathway at a different point than does the targeted treatment used initially. In vitro, this inhibition reverted the expression of 80 percent of the genes that formed the signature for resistance back to the treatment-sensitive expression signature. When mice with a treatment-resistant tumor were given the MEK inhibitor, dendritic cells became more numerous and active, inducing T cell proliferation. When the researchers gave the animals immunotherapy, the T cells were able to bring the tumors under control, and the mice survived longer. The effects were quite drastic, indicating that the MAP kinase pathway along with the dendritic cells really are responsible for mediating cross-resistance, says Obenauf.

This is a very elegant, intricate, thorough study, Hargadon concludes. Several different tumor models were studied, several different therapeutic regimens were evaluated, all pointing to the same phenomenon here.

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Cross-Resistance: One Cancer Therapy Can Undermine the Next - The Scientist

Cell Biology

UCT student graduates after coming to SA with only R500 – IOL

By Staff Reporter Jul 13, 2021

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A UCT student from Namibia will finally graduate this week after she first came to the country with only R500.

Aune Angobe will graduate with an MSc Molecular and Cell Biology degree after achieving over 95% for her course.

She was raised by her grandparents in Ongongo village.

She said she was privileged to have grandparents who had always known the value of education.

I attended primary and secondary school in the northern part of Namibia under their tender care. Throughout my schooling journey Id always enjoyed science subjects, and I have no doubt that I was a scientist from birth.

Despite my poor family background, I studied hard and matriculated with good grades. In 2013, I was granted admission to the University of Namibia for an honours degree programme in science (microbiology), which was funded by a government loan, she said.

After completing her undergraduate studies in 2017 she never had any plans of studying further, but that all changed in 2018.

Angobe said she started growing a strong feeling for furthering her studies and searched for opportunities in numerous universities in Namibia and South Africa.

She he was admitted at UCT for her MSc in Molecular and Cell Biology, however funding was her biggest obstacle.

I remember clearly that when I arrived in Cape Town, I did not have funds for my accommodation and living expenses. I had only R500.

I was accommodated by a friend where I stayed for about two weeks. During this period, my supervisor, my friend and I were constantly worried about how I was going to survive, she said.

Angobe said she then decided to approach student housing where she cried her lungs out to put her plea across, and was eventually given accommodation.

She said she always felt like an outsider coming from a foreign country and struggled with the language barrier and being away from her support system.

Angobe added, My advice to others going through the same experience is that persistence is key. Where theres a will, theres always a way. So dont give up. To current students, self-confidence is key. Always believe in yourself and keep pushing, no matter the circumstances.

Associate Professor Inga Hitzeroth, Angobes supervisor said she is an amazing student who is a go getter.

What stood pout for me was how organised she was, she did not wait for you to organise stuff for her she was very proactive. She is very positive with a lovely personality, said Hitzeroth.

| Weekend Argus

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UCT student graduates after coming to SA with only R500 - IOL

Cell Biology

Computer simulation model identifies key factors for successful transit of sperm in the genital tract – News-Medical.net

A research team at the Humboldt University Berlin and the Leibniz Institute for Zoo and Wildlife Research (Leibniz-IZW) developed an agent-based computer model to simulate the journey of sperm cells through the female genital tract. Key factors for a successful transit could be identified without the use of animal experiments and were published in the scientific journal "PLoS Computational Biology".

During mating in wildlife species, males transfer millions of sperm into the female genital tract. On the way to the egg cell the sperm have to pass through the genital tract. Very few of the sperm cells actually succeed in passing through and reaching the vicinity of the egg cell. Those that do will then be conditioned for fertilisation. Mechanisms underlying sperm selection and, therefore, reproductive success are largely unknown, as their experimental study in the living organism is very difficult for both ethical and practical reasons. A deeper understanding of the factors which favour successful sperm migration and selection in the context of species-specific reproductive systems would be of great fundamental as well as of applied interest, since for threatened wildlife species this will help recognise reproductive problems and optimise assisted reproduction techniques such as artificial insemination.

The scientist team developed a spatio-temporal computer simulation model of the mammalian female genital tract, in which individual sperm cells were treated as independent agents equipped with a set of biophysical characteristics specifying concrete properties and subjected to specific rules for motion and interaction with the female genital tract. The first implementation used data on bovine genital tract geometry and the biophysical properties and principles of sperm motion of bovine sperm as observed in test tubes. Thus, sperm preferentially swam against a fluid stream (positive rheotaxis) and moved along wall structures (thigmotaxis).

In order to ensure that the model was reasonably realistic in depicting salient features of the interaction between sperm and the female genital tract, the simulation results were compared with published data derived from cattle. The simulation results demonstrated a close match with the observed timing and number of sperm actually reaching the entry of the oviducts.

As expected, we found that physical sperm characteristics such as velocity and directional stability are essential for successful sperm. In addition, the ability to swim against the mucus flow of cervical secretions as well as the ability of sperm to align to epithelial walls of the genital tract turned out to have a tremendous impact on the chances of a successful transit of sperm to the oviduct."

Jorin Diemer, Doctoral Student, Humboldt-Universitt zu Berlin

Karin Mller, leader of the andrology lab at Leibniz-IZW, concludes, "that these identified characteristics of sperm should be considered in future attempts to condition sperm in artificial selection procedures since natural selection processes are normally bypassed in reproductive test tube technologies."

This is of particular importance because a species-specific optimal time window for sperm accumulation in the oviduct exists in relation to the timing of ovulation when the oocyte is liberated for fertilisation. "The big advantage of our model is its flexibility, it can be extended and generalised to other systems," highlights Edda Klipp, leader of the Theoretical Biophysics department at Humboldt-Universitt zu Berlin.

Predictions from this computer simulation system have the potential to improve assisted reproduction in endangered species, livestock and perhaps humans without using animal experiments.

Source:

Journal reference:

Diemer, J., et al. (2021) Sperm migration in the genital tractIn silico experiments identify key factors for reproductive success. PLOS Computational Biology. doi.org/10.1371/journal.pcbi.1009109.

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Computer simulation model identifies key factors for successful transit of sperm in the genital tract - News-Medical.net

Human Behavior

Understanding human behavior as part of the IPM process – Pest Management Professional – Pest Management Professional magazine

PHOTO: KAMELEON007/ISTOCK / GETTY IMAGES PLUS/GETTY IMAGES

There are lots of ways to practice integrated pest management (IPM), but many pest management professionals (PMPs) would agree that IPM includes sanitation, exclusion and chemical control elements as part of a multifaceted approach to managing pest populations.

Many PMPs also would agree that one of the biggest hurdles to implementing a successful IPM program for clients are the clients themselves.

A commonly recommended IPM tactic is to keep doors and windows closed to keep flying insects from entering a building. Yet, why do we find this suggestion so often ignored? Here are a few questions to consider that could help get to the root cause:

Certainly, budget limitations can affect how quickly (and effectively) your exclusion recommendations are executed. And operational constraints can make sanitation tricky. But the biggest hurdle almost always is people.

By taking a closer look and understanding the underlying behaviors that are making effective pest management more difficult, the root cause can be addressed. Everybody wins.

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Understanding human behavior as part of the IPM process - Pest Management Professional - Pest Management Professional magazine

Human Behavior

Don’t rely too much on social media to understand human behavior – The Next Web

Since the early days of social media, there has been a lot ofexcitement about how data traces left behind by users can be exploited for the study of human behavior. Nowadays, researchers who were once restricted to surveys or experiments in laboratory settings have access to huge amounts of real-world data from social media.

The research opportunities enabled by social media data are undeniable. However, researchers often analyze this data with tools that were not designed to manage the kind of large, noisy observational sets of data you find on social media.

We explored problems that researchers might encounter due to this mismatch between data and methods.

What we found is that the methods and statistics commonly used to provide evidence for seemingly significant scientific findings can also seem to support nonsensical claims.

The motivation for our paper comes from a series of research studies that deliberately present absurd scientific results.

One brain imaging study appeared to show the neural activity of a dead salmon tasked with identifying emotions in photos. An analysis of longitudinal statistics from public health records suggested that acne, height, and headaches are contagious. And an analysis of human decision-making seemingly indicated people can accurately judge the population size of different cities by ranking them in alphabetical order.

Why would a researcher go out of their way to explore such ridiculous ideas? The value of these studies is not in presenting a new substantive finding. No serious researcher would argue, for example, that a dead salmon has a perspective on emotions in photos.

Rather, the nonsensical results highlight problems with the methods used to achieve them. Our research explores whether the same problems can afflict studies that use data from social media. And we discovered that indeed they do.

When a researcher seeks to address a research question, the method they use should be able to do two things:

For example, imagine you have chronic back pain and you take a medical test to find its cause. The test identifies a misaligned disc in your spine. This finding might be important and inform a treatment plan.

However, if you then discover the same test identifies this misaligned disc in a large proportion of the population who do not have chronic back pain, the finding becomes far less informative for you.

The fact the test fails to identify a relevant, distinguishing feature of negative cases (no back pain) from positive cases (back pain) does not mean the misaligned disc in your spine is non-existent. This part of the finding is as real as any finding. Yet the failure means the result is not useful: evidence that is as likely to be found when there is a meaningful effect (in this case, back pain) as when there is none is simply not diagnostic, and, as result, such evidence is uninformative.

Using the same rationale, we evaluated commonly used methods for analyzing social media data called null hypothesis significance testing and correlational statistics by asking an absurd research question.

Past and current studies have tried to identify what factors influence Twitter users decisions to retweet other tweets. This is interesting both as a window into human thought and because resharing posts is a key mechanism by which messages are amplified or spread on social media.

So we decided to analyze Twitter data using the above standard methods to see whether a nonsensical effect we call XYZ contagion influences retweets. Specifically, we asked

Does the number of Xs, Ys, and Zs in a tweet increase the probability of it being spread?

Upon analyzing six datasets containing hundreds of thousands of tweets, the answer we found was yes. For example, in a dataset of 172,697 tweets about COVID-19, the presence of an X, Y, or Z in a tweet appeared to increase the messages reach by a factor of 8%.

Needless to say, we do not believe the presence of Xs, Ys, and Zs is a central factor in whether people choose to retweet a message on Twitter.

However, like the medical test for diagnosing back pain, our finding shows that sometimes, methods for social media data analysis can reveal effects where there should be none. This raises questions about how meaningful and informative results obtained by applying current social science methods to social media data really are.

As researchers continue to analyze social media data and identify factors that shape the evolution of public opinion, hijack our attention, or otherwise explain our behavior, we should think critically about the methods underlying such findings and reconsider what we can learn from them.

The issues raised in our paper are not new, and there are indeed many research practices that have been developed to ensure results are meaningful and robust.

For example, researchers are encouraged to pre-register their hypotheses and analysis plans before starting a study to prevent a kind of data cherry-picking called p-hacking. Another helpful practice is to check whether results are stable after removing outliers and controlling for covariates. Also important are replication studies, which assess whether the results obtained in an experiment can be found again when the experiment is repeated under similar conditions.

These practices are important, but they alone are not sufficient to deal with the problem we identify. While developing standardized research practices is needed, the research community must first think critically about what makes a finding in social media data meaningful.

Article by Jason Burton, PhD researcher, Birkbeck, University of London; Nicole Cruz, Postdoctoral Research Associate, UNSW, and Ulrike Hahn, Professor of Psychology, Birkbeck, University of London

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Don't rely too much on social media to understand human behavior - The Next Web

Human Behavior

The Office Teaches Human Behavior to AI. Is That Really a Good Thing? – DesignNews

Humans know how to interact with each other using body language and communication, but robots developed to work alongside people dont have this innate sensibility.

Artificial intelligence (AI) is helping collaborative robots predict how a person likely will behave so it can respond accordingly. That is one reason why researchers have been using movies, popular TVs shows, and sports to train the system on human behavior.

Related: Will Advanced Artificial Skin Raise Hope in Restoring a Sense of Touch?

Researchers from Columbia Engineering developed a computer-vision technique for giving machines a more intuitive sense for patterns of human behavior by using higher-level associations between people, animals, and objects. They also leveraged an ancient form of geometry in their mathematical framework for how the AI functions.

The algorithm, the most accurate method to date for predicting video action events up to several minutes in the future, analyzed thousands of hours of movies, sporting events, and TV shows like The Office. In this way, it learned to predict hundreds of human interactions and activities, such as handshakes or fist bumps.

Related: Can New Materials Discovery get a Boost From Artificial Intelligence?

When the system cant predict the specific action, it finds a higher-level concept that is related to the action, such as, in the case of handshakes or fist bumps, the word greeting, researchers said.

Our algorithm is a step toward machines being able to make better predictions about human behavior, and thus better coordinate their actions with ours, said Carl Vondrick, assistant professor of computer science at Columbia, who directed the study. Our results open several possibilities for human-robot collaboration, autonomous vehicles, and assistive technology.

The team is no stranger to predictive machine learning. However, in the past, its efforts, like those of other scientists, included predicting just one action at a time. In this scenario, the algorithms must decide how to classify an actsuch as a hug, high five, or even someones lack of response, which would be classified as ignore.

However, when algorithms couldnt with high certainty identify an action, most couldnt find common threads between following potential options for action, researchers said.

To find a new way to develop long-range prediction models, Columbia Engineering Ph.D. students Didac Suris and Ruoshi Liu took a different approach based on the idea that not everything in the future is predictable, Suris said in a press statement.

When a person cannot foresee exactly what will happen, they play it safe and predict at a higher level of abstraction, he said in the statement. Our algorithm is the first to learn this capability to reason abstractly about future events.

In other words, the new AI model can recognize when it cant predict a future action with certainty and, like people do all the time, can make a guess as to what it will be by relating it to a concept.

To develop the model based on this idea, Suris and Liu used unusual geometries that arent commonly taught in high-school mathematics; instead, they date back to the time of ancient Greeks. These geometries have counter-intuitive properties in which the straight lines of typical geometry can bend, and triangles dont have three even sides but instead bulge, researchers said.

Using principles of this unusual type of geometry, researchers constructed AI models that could organize high-level concepts to predict future human behavior based on how predictable events are in the future, they said.

For example, humans know that swimming and running are both types of exercise. The system can categorize such related activities on its while also being aware of uncertainty. The latter instance provides more specific actions when there is a certainty and more generic predictions when there is not, researchers said.

Human behavior is often surprising, Vondrick said in a press statement. Our algorithms enable machines to anticipate better what they are going to do next.

Researchers wrote a paper on their work and presented the study at the International Conference on Computer Vision and Pattern Recognition on June 24.

While computers currently take action based on preprogramming, the model developed by the team can help give collaborative robots more spontaneity, just as humans do in their interactions, Liu said. This can help humans who in the future work closely with robots form a type of relationship, he said.

Trust comes from the feeling that the robot understands people, Liu explained in a press statement. If machines can understand and anticipate our behaviors, computers will be able to seamlessly assist people in daily activity.

Researchers plan to continue their work by verifying how the algorithm works outside of the lab in diverse settings rather than merely testing it on benchmark tasks, they said. If its successful, the model can develop and eventually use robots that can work alongside humans to improve our safety, health, and security, researchers said.

The team also plans to continue to improve the algorithms performance with larger datasets and computers, and other forms of geometry.

Stills from The Cider House Rules (top) and Mumford (bottom).

Elizabeth Montalbano is a freelance writer who has written about technology and culture for more than 20 years. She has lived and worked as a professional journalist in Phoenix, San Francisco, and New York City. In her free time, Elizabeth enjoys surfing, traveling, music, yoga, and cooking. She currently resides in a village on the southwest coast of Portugal.

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The Office Teaches Human Behavior to AI. Is That Really a Good Thing? - DesignNews

Human Behavior

Dont hike so close to me: How the presence of humans can disturb wildlife up to half a mile away – The Current GA

Millions of Americans are traveling this summer as pandemic restrictions wind down. Rental bookings and crowds in national parks show that many people are headed for the great outdoors.

This story also appeared in The Conversation

Seeing animals and birds is one of the main draws of spending time in nature. But as researchers who study conservation, wildlife and human impacts on wild places, we believe its important to know that you can have major effects on wildlife just by being nearby.

In a recent review of hundreds of studies covering many species, we found that the presence of humans can alter wild animal and bird behavior patterns at much greater distances than most people may think. Small mammals and birds may change their behavior when hikers or birders come within 300 feet (100 meters) the length of a football field. Large birds like eagles and hawks can be affected when humans are over 1,300 feet (400 meters) away roughly a quarter of a mile. And large mammals like elk and moose can be affected by humans up to 3,300 feet (1,000 meters) away more than half a mile.

Many recent studies and reports have shown that the world is facing a biodiversity crisis. Over the past 50 years, Earth has lost so many species that many scientists believe the planet is experiencing its sixth mass extinction due mainly to human activities.

Protected areas, from local open spaces to national parks, are vital for conserving plants and animals. They also are places where people like to spend time in nature. We believe that everyone who uses the outdoors should understand and respect this balance between outdoor recreation, sustainable use and conservation.

Pandemic lockdowns in 2020 confined many people indoors and wildlife responded. In Istanbul, dolphins ventured much closer to shore than usual. Penguins explored quiet South African Streets. Nubian ibex grazed on Israeli playgrounds. The fact that animals moved so freely without people present shows how wild species change their behavior in response to human activities.

Decades of research have shown that outdoor recreation, whether its hiking, cross-country skiing or riding all-terrain vehicles, has negative effects on wildlife. The most obvious signs are behavioral changes: Animals may flee from nearby people, decrease the time they feed and abandon nests or dens.

Other effects are harder to see, but can have serious consequences for animals health and survival. Wild animals that detect humans can experience physiological changes, such as increased heart rates and elevated levels of stress hormones.

And humans outdoor activities can degrade habitat that wild species depend on for food, shelter and reproduction. Human voices, off-leash dogs and campsite overuse all have harmful effects that make habitat unusable for many wild species. Disturbing shorebirds can cause them to stop eating, stop feeding their young or flee their nests, leaving chicks vulnerable.

For our study we examined 330 peer-reviewed articles spanning 38 years to locate thresholds at which recreation activities negatively affected wild animals and birds. The main thresholds we found were related to distances between wildlife and people or trails. But we also found other important factors, including the number of daily park visitors and the decibel levels of peoples conversations.

The studies that we reviewed covered over a dozen different types of motorized and nonmotorized recreation. While it might seem that motorized activities would have a bigger impact, some studies have found that dispersed quiet activities, such as day hiking, biking and wildlife viewing, can also affect which wild species will use a protected area.

Put another way, many species may be disturbed by humans nearby, even if those people are not using motorboats or all-terrain vehicles. Its harder for animals to detect quiet humans, so theres a better chance that theyll be surprised by a cross-country skier than a snowmobile, for instance. In addition, some species that have been historically hunted are more likely to recognize and flee from a person walking than a person in a motorized vehicle.

Generally, larger animals need more distance, though the relationship is clearer for birds than mammals. We found that for birds, as bird size increased, so did the threshold distance. The smallest birds could tolerate humans within 65 feet (20 meters), while the largest birds had thresholds of roughly 2,000 feet (600 meters). Previous research has found a similar relationship. We did not find that this relationship existed as clearly for mammals.

We found little research on impact thresholds for amphibians and reptiles, such as lizards, frogs, turtles and snakes. A growing body of evidence shows that amphibians and reptiles are disturbed and negatively affected by recreation. So far, however, its unclear whether those effects reflect mainly the distance to people, the number of visitors or other factors.

While theres much still to learn, we know enough to identify some simple actions people can take to minimize their impacts on wildlife. First, keep your distance. Although some species or individual animals will become used to human presence at close range, many others wont. And it can be hard to tell when you are stressing an animal and potentially endangering both it and yourself.

Second, respect closed areas and stay on trails. For example, in Jackson Hole, Wyoming, wildlife managers seasonally close some backcountry ski areas to protect critical habitat for bighorn sheep and reduce stress on other species like moose, elk and mule deer. And rangers in Maines Acadia National Park close several trails annually near peregrine falcon nests. This reduces stress to nesting birds and has helped this formerly endangered species recover.

Getting involved with educational or volunteer programs is a great way to learn about wildlife and help maintain undisturbed areas. As our research shows, balancing recreation with conservation means opening some areas to human use and keeping others entirely or mostly undisturbed.

As development fragments wild habitat and climate change forces many species to shift their ranges, movement corridors between protected areas become even more important. Our research suggests that creating recreation-free wildlife corridors of at least 3,300 feet (1,000 meters) wide can enable most species to move between protected areas without disturbance. Seeing wildlife can be part of a fun outdoor experience but for the animals sake, you may need binoculars or a zoom lens for your camera.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Human Behavior

This startup aims to improve workplace conversations with empathy-as-a-service software – GeekWire

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Most of us have had the experience of sending a text or email that came across sounding insensitive or angry, even though that wasnt our intent.

Unfortunately, the lack of social cues in such messaging makes it much easier to be misinterpreted. Depending on the communication, this can lead to misunderstandings, hurt feelings or worse. Thats a shortcoming Bellevue, Wash.-based mpathic wants to correct using empathic AI.

Drawing on insights and datasets assembled over the past decade, mpathic has set out to promote human connection and understanding in the workplace.

To do this, theyve created plugins that tie into their cloud-based empathy-as-a-service, or EaaS, to help humans talk to humans using real-time text corrections. This way, texts and emails can be reviewed and changes can be suggested prior to hitting Send. By adding these capabilities into platforms like Slack, or Gmail, mpathic hopes to bring more empathy to the corporate communication landscape.

We realized this could all be mediated with an AI empathy engine, almost like Grammarly for empathy, said co-founder Grin Lord. Weve had amazing developments in AI that allow us to do this now in real time, making this is the first time in human history that we can get real-time empathy correction thats dynamic.

In an example from a recent pitch, the service suggested replacing an inflammatory message like, Why does Nic schedule these meetings always at the last minute? Am I right? with a more open question: How do you feel about the meeting change?

Based on years of research on human interaction, mpathic offers a unique approach to guiding users. Lord, who has a Doctor of Psychology degree, initially based mpathics dataset on insights she gained doing research in the early 2000s at Harborview Medical Center in Seattle, the only Level I trauma center in Washington state.

During that time, Lord was part of a group doing research on empathic listening. Following a car crash, DUI drivers would frequently be brought into Harborview. Rather than giving the driver pamphlets or telling them what to do or shaming them, the researchers would listen to them, perhaps for 15 or 20 minutes, following specific protocols. In a randomized controlled trial, they saw a measurable drop in drinking by those drivers that lasted for up to three years, as well as a 48% reduction in hospital readmissions. Not only did this help the subject toward recovery, it led to significant cost reductions, as well as greater public safety.

Since then, Lord has been involved with other startups including Lyssn, a platform for assessing behavioral health provider empathy and engagement during clinical sessions.

Prior to its launch, the team behind mpathic started Empathy Rocks, which builds human connection using empathic AI through a gamified platform. The platform allows practitioners to improve their empathic listening skills while earning continuing education credits.

But it was during the early seed funding stage for Empathy Rocks that Lord and co-founder Nic Bertagnolli became aware they already had a viable product in the underlying empathy engine for that platform. Pivoting, they launched mpathic to make the engine more readily and widely available.

Developing both Grammarly for empathy and an API, mpathic wants to do more than simply promote good relations between employees. Given the expanding globalization of many corporations and the growing pool of employees from other parts of the country and the world, mpathic wants to provide human resources departments with a tool that can help smooth the onboarding of employees. Since different regions have different ideas and attitudes about what constitutes civil and sensitive behavior, mpathic can be used to help integrate new hires into their new team more rapidly.

Lord is quick to point out that mpathic doesnt just suggest text corrections but makes other kinds of behavioral suggestions, too. In this way, the user builds an understanding of empathic communication and behavior through context, use and repetition.

We actually make corrections that are very behavioral, said Lord. So, it may not even be a replacement of a word or transformation of the text. Instead, the AI may suggest calling a meeting or getting on the phone, because certain things dont need to be in an email.

Though mpathic grew out of Empathy Rocks, the gamified training platform continues to provide empathic listening training as it acquires new data thats used to train mpathics EaaS. The platform was designed by the teams empathy designer, Dr. Jolley Paige, who notes the many factors that need to be considered at a time when AI bias is such a concern.

We were thinking about gender, age, culture, where youre located in the country, but also about different abilities, too, Jolley said. So, if somebody has a language processing disorder, how would that impact how they interact with this game?

While some people may have concerns about using AI to modify human behavior, lots of companies see value in such an approach. Some of our early enterprise partners are looking at plugging mpathic into their Slack, Gmail or whatever, primarily because theyre interested in this idea of quickly onboarding cross-cultural and global teams, Lord said. I think it can be useful for unifying mission values language for a company.

Last month, mpathic was one of 14 startups that pitched at PIE Demo Day. PIE (Portland Incubator Experiment) is led by general manager Rick Turoczy and seeks to provide founders often first-time entrepreneurs with access to mentorship and networks.

Empathy Rocks and mpathic intentionally source and curate their data to include underrepresented voices and are part of All Tech is Human as well as other communities committed to ethical AI development.

Empathic AI is part of a much broader field of computer science, originally known as affective computing and more recently referred to as emotion AI or artificial emotional intelligence. Originating out of MIT Media Lab and other research institutes about 25 years ago, emotion AI involves systems that can read, interpret and interact with human emotions. Since emotion and especially empathy are central to the human condition, such work has the potential to make our technologies interact more easily, humanely and responsibly with people, both at home and in the workplace.

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This startup aims to improve workplace conversations with empathy-as-a-service software - GeekWire

Human Behavior

Getting dressed with help from robots – MIT News

Basic safety needs in the paleolithic era have largely evolved with the onset of the industrial and cognitive revolutions. We interact a little less with raw materials, and interface a little more with machines.

Robots dont have the same hardwired behavioral awareness and control, so secure collaboration with humans requires methodical planning and coordination. You can likely assume your friend can fill up your morning coffee cup without spilling on you, but for a robot, this seemingly simple task requires careful observation and comprehension of human behavior.

Scientists from MITs Computer Science and Artificial Intelligence Laboratory (CSAIL) have recently created a new algorithm to help a robot find efficient motion plans to ensure physical safety of its human counterpart. In this case, the bot helped put a jacket on a human, which could potentially prove to be a powerful tool in expanding assistance for those with disabilities or limited mobility.

Developing algorithms to prevent physical harm without unnecessarily impacting the task efficiency is a critical challenge, says MIT PhD student Shen Li, a lead author on a new paper about the research. By allowing robots to make non-harmful impact with humans, our method can find efficient robot trajectories to dress the human with a safety guarantee.

Robot-assisted dressing could aid those with limited mobility or disabilities.

Human modeling, safety, and efficiency

Proper human modeling how the human moves, reacts, and responds is necessary to enable successful robot motion planning in human-robot interactive tasks. A robot can achieve fluent interaction if the human model is perfect, but in many cases, theres no flawless blueprint.

A robot shipped to a person at home, for example, would have a very narrow, default model of how a human could interact with it during an assisted dressing task. It wouldnt account for the vast variability in human reactions, dependent on myriad variables such as personality and habits. A screaming toddler would react differently to putting on a coat or shirt than a frail elderly person, or those with disabilities who might have rapid fatigue or decreased dexterity.

If that robot is tasked with dressing, and plans a trajectory solely based on that default model, the robot could clumsily bump into the human, resulting in an uncomfortable experience or even possible injury. However, if its too conservative in ensuring safety, it might pessimistically assume that all space nearby is unsafe, and then fail to move, something known as the "freezing robot" problem.

To provide a theoretical guarantee of human safety, the team's algorithm reasons about the uncertainty in the human model. Instead of having a single, default model where the robot only understands one potential reaction, the team gave the machine an understanding of many possible models, to more closely mimic how a human can understand other humans. As the robot gathers more data, it will reduce uncertainty and refine those models.

To resolve the freezing robot problem, the team redefined safety for human-aware motion planners as either collision avoidance or safe impact in the event of a collision. Often, especially in robot-assisted tasks of activities of daily living, collisions cannot be fully avoided. This allowed the robot to make non-harmful contact with the human to make progress, so long as the robot's impact on the human is low. With this two-pronged definition of safety, the robot could safely complete the dressing task in a shorter period of time.

For example, lets say there are two possible models of how a human could react to dressing. Model One is that the human will move up during dressing, and Model Two is that the human will move down during dressing. With the teams algorithm, when the robot is planning its motion, instead of selecting one model, it will try to ensure safety for both models. No matter if the person is moving up or down, the trajectory found by the robot will be safe.

To paint a more holistic picture of these interactions, future efforts will focus on investigating the subjective feelings of safety in addition to the physical during the robot-assisted dressing task.

This multifaceted approach combines set theory, human-aware safety constraints, human motion prediction, and feedback control for safe human-robot interaction, says assistant professor in The Robotics Institute at Carnegie Mellon University Zackory Erickson. This research could potentially be applied to a wide variety of assistive robotics scenarios, towards the ultimate goal of enabling robots to provide safer physical assistance to people with disabilities.

Li wrote the paper alongside CSAIL postdoc Nadia Figueroa, MIT PhD student Ankit Shah, and MIT Professor Julie A. Shah. They will present the paper virtually at the 2021 Robotics: Science and Systems conference. The work was supported by the Office of Naval Research.

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Getting dressed with help from robots - MIT News