Category Archives: Human Behavior

Fear, Greed and the Evolution of Money in the Age of the Coronavirus Pandemic – Cointelegraph

The COVID-19 pandemic is not going to end soon. Fear and anxiety have skyrocketed, and nearly half of the people in the United States feel the coronavirus has harmed their mental health. People are scared, anxious, depressed, on edge and struggling to sleep through the night.

We watched as China took extreme measures to improve the coronavirus crisis there. We watched as Italy locked down the country and people scurried to other parts of Europe. We then watched as California Governor Gavin Newsom took early measures for the U.S. and locked down the state. We watched again as New York became the epicenter of the crisis.

In places like Hong Kong, which did a good job containing the virus, they got comfortable and went back to work, and you saw reinfection. The same thing will happen around the world. In Australia, they are prepared to put crisis measures in place for six months. They get it.

An economic downturn has been expected for awhile, and the International Monetary Fund has declared that we are in a recession, but the numbers suggest we are in a depression. Weve seen the ghost cities in China and how its economy was so heavily invested in a real estate bubble that will one day pop. Weve seen the national debt skyrocket here in the U.S. All that is just the tip of the iceberg.

Fear, uncertainty and doubt are wrecking economic havoc. Back in the 1920s and 1930s as the Great Depression set in, we werent worried about a third of the workforce being out of employment options because its jobs were deemed nonessential. The problems are just beginning.

A colleague of mine with CoinGenius conducted an experiment. He went into his bank branch and asked to withdraw $100,000. It was for personal reasons, he told the bank teller. They would not give it to him and said it would take at least two weeks. Did you know that when you deposit money into your bank you transfer the ownership of that money?

Now, add the coronavirus to the mix. It has exacerbated the situation. In the U.S., the Defense Production Act has mobilized businesses from Hanes to Tesla in the fight against COVID-19, and the Stafford Act has given the federal government unprecedented powers.

The situation will change human behavior. Once this is all over, the world will look very different. China will have more soft power, more social capital and more economic power, as theyre already reopening manufacturing, shipping and distribution. The virus is simply a catalyst for something that has been a long time coming: a global financial crisis and a new global order.

As I learned in 2001, 2008, 2011 and again now, when there is extreme market stress and the whole board is red the Dow Jones Industrial Average is down, Treasurys are down, Bitcoin (BTC) was down and crude oil is down people move to the sidelines to wait it out. If your option is to sell Bitcoin at $2,000 in order to feed your family or keep your house, you sell the Bitcoin at $2,000. It doesnt matter if you think it is going up or down because if youre not here tomorrow then it doesnt matter.

While everyone waits on the sidelines for now, soon there will be a great reallocation. Until then, the U.S. dollar will grow stronger, but that will reverse course once the countrys debt burden graces headlines. With Treasurys yielding negative returns, the safe havens of yore all of a sudden dont look so safe. Reallocation will come quicker than it did in 2008.

The debt-based world we created cant be saved. There is going to be pain. The challenge as a trader is developing a thesis you feel strongly about based on sound data and analytics, not emotion. Even then, the data provokes emotion. But you have to focus on education and your plan and stick to your thesis. All assets went down during the recent stock market crash because people want to wait to see whats next.

The coronavirus stimulus is worth $6 trillion. Many expect it to increase to $10 trillion. Thats a lot of U.S. dollars. Now compare that to the 21 million Bitcoin that will exist, much of which will be lost. Like gold and silver, Bitcoin is a commodity. It is worth what people are willing to pay for it. People will likely be willing to bet a lot more for it on the other side of this crisis.

Wall Street will eventually look to alternative assets, and they have options beyond gold and silver. Now, they have Bitcoin and cryptocurrencies. Theyve been primed by recent news to consider these alternative assets. We are hearing talk of the digitalization of the U.S. dollar and talk of blockchain-based supply chains. Everyone is working from home and getting a feel for productivity applications like Slack and Zoom. Next, theyre going to see the benefit of crypto as the digital revolution takes hold.

I have spoken with family offices and hedge funds across the world. They see the value of Bitcoin. These institutional backers bought Bitcoin when it dropped into the $4,000 range. More institutional capital will come into the space as a diversification play, if not as a store of value. Theyre looking to diversify away from traditional assets. They want the U.S. dollar. And then, theyll want commodities like precious metals and cryptocurrencies like Bitcoin. With that said, for now Bitcoin is not a proper safe haven. It remains a speculative hedge. Thats why in times of extreme panic it will continue to decrease in value alongside other assets.

Not only will cryptocurrencies become more palatable throughout this process, but so too will the underlying technology: blockchain. In California, weve already seen fraudulent coronavirus test kits and masks on the market. A blockchain-based supply chain could bring needed transparency.

Coronavirus information could more efficiently be shared with a blockchain-based system. Currently, authorities in disparate regions cobble information together. An enterprise implementation of blockchain could make this information available in real time.

As we recover from this crisis, the public will demand more trusted sources of information. Theyll want the transparency that blockchain brings. Entrepreneurs and large enterprises will work together on open-source projects that will set the standard for the next decade. People are talking about digital multiday voting if people cant make it out to the polls due to the virus. Such voting arrangements could threaten our democracy. Secure digital blockchain-based voting could diminish the risk of election fraud.

Another challenge is keeping positive during these troubled times. Humans are probably the most social creatures. We need social contact, and we need to look each other in the eyes. Thankfully, technology allows us to do that. If youre scared, anxious, depressed, on edge and struggling to sleep through the night, know youre not in this alone. And new technologies would help to bring people together and share information about the fluid situation in which weve all found ourselves.

The views, thoughts and opinions expressed here are the authors alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.

Jeremy Born is the principal founder and CEO of CoinGenius, a data and analytics platform for crypto traders. He is host of the April 10 virtual summit Fear, Greed and The Evolution of Money, which will feature speakers Brock Pierce, Nick Spanos, Vinny Lingham, Tron Black, Miko Matsumura and more.

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Fear, Greed and the Evolution of Money in the Age of the Coronavirus Pandemic - Cointelegraph

The essence of relationship – The Kashmir Images Newspaper

Added on April 10, 2020Other View

BY: Syed Maajid Rashid Andrabi

At a time when the whole world is grappling with Covid-19 pandemic and people are confined to their homes, it is worthwhile to mention that all the worldly gains at this point are of no value. What still matters, is the concern we have for our dear ones whom we had taken for granted until recently but now have again shifted our attention back to them.

With the ever-evolving social setup, we have come to a point where the essence of relationship has lost its meaning. We have inadvertently entered in a phase where the basic understanding of relationships is becoming a casualty. Though we all are living together, but there are separations in this togetherness! What is predominant today, is the absolute need-based interactions, which cease to exist once needs are fulfilled. We are so much occupied in our daily affairs that we seldom find time to meet and greet people whom we owe our immense gratitude.

We all are a part of a big system and our association with one another is a prerequisite to ensure that all of us advance together in a much dignified way. But going by the present trend, there are very less chances to progress that way. Instead, what seems to be unfolding is the inopportune way of our daily affairs, which has put a question mark on the etiquettes governing our social behavior. It seems that all of what we have been taught has lost its significance over time. It is a wonder to see how human behavior and priorities have changed, adding complexity to the relationships.

Having said it, whatever form they may be in, relationships are the basis which hold us together. They induce the spirit of contentment in our lives by their fruitful association. They add value to our lives and justify our existence. They make our life beautiful with pleasant experiences.

Looking at the present day societies, one only gets a gloomy representation of how the relationships have succumbed to the illusions of the time. No one seems to honor the obligations he or she is bound to do as a part of his/her affirmative stand towards any relationship. What is being witnessed is the unfamiliar attitude of people towards others. People have belittled relationships in all aspects as their preferences have changed, much due to their changed aspirations and their understanding of relationships.

Nowadays, human relationships have fallen victim of greed and are being looked as the means to attain material gains rather than recognizing them as a treasure to derive heavenly pleasure by gratifying the commitments to honor the sanctity of relationships. Our relationships are getting influenced by a variety of factors. From our own preoccupations to the technological interventions, all have a deep impact on our thought processes, which is evident in the form of behavioral changes. While there is always a thrust to stick to the moral lines to comply with the standards of human dignity, but to our utter shock these are remembered by only some while a majority seems to be indifferent and thus dont value human relationships. The technological influence on our relations is worth introspection. We all are living more in the virtual sphere around us which benefits none but puts all of us at the risk of losing our loved ones by creating a void amongst ourselves. We are faking ourselves to create a fabricated atmosphere of happiness which is impacting us in a negative way. Relationships are no more cherished and admired as was in the past. We have degraded the spirit of relationships to the extent that we are no more concerned about their welfare.

Humans though have evolved very much but their dependence on relationships has never diminished. We all need someone to be by our side in our thick and thin and relationships are the only means to realize this need. There are only some who truly value relationships and acknowledge their role. While it is an established fact that human relationships flourish when they are nurtured with love, care and trust; they cease to survive if left unattended. However, those who seem to be indifferent towards honoring relationships end up with the feeling of emptiness and live a solitary life which is worth no good irrespective of how successful they are in accumulating wealth and other material things.

The path to a successful relationship lies in appreciating every moment of association and being courteous to one another in lending a helping hand. We need to accept any relationship by heart rather than keeping it to words only. There is no way human civilization can prosper if the respect for human relationships doesnt become its inherited trait.

Time has come when we have to reverse this unpropitious trend and begin to value the essence of relationship, lest we fall prey to unceremonious pretences which would not only erode the social fabric but also deal a strong blow to our decency. It would set a wrong notion for the generations to come which would eventually live up with the same discourteous practice. We need to change.

(The author can be reached at syedmajidr59@gmail.com)

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The essence of relationship - The Kashmir Images Newspaper

Pandemic decision-making: Why Humans aren’t wired to understand the coronavirus – NBCNews.com

When people make decisions, psychologists have found, two main systems influence their thinking and decision-making: gut feelings and logical analysis.

One is more immediate and based on intuition. The other is slower, more thoughtful and based on evidence.

"The fast-thinking and gut feelings usually dominate," said Paul Slovic, a professor of psychology at the University of Oregon and author of several books on risk perception and behavioral sciences. "This is because it's natural and easy, and most of the time we trust our intuitions to give us good guidance in our daily lives."

Human intuition, however, is not particularly well geared to a pandemic.

Around the world, the coronavirus has forced schools to close, economies to stall and countries to lock down their borders. It has also solicited a wide range of reactions from people, many of which psychologists say show just how difficult it can be for the human mind to comprehend risk.

That mental disconnect can be explained by how humans react to fear and their perception of risk and the biases that color both. The concept of risk is difficult for the human mind to grasp because it's typically based on perceived threats rather than any quantifiable measure of the threat itself, Slovic said.

"Danger is real, but risk is a construct that has many different meanings and definitions," Slovic said. "It's a word we attach to things that are dangerous to try to gauge how dangerous they are to us."

Slovic and his colleagues have conducted extensive research showing that from a psychological standpoint, risk is subjective.

"It's based on judgments and assumptions," he said. "And we basically base these judgments on our feelings."

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Even before the coronavirus spread widely in the U.S., fear of the pathogen was high, which led people in some cities to hoard toilet paper and other supplies. And following reports that the virus emerged in Wuhan, China, cities such as Los Angeles, San Francisco and New York reported racist attacks against Asian Americans.

When there is a lack of information, emotions can fill in the gaps, and when those emotions are miscalibrated, they can lead to bad judgment or dangerous behavior.

"When you don't have enough information or you don't have accurate information, the fear you're feeling makes every threat seem much more likely," said David DeSteno, a professor of psychology at Northeastern University in Boston.

On the other hand, there have been instances of people not taking the coronavirus threat seriously enough. In the U.S., the federal government has been criticized for its slow response. On Feb. 28, President Donald Trump downplayed the virus' threat at a rally, accusing Democrats of overhyping the outbreak.

And even after some states enacted strict, statewide stay-at-home orders to slow the spread of the coronavirus, some governors resisted issuing similar social distancing measures. As of Wednesday, five states had no official stay-at-home orders, even though Dr. Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases and a member of the White House coronavirus task force, said last week that he doesn't understand why Americans in all states aren't operating under the same guidelines.

Part of the reason is that responses to the outbreak have become politically charged, according to Slovic.

"Even in this case, where we have a devastating reality that should be bringing us all together, we still see partisan divisions," he said.

That introduces biases into the mix, which can sway people's perceptions. With social media, for example, information can be passed around rapidly, but so can misinformation.

"We tend to tailor it so that we only watch the stuff that tells us what we want to believe or things along our own ideological biases," Slovic said. "This all leads to tremendous polarization."

Even without biases, pandemics can be particularly difficult to comprehend because they operate on the principle of exponential growth. In other words, outbreaks can escalate quickly. A location with one infection could see hundreds after a week, multiplying into the thousands not long after.

"The way the virus spreads, everything is under control until it isn't that's the nature of exponential growth," Slovic said. "Our minds think linearly, at a constant rate of growth, but this is a nonlinear process. It's a natural tendency for most of us to underestimate the speed at which an exponential process will take off, and then suddenly it overwhelms us."

Slovic said he worries that the same false sense of comfort drives many people to downplay the dangers of climate change.

"We're reacting very strongly to this pandemic now because the crisis is very close to us," he said. "Fortunately, we don't have these pandemics very often, but we do have some very serious natural disasters, whether it's hurricanes, floods, earthquakes or massive forest fires. Yet we can be rather blas about natural disasters, and even when they hit, people go back to the same places and rebuild and expose themselves to similar risks again."

Apossible explanation for this imbalance is that the coronavirus and COVID-19, the disease it causes, are new.

"The unknown factor definitely makes it worse," DeSteno said. "It's a new virus, so nobody has immunity to it. Anybody can succumb to it, so it's almost universal."

The virus is also an invisible threat, which can heighten its perceived risk, according to DeSteno.

"If there's a flood or a fire, you can see that," he said. "Here, you can't see germs, so we don't know where they are, and that means they could be anywhere."

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Pandemic decision-making: Why Humans aren't wired to understand the coronavirus - NBCNews.com

Social distancing works just ask lobsters, ants and vampire bats – The Conversation Africa

Social distancing to combat COVID-19 is profoundly impacting society, leaving many people wondering whether it will actually work. As disease ecologists, we know that nature has an answer.

Animals as diverse as monkeys, lobsters, insects and birds can detect and avoid sick members of their species. Why have so many types of animals evolved such sophisticated behaviors in response to disease? Because social distancing helps them survive.

In evolutionary terms, animals that effectively socially distance during an outbreak improve their chances of staying healthy and going on to produce more offspring, which also will socially distance when confronted with disease.

We study the diverse ways in which animals use behaviors to avoid infection, and why behaviors matter for disease spread. While animals have evolved a variety of behaviors that limit infection, the ubiquity of social distancing in group-living animals tells us that this strategy has been favored again and again in animals faced with high risk of contagious disease.

What can we learn about social distancing from other animals, and how are their actions like and unlike what humans are doing now?

Social insects are some of the most extreme practitioners of social distancing in nature. Many types of ants live in tight quarters with hundreds or even thousands of close relatives. Much like our day care centers, college dormitories and nursing homes, these colonies can create optimal conditions for spreading contagious diseases.

In response to this risk, ants have evolved the ability to socially distance. When a contagious disease sweeps through their society, both sick and healthy ants rapidly change their behavior in ways that slow disease transmission. Sick ants self-isolate, and healthy ants reduce their interaction with other ants when disease is present in the colony.

Healthy ants even close rank around the most vulnerable colony members the queens and nurses by keeping them isolated from the foragers that are most likely to introduce germs from outside. Overall, these measures are highly effective at limiting disease spread and keeping colony members alive.

Many other types of animals also choose exactly who to socially distance from, and conversely, when to put themselves at risk. For example, mandrills a type of monkey continue to care for sick family members even as they actively avoid sick individuals to whom they are not related. In an evolutionary sense, caring for a sick family member may allow an animal to pass on its genes through that family members offspring.

Further, some animals maintain essential social interactions in the face of sickness while foregoing less critical ones. For example, vampire bats continue to provide food for their sick groupmates, but avoid grooming them. This minimizes contagion risk while still preserving forms of social support that are most essential to keeping sick family members alive, such as food sharing.

These nuanced forms of social distancing minimize costs of disease while maintaining the benefits of social living. It should come as no surprise that evolution favors them in many types of animals.

Human behavior in the presence of disease also bears the signature of evolution. This indicates that our hominid ancestors faced many of the same pressures from contagious disease that we are facing today.

Like social ants, we are protecting the most vulnerable members of our society from COVID-19 infection by ensuring that older individuals and those with pre-existing conditions stay away from potentially contagious people. Like monkeys and bats, we also practice nuanced social distancing, reducing non-essential social contacts while still providing essential care for sick family members.

There also are important differences. For example, in addition to caring for sick family members, humans sometimes increase their own risk by caring for unrelated individuals, such as friends and neighbors. And health care workers go further, actively seeking out and helping precisely those who many of us carefully avoid.

Altruism isnt the only behavior that distinguishes human response to disease outbreaks. Other animals must rely on subtle cues to detect illness among group members, but we have cutting-edge technologies that make it possible to detect pathogens rapidly and then isolate and treat sick individuals. And humans can communicate health threats globally in an instant, which allows us to proactively institute behaviors that mitigate disease. Thats a huge evolutionary advantage.

Finally, thanks to virtual platforms, humans can maintain social connections without direct physical contact. This means that unlike other animals, we can practice physical rather than social distancing, which lets us preserve some of the important benefits of group living while minimizing disease risk.

The evidence from nature is clear: Social distancing is an effective tool for reducing disease spread. It is also a tool that can be implemented more rapidly and more universally than almost any other. Unlike vaccination and medication, behavioral changes dont require development or testing.

However, social distancing can also incur significant and sometimes unsustainable costs. Some highly social animals, like banded mongooses, do not avoid group members even when they are visibly sick; the evolutionary costs of social distancing from their relatives may simply be too high. As we are currently experiencing, social distancing also imposes severe costs of many kinds in human societies, and these costs are often borne disproportionately by the most vulnerable people.

Given that social distancing can be costly, why do so many animals do it? In short, because behaviors that protect us from disease ultimately allow us to enjoy social living a lifestyle that offers myriad benefits, but also carries risks. By implementing social distancing when its necessary, humans and other animals can continue to reap the diverse benefits of social living in the long term, while minimizing the costs of potentially deadly diseases when they arise.

Social distancing can be profoundly disruptive to our society, but it can also stop a disease outbreak in its tracks. Just ask ants.

[You need to understand the coronavirus pandemic, and we can help. Read The Conversations newsletter.]

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Social distancing works just ask lobsters, ants and vampire bats - The Conversation Africa

Force production of human cytoplasmic dynein is limited by its processivity – Science Advances

Abstract

Cytoplasmic dynein is a highly complex motor protein that generates forces toward the minus end of microtubules. Using optical tweezers, we demonstrate that the low processivity (ability to take multiple steps before dissociating) of human dynein limits its force generation due to premature microtubule dissociation. Using a high trap stiffness whereby the motor achieves greater force per step, we reveal that the motors true maximal force (stall force) is ~2 pN. Furthermore, an average force versus trap stiffness plot yields a hyperbolic curve that plateaus at the stall force. We derive an analytical equation that accurately describes this curve, predicting both stall force and zero-load processivity. This theoretical model describes the behavior of a kinesin motor under low-processivity conditions. Our work clarifies the true stall force and processivity of human dynein and provides a new paradigm for understanding and analyzing molecular motor force generation for weakly processive motors.

Cytoplasmic dynein 1 (hereafter referred to as dynein) is a large >1.5-MDa multiprotein complex (1). As a member of the AAA+ [adenosine triphosphatase (ATPase) associated with various cellular activities] protein family (2), mammalian dynein performs a multitude of cellular functions. These include minus enddirected microtubule (MT) transport of various cargos (3), mitosis (4), nuclear positioning (5), and cell migration (6). The dynein holoenzyme is a dimer of heavy chains (HCs); these comprise the ring-shaped motor domain (MD; containing six AAA+ domains), the MT-binding stalk, and the dimerization domain (tail) (7). The latter forms a scaffold for associated subunits, such as light chains (LCs), light intermediate chains (LICs), and intermediate chains (ICs), as well as for regulatory cofactors, such as dynactin, Lis1 (lissencephaly protein 1), NudE (nuclear distribution factor E), and NudEL (NudE-like protein) (1).

In this work, we sought to clarify longstanding uncertainties regarding mammalian dyneins single-molecule, intrinsic stall force (maximal sustained force generation in the absence of cofactors) and processivity (ability to take multiple steps before MT dissociation, as measured by run length). Over the past several decades, dyneins molecular function has been investigated both in vivo and in vitro. Until recently, most studies have focused on dyneins from Dictyostelium discoideum and Saccharomyces cerevisiae (yeast) due to their stability, ease of genetic manipulation, and established purification protocols (8, 9). However, there are notable differences between the function of yeast dynein and that of higher eukaryotes (10). Even in the absence of cofactors, yeast dynein is highly processive. The run length of isolated yeast dynein is in the range of 1 to 3 m (8, 11), with a measured stall force of ~4 pN (12, 13) [we show here that the previously reported 7-pN stall force (14) is likely the result of an unintended electronic low-pass filtering of the trapping data; see Supplementary Materials]. In contrast, the study of mammalian dynein has yielded variable results. A wide range of single-molecule stall forces, between ~0.8 and 7 pN (1521), have been reported, and single-molecule processivity has ranged from immeasurable (22, 23), to over several hundred nanometers (17), to 1 m (20, 24, 25). A number of technical issues have likely contributed to the variability of both processivity and stall force measurements for mammalian dynein. These include variations in protein purification, protein labeling, and microscopy techniques. We discuss these issues in detail below, but much of the challenge in these measurements actually arises from the relatively low intrinsic processivity of mammalian dynein. Whereas yeast dynein frequently achieves its stall force before MT dissociation (as determined by a plateau or stalling of its displacement versus time) (14), mammalian dyneins low processivity results more frequently in short displacements with runs terminated by detachment from the MT rather than sustained force plateaus (17, 26). Do the forces achieved represent stall force or simply premature MT dissociation due to low processivity? How can stalling be defined objectively? Is processivity affected by force? Answering these questions in the absence of dynein cofactors has been challenging but important for at least two reasons. First, there are reports of dynactin-independent recruitment of dynein to organelles (27, 28), and understanding dynein function under these circumstances will be greatly aided by accurately characterizing its single-molecule processivity and force generation in isolation. Second, it is essential to study dynein function in isolation to understand the effects of its various cofactors. Despite variable processivity in vitro (see further discussion below), mammalian dynein complexes have been shown to move over several micrometers in vivo (17, 18, 29). However, this appears to be mediated by the presence of dynactin and other cofactors.

Recent publications have shown that when mammalian dynein is complexed with dynactin via the coiled coilcontaining cargo adaptor, Bicaudal-D2 (BicD2), dynein moves over several micrometers in vitro [~5 (30) and ~9 m (31)]. This augmented processivity appears to result from relief of a low MT-affinity, autoinhibited phi-particle (32) conformation [in which the stalks cross each other (7, 26)] by dynactin and BicD (as well as other coiled coilcontaining cargo adaptors) (7). Cofactor binding reorients the two MDs parallel to each other and converts mammalian dynein from a weakly processive (10) to an ultraprocessive motor (30, 31, 33). It is likely that, by virtue of increased processivity, these dynein-dynactin-BicD2 complexes exhibit more classic stalling behavior and greater stall forces in optic tweezers assays (12, 34). However, without knowing dyneins intrinsic function, it is impossible to understand the effects of its cofactors on force generation. Is the stall force actually increased or is it the same but simply easier to observe? Might cofactor association increase processivity while actually reducing stall force? Moreover, although dynein appears to be more processive when bound by these cofactors, its intrinsic processivity is still somewhat uncertain (10, 22). We clarify several of these questions here by observing both processivity and force generation in an optic tweezers assay and developing a theoretical model to describe these observations and predict both intrinsic processivity and stall force despite the difficulty of observing these attributes directly. Below, we discuss some of the technical challenges in measuring mammalian dynein single-molecule function.

Different purification strategies and protein sources [brain (16, 17, 20, 23, 25) and tissue culture (26, 35)] may have resulted in varying fractions of inactive or conformationally inhibited proteins [see, e.g., (30, 31)]. In addition, when determining dynein processivity using optical microscopy (using attachment to fluorescence tags or visible beads to track the motor), the detection limit is typically several hundred nanometers. Therefore, run lengths for single motors have remained immeasurable in some studies (22, 23). In contrast, others have reported a run length of 1.3 m for 1% of the cofactor-free dynein molecules (30). When bound to beads, mammalian dynein generates movement over ~0.7 to 1 m (20, 24, 36). Conversely, using both Qdots suited for high-precision fluorescence microscopy and bead assays, Ori-McKenney et al. (17) measured a run length of ~0.3 m for a subfraction of the motor population. In addition to a low fraction of moving motors, diffusive bidirectional events have also often been reported instead of, or in addition to, unidirectional movement (17, 20, 22, 23, 25). A recent study by Torisawa et al. (26) reported bidirectional diffusion biased toward the MT minus end, with an average run length of ~100 nm. In this case, it was attributed to an autoinhibited conformation of dynein dimers in the absence of cofactors. However, even in the presence of dynactin/BicD2, the diffusive fraction can constitute ~30% of all data (30).

In the present study, we analyze native human dynein purified from human embryonic kidney (HEK) 293 cells (37, 38) using single-molecule optical tweezers. With this method, we confirm a very low fraction of stalling events (200 ms), similar to other studies (17), which complicates stall force analysis. On the basis of the reported submicroscopic run length of mammalian dynein (10), we hypothesized that the ability to observe motor stalling is directly limited by the motors weak processivity. That is, dynein force generation is commonly observed at trap stiffnesses of 0.01 to 0.02 pN/nm, resulting in the motor having to travel 50 to 100 nm to reach a force of 1 pN. Our analysis, which included a range of trap stiffnesses, indeed reveals a novel hyperbolic trap stiffness dependence for the average detachment forces, with a force plateau of ~2 pN at saturating adenosine triphosphate (ATP) concentrations. This force plateau coincides with the measured stall force at high trap stiffnesses. We further derive a theoretical model that accurately describes the hyperbolic trap stiffness dependence and the experimentally measured force plateau. This equation is independent of motor compliance and allows for determination of the maximal force generation of dynein without the observation of motor stalling. We also find that a hyperbolic increase in measured forces correlates with a hyperbolic decrease in the corresponding distance displacements. Dyneins run length at zero load can therefore be estimated from the y intercept at a trap stiffness of zero, resulting in a zero-load run length of ~100 nm. Application of our experimental framework to full-length yeast dynein and kinesin-1, both of which are highly processive motors, reveals that, at low ionic strength, both motors are insensitive to changes within a practical trap stiffness range, while a similar trap stiffness dependence is observed for kinesin-1 at elevated ionic strengths. Our study, therefore, provides a method for determining the force-free processivity and stall force of mammalian dynein (and possibly other cytoskeletal motors), without the need to directly measure dynein displacements at zero load or to directly measure motor stalling. Thus, our work clarifies longstanding discrepancies regarding mammalian dynein single-molecule functional properties and provides a novel framework for studying weakly processive molecular motors in general.

To determine the motion and force generation capabilities of human dynein, we used a native human dynein containing a multifunctional streptavidin- and green fluorescent protein (GFP)tagged intermediate chain (mfGFP-IC) (37, 38). Tagged dynein complexes were purified from HEK293 cells using the streptavidin tag in the mfGFP-IC construct via one-step StrepTrap column purification (fig. S1A). This yielded mostly dynein HC dimers, as confirmed by blue native polyacrylamide gel electrophoresis (BN-PAGE; fig. S1B). Western blot analysis further confirmed the copurification of dynein subunits (IC, LC, and LIC) and showed trace amounts of cofactors, including Lis1, nuclear distribution element 1 (Nde1), and dynactin p150 subunit (fig. S1C). To remove inactive motors, bound cofactors, dynein aggregates, and other possible contaminants, we then conducted a second purification step using the MT-binding and release (MTBR) assay. Here, dynein was bound to MTs in the presence of the ATP analog, AMP-PNP (adenylyl-imidodiphosphate). Active motors were then released by the addition of ATP and salt (fig. S1D). After MTBR, cofactors were no longer detectable by Western blot, and when adhered to carboxyl-trapping beads, the purified dynein complex exhibited exclusively minus enddirected motility (see Supplementary Materials for additional information about the characterization of the StrepTrap- and MTBR-purified protein fractions).

To next test whether native human dynein is a weakly processive motor in isolation, we conducted optical trapping studies (Fig. 1, A and B). For these analyses, dynein was adhered to 1-m-diameter carboxyl beads, and processive motion was measured by determining the motile bead fraction as a function of relative motor bead concentration (Fig. 1C). In contrast to single-molecule fluorescence microscopy and MT-landing rate assays that have a resolution limit of several hundred nanometers (22, 30, 31), bead displacements in the sub-100-nm range can be resolved in the optical trap (Fig. 1B). Because most publications on mammalian dyneins [see, e.g., (15, 17, 18, 20, 21)] have reported stall forces between 1 and 2 pN, we chose a trap stiffness of k = 0.01 pN/nm, which is expected to result in bead-trap separations of 100 to 200 nm. A bead was counted as moving if its displacement was 50 nm, equivalent to 0.5 pN. The dilution curve obtained from this assay was then analyzed on the basis of two models [see Materials and Methods, Supplementary Materials, and (3941)]: one describing force generation by one or more motors (processive model) and the other describing force generation driven by two or more motors (nonprocessive model). We found that our data were best described by the processive model [R2 = 0.996; Akaikes information criterion (AIC) probability of 99.99%]. This was true even when beads displaying exclusively large (up to 50 nm) single forward-backward steps, without any resolved intermediate steps (Fig. 1B), were counted as nonmoving (R2 = 0.992; AIC probability of 99.97%; fig. S2A) [we suspect that these displacements are generated by dynein motors that transition reversibly from the autoinhibitory to the open conformation (7)]. We, therefore, find that under these experimental conditions, native human dynein is a weakly processive motor (17).

(A) Schematic of the optical tweezers assay (not to scale). A 0.9-m-diameter carboxyl bead is nonspecifically bound to a purified single human dynein and is trapped by a near-infrared optical trapping beam focused via a high numerical aperture microscope objective lens. The trap holds the bead directly above an MT that is covalently linked to the glass surface of the coverslip. When the dynein binds to and moves along the MT in the presence of ATP, it pulls the attached bead with it. The trap resists this motion, exerting a force F = k x on the bead-motor complex, where k is the trap stiffness and x is the distance from the trap center to the center of the bead. (B) Example traces at 1 mM ATP and k = 0.01 pN/nm (see also fig. S2B). Stalling events (red horizontal bars) can be observed but are rare. Fast events, including large single forward-backward steps without any resolved intermediate steps (black star), are frequent. Events that are counted as force generation events are marked with black arrows. (C) Dilution curve counting beads as moving if forces equaled or exceeded 0.5 pN. Error bars were calculated assuming a binomial distribution. Twelve to 85 beads were tested for each dilution (Ntotal = 318). The curves are fits to equations assuming processive motors (Eq. 6, = 3.3 0.1, solid line, R2 = 0.996) and nonprocessive motors (Eq. 7, = 7.9 0.7, dashed line, R2 = 0.949).

The stall force of single human dynein complexes at saturating [ATP] was next determined by analyzing trapping data obtained at the single-molecule level [in this case, 30% bead motility (3941)]. Stalling was defined as a 200-ms force plateau [as in (17)], excluding jump-like events to such a plateau (Fig. 1B). The resulting stall force histogram (Fig. 2A) followed a Gaussian distribution, with an average of 0.9 0.3 pN (SD; N = 77; k = 0.01 pN/nm), which is consistent with previously published stall forces for mammalian dyneins (15, 17, 18, 20, 21). However, the vast majority of runs did not result in stalling, and in most cases, dynein released prematurely from the MT (Fig. 1B, middle). Hence, the stall percentage (number of stalling events/number of all events 100%) was extremely low (6.8%). In addition, the detachment force, defined as the average maximum force of all events, was found to be significantly lower (0.63 0.01 pN, SEM) than the stall force (0.89 0.02 pN, SEM) (Fig. 2, A and B). It has been previously noted (42) that motors with low processivity, such as kinesin-5 (Eg5), rarely show clear stalling events before dissociation. Our observations therefore suggest that the limited processivity of human dynein in isolation may limit the observed stall force in the optical tweezers assay.

(A) Stall force histograms of StrepTrap-purified and MT-binding released human dynein counting stalling events 200 ms and excluding jump-like behavior measured at k = 0.01 pN/nm (black bars) and k = 0.03 pN/nm (gray bars). The Gaussian distributions (solid curves) are centered at 0.9 0.3 pN (SD; N = 77) and 1.3 0.5 pN (SD; N = 48). (B) All measured forces (detachment forces) acquired at k = 0.01 pN/nm (mean force: 0.64 pN; N = 572) and k = 0.03 pN/nm (mean force: 1.1 pN; N = 225). (C) Example record showing force generation events of a single dynein molecule bound to trapping bead measured at 0.01 pN/nm (left) and subsequently at 0.03 pN/nm (right), demonstrating an increase in force generation with increasing trap stiffness.

To determine whether dyneins weak processivity did decrease its measured force generation, we repeated the optical trapping experiments at a trap stiffness of 0.03 pN/nm (such that dynein has to move only ~33 nm to reach a force of 1 pN). As expected, both the stall forces (Fig. 2A) and the detachment forces (Fig. 2B) increased with elevated trap stiffness (for all presented detachment force analyses, we took all events into account that were identifiable as force generation events, even when they occurred below 0.5 pN). Next, we raised the trap stiffness to 0.03 pN/nm for a given dynein-bound bead, following data acquisition at 0.01 pN/nm, to show that individual motors exhibiting one force generation behavior at a lower trap stiffness exhibit larger peak forces at higher trap stiffness. As expected, we found that force output increased as the trap stiffness was raised (Fig. 2C). We then determined whether the increased force generation at higher trap stiffnesses is reversible or whether dynein switches into a persistent high-force state, even if the trap stiffness is reduced again. To distinguish between these possibilities, we changed the trap stiffness from 0.06 to 0.01 pN/nm and back to 0.06 pN/nm (fig. S3). From these experiments, we found that the trap stiffnessinduced increase in measured force generation is reproducible.

To determine dyneins maximal force-generating capabilities, the impact of its weak processivity must be negligible. Therefore, we adjusted the trap stiffness over a wide range of values (0.005 to 0.1 pN/nm), thereby probing the effects of run length on the measured forces. The force histograms obtained at 0.01 and 0.02 pN/nm (Fig. 2B and fig. S4) are in the range of previously reported values (15, 17, 18, 20, 21, 26). In addition, the obtained mean detachment forces are statistically indistinguishable from the mean detachment forces measured at the same trap stiffnesses for dynein motors that are attached specifically to the beads coated with anti-GFP antibodies against the GFP-tagged IC of dynein (P < 0.97 at 0.01 pN/nm and P < 0.83 at 0.02 pN/nm for nonspecific versus specific bead coupling; fig. S4), demonstrating that the measured detachment forces do not depend on the coupling strategy. When plotting the cumulative distribution functions (CDFs) of the detachment forces measured at low to high trap stiffnesses (Fig. 3A), a continuous increase in force was observed, a phenomenon that has, to our knowledge, not been reported previously. In contrast, the CDFs of the detachment distances (Fig. 3B) showed a continuous decrease with increasing trap stiffness, consistent with the trend observed for the three initially tested trap stiffnesses of 0.01, 0.02, and 0.03 pN/nm. This dependence on trap stiffness for both the average detachment force and displacement distance appears to follow a hyperbolic function (Fig. 3C), with the forces approaching a force plateau at infinite trap stiffness and the distances decaying to zero. We, therefore, hypothesized that this detachment force plateau corresponds to the maximal force that a single mammalian dynein motor can generate in isolation.

(A and B) CDFs of measured detachment forces (A) and corresponding displacements (B) acquired at various trap stiffnesses: black, k = 0.005 pN/nm; red, k = 0.01 pN/nm; blue, k = 0.02 pN/nm; green, k = 0.03 pN/nm; orange, k = 0.05 pN/nm; magenta, k = 0.06 pN/nm; brown, k = 0.08 pN/nm; cyan, 0.1 pN/nm. (C) Forces and distances as a function of trap stiffness [trap stiffnesses are displayed as mean 95% confidence intervals (CIs)]. The average distances (left y axis, black spheres) and average forces (right y axis, green spheres) (the error bars represent the 95% CI of the mean) were fit to Eqs. 5 and 4 (black curve, R2 = 0.983; green curve, R2 = 0.991), resulting in the force-free run length x0 = 91 5 nm (SEM) and the stall force Fs depicted by the dashed line (mean SEM). (D) Velocity versus force. Points are means, and error bars span 95% CIs of the mean. The solid line is a linear fit that intercepts the ordinate at 540 nm/s (60 nm/s, SEM) and the abscissa at 1.9 pN (0.2 pN, SEM; N = 83 to 84 at each force; 335 events total). (E) Stall force histogram compiling stall forces (200 ms) at k = 0.1 pN/nm (N = 115; mean SD from Gaussian fit).

To test our hypothesis that the average detachment force increases hyperbolically as a function of the trap stiffness and that the force plateau corresponds to the maximal force that a dynein molecule can generate in isolation, we derived an analytical equation for the average detachment force. In this model, the average detachment force is defined as followsF=0FsFp(F)dF(1)where Fs is the stall force, and p(F) is the probability density of the detachment forces. This probability density is related to the motors force-dependent unbinding rate , its force-velocity relation v(F), and its compliance (43); here, the force-dependent stepping behavior of dyneins two MDs (4446) is effectively accounted for by v(F). However, because we record the trajectory of the trapped bead and not the trajectory of the moving motor (the trajectories of the bead and motor would only coincide if the motor were noncompliant), we transformed these quantities into parameters that describe the motor-powered bead movement. We, therefore, derived the probability density function as followsp(F)=(F)kvb(F)e0F(F)kvb(F)dF(2)where (F) is the force-dependent unbinding rate, k is the trap stiffness, and vb (F) the force-velocity relation of the bead [as (F), k, and vb(F) have the units 1/s, N/m, and m/s, p(F) has the unit 1/N]. To derive an analytical solution, we made two assumptions. First, we presumed that the force-velocity relation of the bead decreases linearly with the force, such thatvb=v0(1F/Fs)(3)where v0 is the force-free velocity. By extracting the force-velocity relation from the recorded traces, we indeed find that this equation describes the experimental force-velocity relation reasonably well (Fig. 3D). Our second assumption is a force-independent constant unbinding rate, (F) = . While this is a rather crude approximation, we find that the resulting analytical solution fits the data well (see below). As a further validation of our theoretical framework, we also investigated a scenario in which the unbinding rate depends exponentially on force and obtained very similar estimates for the stall force and the force-free run length (fig. S5). These results suggest that the stall force and the force-free run length are well constrained parameters, but that the accurate form of the force-dependent unbinding rate cannot be obtained using this approach (see Supplementary Materials for a detailed discussion). Therefore, the assumption of a constant unbinding rate is sufficient for our analysis, and together with the linear force-velocity relation for the bead, we can finally obtain an analytical equation describing the average detachment forceF=Fs1+Fskx0(4)where x0 = v0/ is the force-free run length. Fitting this equation to the experimental data, we obtain a value of 1.9 0.1 pN (SEM) for Fs and 87 6 nm (SE) for the force-free run length, x0. If we restrict the fit to the data acquired at trap stiffnesses up to only 0.05 pN/nm, we obtain a very similar result (1.9 0.1 pN and 85 7 nm), demonstrating that this method yields reliable results even if we obtain data in a relatively narrow range of trap stiffnesses. In addition, at the limit for an increased trap stiffness, Fs. Providing further support that this analytical equation describes the data well enough, we find that the resulting value for Fs coincides with both the stall force value resulting from the force-velocity analysis (1.9 0.2 pN, SE; Fig. 3D) and with the stall forces (200 ms) measured at the largest trap stiffness of 0.1 pN/nm (1.9 0.1 pN, SE; Fig. 3E). In contrast, stall forces measured below 0.04 pN/nm deviate significantly from the hyperbolic plateau (Fig. 2A), suggesting that the 200-ms force plateaus at low trap stiffnesses do not correspond to true stalling events.

Analogous to the hyperbolic relationship between the detachment force and the trap stiffness, the average detachment distance decays hyperbolically with increasing trap stiffness (Fig. 3, B and C). As is the case for the average detachment forces, the data can be fit to the theoretical average run length by the equationx=F/k=x01+kx0Fs(5)

At the limit for a decreasing trap stiffness, the average run length converges to the force-free run length, x x0.

Thus, our theoretical analysis shows that the average detachment force and the average run length depend hyperbolically on the trap stiffness. In addition, the force plateau corresponds to the stall force of the motor. This suggests that the detachment forces measured at commonly used trap stiffnesses (0.005 to 0.04 pN/nm) may be limited by dyneins processivity. We, therefore, hypothesized that this behavior should also be observed with highly processive motors under conditions where their processivity is significantly impaired. To test this possibility, we analyzed two other MT-associated molecular motorsthe highly processive yeast dynein and the plus enddirected kinesin-1as a function of the trap stiffness in buffers of varying ionic strength.

Yeast cytoplasmic dynein is a highly processive molecular motor that displays run lengths between 1 and 3 m, with some runs up to 20 m (8, 11, 47), and stall forces reported as high as 7 pN (14) (see Supplementary Materials for a detailed theoretical analysis that suggests that the previously measured 7-pN stall force likely resulted from an unintended electronic low-pass filtering during data acquisition). We analyzed the forces generated by the full-length yeast dynein construct, VY97-GFP [a GFP-tagged full-length motor (14)], at trap stiffnesses between 0.03 and 0.05 pN/nm (Fig. 4). Analysis of trap stiffnesses below 0.03 pN/nm was not feasible due to the limited reach of our optical trap (<300 nm), as escape of the dynein-bound beads from the trap occurred at lower trap stiffnesses. In a buffer system containing 30 mM Hepes and 100 mM KAc (potassium acetate stock solution pH adjusted to 7.0; see Materials and Methods), we did not detect any significant effect of trap stiffness on force (Fig. 4B), and we obtained an average stall force of 4.5 0.7 pN (SD; Fig. 4C). This is in agreement with our recent work (13) but is somewhat smaller than the previously published value (14) for the same construct (see Supplementary Materials for discussion). Therefore, even in 100 mM KAc, yeast dynein is sufficiently processive for its run length to have no impact on the observed forces. These trap stiffnessindependent results also serve as internal control to show that our trapping instrument was accurately calibrated and functioning as expected.

(A) Example trace showing stalling events and premature detachments of full-length yeast dynein (k = 0.05 pN/nm). Yeast dynein was specifically attached to beads coated with anti-GFP antibodies. (B) Forces versus trap stiffness. Values of stall forces (10 s, black) and of all events (gray) do not depend on the trap stiffness in the measured trap stiffness range (the error bars represent the 95% CIs of the means of the measured forces and trap stiffnesses). (C) Histogram of stall forces measured at various trap stiffnesses (N = 486; mean SD).

We next analyzed the plus enddirected motor kinesin-1 using the well-characterized construct, K560-GFP, a 560amino acidlong, tail-truncated construct of human conventional kinesin-1, with a C-terminal F64L/S65T variant of GFP (4850). Because this construct has been previously shown to display significant salt-dependent reductions in run length (50), we considered it to be an ideal candidate to test the relationship between processivity and force generation for a kinesin motor.

To first confirm the functionality of the K560 construct, we conducted an MT-gliding assay, which yielded a velocity of 670 10 nm/s (SEM) at 1 mM ATP in Pipes-Hepes buffer (PHB). This is almost twice the velocity previously reported by Thorn et al. for the same construct in buffer containing 12 mM Pipes, 2 mM MgCl2, and 1 mM EGTA (pH 6) (BRB12) (50), but is comparable to the value reported by Friedman et al. (51) (also in BRB12). For optical trapping experiments, the GFP moiety fused to K560 was used to specifically couple the motor to beads coated with anti-GFP antibodies. We then determined the single-molecule stall force of K560 in 80 mM Pipes, 2 mM MgCl2, and 1 mM EGTA (pH 6.8) (BRB80), a buffer system typically used to study kinesin. This yielded a value of 5.7 0.9 pN (SD; stall plateaus 200 ms; Fig. 5, A to C), which is in agreement with the previously reported stall forces between 5 and 7 pN (39, 5256). In addition, both the stall forces and the detachment forces were independent of the trap stiffness in BRB80 (Fig. 5B). The stall force was also found to be ~1.4 times greater than the detachment force, which is in good agreement with the ratio reported for kinesin-1 by Furuta et al. (6.8/5.2 pN ~1.3, BRB12) (55).

(A) Example trace of K560 in BRB80 trapping buffer showing a stalling event (red horizontal bar) and events without stalling (k = 0.03 pN/nm). (B) Stall forces (stalling for 200 ms; black crosses) and detachment forces (gray crosses) measured in BRB80 (the error bars are the 95% CIs of the mean forces and trap stiffnesses). (C) Stall force histogram measured in BRB80 (k = 0.03 to 0.12 pN/nm, N = 409, Fstall = 5.7 0.9 pN; mean SD). (D) Trap stiffness dependence of all events in Pipes-Hepes trapping buffer (blue, red), Pipes-Hepes trapping buffer +20 mM KAc (green, orange), + 30 mM KAc (dark cyan, purple), and + 40 mM KAc (light blue, magenta). Fitting Eq. 4 to the detachment forces (right y axis, circles, diamonds, hexagons, and crosses) and Eq. 5 to the distances (left y axis, triangles, squares, and plus signs) reveals the same hyperbolic dependence (solid curves) on the trap stiffness as human dynein (see Fig. 3C) (the error bars are the 95% CIs of the means of the measured forces, distances, and the trap stiffnesses). The dashed line depicts the average stall force in BRB80 [5.7 0.9 pN; see (C) for histogram].

When force measurements for K560 were obtained in PHB, we observed a slight dependence of the detachment force and distance on trap stiffness (Fig. 5D). However, the stall force Fs that resulted from the fit of Eq. 4 to the measured detachment forces (5.6 0.1 pN, SEM; Fig. 5D), agreed well with the stall forces (200 ms) measured for the highest trap stiffness in PHB (5.7 0.2 pN, mean SEM; 0.09 pN/nm) and with the stall force in BRB80 (Fig. 5C), indicating that the intrinsic stall force of K560 is not significantly reduced by the PHB system.

Last, to determine whether a systematic increase in buffer ionic strength results in a more pronounced effect of the trap stiffness, the PHB system was supplemented with increasing amounts of KAc. We observed a gradually heightened sensitivity to the trap stiffness with the addition of 20 to 40 mM KAc (Fig. 5D), whereas at high trap stiffnesses, the extrapolated force plateaus were similar to the stall force observed in BRB80 (Fig. 5, C and D). Only the buffer with the highest KAc concentration resulted in a significantly decreased value for Fs of 4.6 0.4 pN (SE from the fit with Eq. 4). Our data therefore suggest that kinesins processivity becomes more and more limiting with increasing salt concentration, leading to trap stiffnessdependent forces. This hypothesis is further substantiated by the finding that the y intercepts of the hyperbolic distance fits with Eq. 5 decay with increasing [KAc], suggesting a decreased run length (Fig. 5D). These observationsin combination with the data obtained for human dyneinthus indicate that the y intercept (k = 0.00 pN/nm) in a distance versus trap stiffness plot is a measure of a motors run length at zero load.

Human dynein is an essential motor protein that is responsible for a multitude of transport and force-generating processes within the cell (1, 3). However, despite its central role in cellular biology, the biochemical and biophysical properties of dynein have not been fully deciphered. This is evidenced by the range of run lengths (17, 20, 2225, 36) and forces (1521) (see Introduction) that have been reported for this protein.

In this study, our analysis of human dynein in the absence of cofactors suggests that the forces generated by this protein in optical trapping assays are limited by the motors processivity. This is indicated by the hyperbolic trap stiffness dependence that is observed for the measured forces and distances. The hyperbolic force plateau for all force-generating events at an infinite trap stiffness was found to coincide with human dyneins stall force (200 ms force plateau) at high trap stiffnesses (1.9 0.1 pN, SEM). High stall forces of up to 2.4 pN can be observed, even at low trap stiffness (0.01 pN/nm; see example traces in fig. S2B), but these are extremely rare. In addition, analogous to the detachment forces, our data imply that the run distance calculated for a trap stiffness of 0.00 pN/nm corresponds to dyneins run length in the absence of load (91 5 nm, SE).

Our hypothesis that trap stiffnessdependent forces act as an indicator for limiting motor processivity is further substantiated by trapping experiments with two other molecular motors. As expected, the highly processive yeast dynein motor (8, 11, 47) did not show trap stiffnessdependent behavior in our optical trap with a reach of ~300 nm, which allowed for trap stiffnesses equal to, or above, 0.03 pN/nm. Lower trap stiffnesses resulted in the motor frequently moving out of the trap due to its high processivity and yielded forces of ~4.5 pN (see Fig. 4), thereby preventing the determination of an accurate detachment force. The processivity of the kinesin-1 motor construct, K560, in contrast, could be sufficiently modulated by the addition of potassium acetate to the buffer. As predicted, increasing amounts of salt led to a heightened dependence on trap stiffness for both the detachment forces and run distances (see Fig. 5). Although the stall forces, as determined by the force plateau of the hyperbolic plot, did not depend significantly on the salt concentration, the distances extrapolated to a trap stiffness of 0.00 pN/nm decreased with the addition of salt, suggesting a decreased run length at zero load (see Fig. 5). Thus, our observation of trap stiffnessdependent behavior for kinesin in elevated salt buffers, but not in the traditional low-salt kinesin buffer system, BRB80, reinforces the validity of our observations with human dynein. We hypothesize that such trap stiffnessdependent forces can be expected for other molecular motors with limiting processivity. However, whether such a behavior is measurable depends on a combination of parameters, including the reach of the optical trap and, thus, the range of usable trap stiffnesses, the presence of vertical force components (57), as well as the motors processivity and its intrinsic stall force (see above, yeast dynein). In support of the broad relevance of this phenomenon, a similar trap stiffnessdependent behavior was reported for the molecular motor, kinesin-5 (Eg5), which generated a stall force of 1.5 pN under low-processivity conditions (58). Similar to human dynein in this study, Eg5 does not show clear stalling events before dissociation, indicative of low processivity (42).

The processivity of a motor is highly dependent on the experimental conditions under which it is measured, including the buffer system. Therefore, a range of observed forces would be expected for any given trap stiffness, depending on the buffer that is used. Moreover, different trap stiffnesses are expected to produce different observed forces in the same buffer system. Thus, the trap stiffness dependence revealed in this study could explain the range of in vitro forces, reported to be between 1 and 2 pN, for human dynein (15, 17, 18, 21). The higher forces reported by Walter et al. (16) and Toba et al. (19), however, are not consistent with our findings at the single-molecule level. Although the reason for this is unclear, possible explanations include the presence of motor aggregates, dynein cofactors, or posttranslational modifications on purified dynein in these publications. Alternatively, contamination with other molecular motors, such as the possible copurification of kinesin-1 with our StrepTrap-purified human dynein (see Supplementary Materials and fig. S6), is difficult to completely rule out, as trace amounts of protein that are undetectable by Western blot analysis could be sufficient to account for the observed single-molecule behavior.

A recent publication (18) has reported that forces measured in vivo for retrograde transport by dynein cluster in 2-pN increments, and the authors attribute this to the cooperation between two dynein dimers. However, our current findings would suggest that the unit force of a single dynein dimer is ~2 pN, in agreement with a recent study on recombinant full-length human dynein (12). Torisawa et al. (26) reported a force generation for human dynein of ~0.9 pN at a 0.01 pN/nm trap stiffness, a value that we also confirmed (Fig. 2A). However, this study further found that force generation does not change when the trap stiffness is increased to 0.028 pN/nm. Further studies will be required to resolve this apparent discrepancy.

In this study, we demonstrate that the peak force generation of mammalian cytoplasmic dynein measured in optical tweezers experiments is strongly influenced by its limited processivity and, therefore, by the trap stiffness. At relatively weak trap stiffness, true stalling is exceedingly rare, because the weakly processive motor simply detaches from the MT before reaching a sufficient displacement to achieve its maximal force. As a result, the measured peak force generation increases as the trap stiffness is increased. We develop a theoretical model to describe the force generation as a function of processivity and trap stiffness and use this model to precisely and definitively determine human dyneins stall force and its zero-load processivity. Our findings resolve a longstanding uncertainty regarding the stall force and processivity of mammalian dynein while simultaneously providing a straightforward, self-consistent methodology for analyzing the single-molecule function of this and other weakly processive molecular motors. This framework provides a much more accurate and precise estimate than the analysis of rare stalling events at low trap stiffnesses and avoids the variability associated with arbitrarily chosen definitions of stalling (52).

This work provides key information on the intrinsic function of human dynein that is necessary to completely understand the effects of cofactors including dynactin and BicD. Using the results and experimental framework developed here will enable future experiments to unambiguously characterize the effects of these cofactors on both processivity and force generation independently. Moreover, although we have shown here that stall force and processivity are indeed distinct functional properties, the challenges encountered with measuring the stall force of a weakly processive motor illustrate the biological importance of processivity to generating force. For example, enhanced processivity (via cofactor association or multimotor ensembles) may be required to generate substantial forces when dynein associates with cargoes via highly compliant linkages such as cell membranes, regardless of the intrinsic maximal force generation capability. In other words, the strength of the motor can only be realized to the extent it can remain attached to its MT track. Last, the characterization of intrinsic dynein function will aid our understanding of biological scenarios in which dynein associates with organelles in the absence of dynactin (27, 28).

Chemicals were obtained from Thermo Fisher Scientific or Sigma-Aldrich unless otherwise stated. All buffers were prepared using UltraPure distilled water from Life Technologies.

HEK293 cells capable of doxycycline-induced mfGFP-IC74 (multifunctional GFP-tagged human cytoplasmic dynein intermediate chain) expression were obtained from T. Murayama [Department of Pharmacology, Juntendo University School of Medicine, Tokyo, Japan (37, 38)]. Cytoplasmic human dynein was purified from HEK cells essentially as described in (37, 38), utilizing the streptavidin tag contained in the mfGFP-IC in a one-step StrepTrap column purification (see Supplementary Materials). The purity and composition of the human dynein complex were analyzed using standard SDS-PAGE, native light blue PAGE, and Western blot analyses (see Supporting Material).

Kinesin-1 [K560-GFP (4850)] and full-length yeast dynein (VY97) were purified as described previously (8, 59). Active motors were obtained in MT-binding release assays (protocol see below).

To further purify the motor proteins used in this study (yeast dynein VY97, kinesin-1 K560, and mfGFP-IC74 human cytoplasmic dynein), the motors were subjected to an MTBR, in which the motors were bound to MTs in a strong-binding state [mimicked by the ATP analog AMPPNP (adenylyl-imidodiphosphate)], and functional motor proteins were subsequently released in the presence of salt and ATP (see Supporting Material).

Coverslips for optical trapping assays were cleaned and coated as previously described (59, 60) using a series of cleaning steps (sonication in mucasol and plasma cleaning) followed by coating with APTES or AE-APTES [(3-aminopropyl)triethoxysilane and N-(2-aminoethyl)-(3-aminopropyl)triethoxysilane, respectively]. Fluorescently labeled MTs were covalently attached to the coverslips via glutaraldehyde.

Proteins were attached either nonspecifically to polystyrene beads (0.9 m; Bangs Laboratories Inc.) or specifically via anti-GFP antibodies (fig. S4). Antibody-bound beads were prepared as described previously (59). Motors were diluted in pure buffer for nonspecific binding and in buffer plus -casein (1 mg/ml) when using antibody-coated beads. -Casein used in these buffers was purified as described previously (59). Motor proteins at a given dilution were incubated with the beads for 10 min on ice before adding the final trapping solution. All final trapping solutions contained an ATP regeneration system [2 mM phosphoenol pyruvate, pyruvate kinase (0.1 mg/ml)], an oxygen scavenger system [22.5 mM glucose, pyranose oxidase (3 U/ml), 90 U/ml catalase (90 U/ml)], and 10 M Taxol. Depending on the assay, trapping buffers used were as follows: PHB [50 mM Pipes, 50 mM Hepes, 2 mM MgCl2, 1 mM EGTA (pH 7.0), 10 mM dithiothreitol (DTT), 1 mM guanosine 5-triphosphate (GTP) or varying amounts of ATP, -casein (1 mg/ml)]; Pipes-Hepes-KAc trapping buffer [50 mM Pipes, 50 mM Hepes, 20 or 30 or 40 mM KAc, 2 mM MgCl2, 1 mM EGTA (pH 7.0), 10 mM DTT, 1 mM ATP, -casein (1 mg/ml)]; Hepes-KAc trapping buffer [30 mM Hepes, 100 mM KAc, 2 mM Mg acetate, 1 mM EGTA (pH 7.2), 10 mM DTT, 1 mM ATP, -casein (1 mg/ml)]; and BRB80 trapping buffer [80 mM Pipes, 2 mM MgCl2, 1 mM EGTA (pH 6.8), 10 mM DTT, 1 mM GTP or 1 mM ATP, -casein (1 mg/ml)]. Trapping assays were performed at 25C.

The optical trapping microscope was calibrated as previously discussed (59). Position and trap stiffness calibrations were performed for each bead tested. The trap stiffness was calculated using a hybrid method of calibration (61), combining the power spectrum and viscous drag analyses, which permits trap stiffness calibration without the need to know the drag coefficient. The microscope body is a Nikon Eclipse Ti-U. A single, custom-designed polychroic mirror guides various lasers into the objective and has high transmission bands for emission of several fluorescent dyes including Cy3/TMR (tetramethylrhodamine) to monitor TRITC (tetramethyl rhodamine isothiocyanate)labeled MTs using a 532-nm laser. To visualize trapped beads, a 470-nm light-emitting diode lamp was used for bright-field illumination using a charge-coupled device camera. Trapping was performed with a 10-W, 1064-nm laser (IPG Photonics) chosen for its output power stability. Detection of bead position with nanometer precision is accomplished by back focal plane detection using an 830-nm laser. Data were acquired using a custom-written software in LabVIEW (National Instruments) and MATLAB (Mathworks) and analyzed using customized MATLAB software.

For the dilution curves (processivity analysis), each bead was tested for at least 4 min before judging it as moving or nonmoving bead. Error bars were calculated assuming a binomial distribution: error bar = sqrt ((1 f) f/N), with f being the fraction of moving beads and N the number of beads tested. Two models were fit to the data. The first equation is based on the assumption that one or more motors are required for the observed force generation (processive model, see Supporting Material for the derivation)F=1exp(c)(6)where F is the moving bead fraction, c is the relative motor concentration, and is a fitting parameter that describes the dependence on the fraction of active motors. The second equation (nonprocessive model) approximates those dilution curves better that result from force generation events driven by two or more motors (see Supporting Material for the derivation)F=1exp(c)cexp(c)(7)

The coefficients of determination (R2) at a significance level = 0.05 and AIC test were used to judge which model approximates the data better. All figures were prepared using Prism software (GraphPad), and all fitting procedures, including the AIC test, were performed therein.

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

Acknowledgments: We thank T. Murayama for generously providing the HEK293 cell line capable of doxycycline-induced mfGFP-IC74 expression. Funding: We were supported by the National Institutes of Health (NIH) grant R01GM098469. M.P.N. received support from the NIH-funded Medical Scientist Training and Molecular Biophysics Training programs at the Albert Einstein College of Medicine (NIH grants T32GM007288 and T32GM008572, respectively). L.R. and S.B. received support from the NIH grant R01GM098469. S.B. received support from the German Research Foundation grant BR 4257/1-1. Author contributions: S.B., F.B., M.P.N., and A.G. designed the research. S.B., F.B., L.R., and A.G. performed the research, S.B. and L.R. produced and purified protein. S.B., F.B., M.P.N., and A.G analyzed the data and wrote the manuscript. Competing interests: All authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

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Force production of human cytoplasmic dynein is limited by its processivity - Science Advances

Spelling the Dream: How an Austin kid is part of an American dynasty – KXAN.com

AUSTIN (KXAN) Its you against the dictionary.

Thats how producer Chris Weller describes the national spelling bee scene, and in his upcoming Netflix documentary Spelling the Dream, he features an Austin native and if a dictionary could be afraid of someone, it would be terrified of him.

Weller describes Austins Nihar Janga, now a freshman in high school, as someone who dominates with humility. Janga shared the 2016 Scripps National Spelling Bee title with Jairam Hathwar as a fifth-grader, and is part of one of the most impressive dynasties in American competition Indian-American students winning the nations top spelling competition.

The past 12 champs have been Indian-American, and since 1999, 19 of the past 23 winners have ethnic roots from the Asian nation. Weller, a spelling bee fan himself, noticed the trend in 2013 and wondered why it was happening.

He met Sam Rega (who previously made the documentary Breaking the Bee), a co-writer and director of the documentary, in 2015. He pitched the idea of exploring the trend to Rega, and off they went.

You could say curiosity drew me in, Weller said, but the families and the kids are what kept me committed.

Weller and Rega followed four spelling bee champions for the film. One of them was Janga. What struck Weller most about Janga was how gracious and respectful he was to his competitors.

He knows his stuff, but he isnt boastful, Weller said. He celebrated equally if he spelled his word correctly and if his opponent, Jairam Hathwar, got his word right.

Janga said he started doing spelling bees in kindergarten because he enjoyed putting letters together to make words, and he won his first national championship in the third grade. He won both the spelling and vocabulary bees at the North South Foundation National Finals, and after he missed the Scripps National Spelling Bee by one spot the year after, in 2015, he said he dedicated himself to making the field.

I started my intensive training daily after that, Janga said. My mom coached me by preparing word lists and quizzing me on not only the correct order of the letters, but also their etymology and definitions to ensure my understanding of the words usage in the real world, rather than just its spelling.

Jangas training paid off, and he said his success doesnt just reflect his hard work. He said the prize also belongs to his family, and they helped him every step of the way.

I really enjoyed preparing for the bees because it was a family effort and brought us together every day, he said. He also mentioned that after competitions were done, he and his family planned to take a long trip to get away from it all.

And when hes not in a spelling competition, he goes out and wins geography bees, too. Hes the first person in history to win both the Scripps National Spelling Bee and the National Geographic Geography Bee. He picked up the geography bee championship last year, and said hed dream about winning both competitions.

And typically, after he realized those dreams, it was the best feeling hed ever felt.

Both times, it felt like I was dancing on the clouds above the summit of Mount Everest, he said.

Weller said he wanted to make this documentary to celebrate the success of kids like Janga. He was well-aware of all the stereotypes that go along with the spelling bee and the kids in them, and he wanted to make sure the film put success at the forefront.

Race and culture are always sensitive topics in filmmaking, even more so if the story involves kids, he said. So from the very beginning we promised ourselves that this would be a celebratory film. It would honor the culture and tell the story from the points of view of the people living the story.

Weller made sure to mention that these kids are just like any other American kid, that they spend their free time doing more than just reading a dictionary and studying. Some are musicians, some love to play video games and some are into other sports. Janga himself said he took up rowing in the past year, and now hes active in the Austin Rowing Club and loves it.

Im trying my best to become really good at it, Janga said.

Weller said the films focus is on the kids, whom he calls the stars of the show, but he and his team also realize that the stereotypes are out there and addressed them.

We included commentary from social scientists on issues like social media bullying and representation in the media, Weller said.

Also included in the film are perspectives from nationally-recognized Indian-Americans in television, comedy and sports. CNNs Dr. Sanjay Gupta and Fareed Zakaria, comedian Hari Kondabolu, ESPNs Kevin Negandhi and 1999 Scripps Spelling Bee champion Nupur Lala help give context to what the winning streak means to the community theyre all part of.

Janga has aspirations of becoming a neurosurgeon, and specifically, he wants to research Alzheimers disease and brain tumors to figure out how to reverse their effects.

I want to take on the social responsibility to fight against mental health issues, primarily in teenagers, as that is a huge problem that needs to be mitigated, he said.

He plans to be involved in anthropology and sociology organizations so he can better understand human behavior and the impact of surrounding society.

Janga has lofty goals, theres no doubt about that. If anybody can accomplish goals like that, Weller says its Janga.

His passion for academics and committing himself to mastering on competition after another both inspire me, and fills me with incredible guilt for all those years I spent playing video games. Weller said.

The film hits Netflix on May 23, the day before the 2020 Scripps National Spelling Bee was supposed to begin. The COVID-19 pandemic has led organizers to suspend it this year. You can read more about Netflixs upcoming releases on its website.

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Spelling the Dream: How an Austin kid is part of an American dynasty - KXAN.com

Unpredictable Human Behavior Just One Of Many Complicated Factors To Consider When Trying To Predict Deaths – Kaiser Health News

The model that the White House has been relying on for number of cases and deaths was just updated to slightly more optimistic totals for the first wave of the outbreak. But other models contradict that outlook. Why is modeling so hard? Scientists have to take a number of unpredictable and unknowable factors into account. Still, they say, "it's much better than shooting from the hip." Meanwhile, the number of deaths in the U.S. surpasses 10,000.

The Washington Post:Americas Most Influential Coronavirus Model Just Revised Its Estimates Downward. But Not Every Model Agrees.A leading forecasting model used by the White House to chart the coronavirus pandemic predicted Monday that the United States may need fewer hospital beds, ventilators and other equipment than previously projected and that some states may reach their peak of covid-19 deaths sooner than expected. ... Experts and state leaders, however, continued to steel themselves for grim weeks ahead, noting that the revised model created by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington conflicts with many other models showing higher equipment shortages, deaths and projected peaks. (Wan and Johnson, 4/6)

The Associated Press:Modeling Coronavirus: 'Uncertainty Is The Only Certainty'So how does modeling work? Take everything we know about how the coronavirus is spreading, when its deadly and when its not, when symptoms show and when they dont. Then factor in everything we know about how people are reacting, social distancing, stay-at-home orders and other squishy human factors. Now add everything we know about testing, treating the disease and equipment shortages. Finally, mix in large dollops of uncertainty at every level. Squeeze all those thousands of data points into incredibly complex mathematical equations and voila, heres whats going to happen next with the pandemic. Except, remember, theres a huge margin of error: For the prediction of U.S. deaths, the range is larger than the population of Wilmington, Delaware. (Borenstein and Johnson, 4/7)

ABC News:CDC Director Downplays Coronavirus Models, Says Death Toll Will Be 'Much Lower' Than ProjectedOne of the nations top public health officials suggested Monday that because Americans are taking social distancing recommendations to heart, the death toll from the novel coronavirus will be much, much, much lower than models have projected. If we just social distance, we will see this virus and this outbreak basically decline, decline, decline. And I think that's what you're seeing, said Robert Redfield, the Director of the Centers for Disease Control. (Meek and Bruggeman, 4/6)

The Wall Street Journal:U.S. Death Toll From Coronavirus Tops 10,000The U.S. coronavirus death toll surpassed 10,000 at the start of a week that officials predicted would be Americas most difficult yet during the global pandemic, while the crisis in Britain deepened as the prime minister was moved to intensive care. Confirmed infections in the U.S. were more than double that of any other nation, at nearly 357,000, with the death toll at 10,783, according to data Monday from Johns Hopkins University. (Calfas, Ping and Kostov, 4/6)

NBC News:Behind The Global Efforts To Make A Privacy-First Coronavirus Tracking AppIn a Google Doc that now stretches beyond 20 pages, software engineers and health experts are working out what they hope can be a way for the world to soon return to something resembling normal life. "What's the minimum duration of contact that we should consider important?" an engineer asked. It's one of many crucial questions from engineers who believe smartphone technology could be the key to creating a way to anonymously track the spread of the coronavirus and by doing so help save lives and get people back to their jobs and social lives. (Ingram and Ward, 4/7)

The New York Times:Does My County Have An Epidemic? Estimates Show Hidden TransmissionAs the coronavirus spreads silently through American cities and towns, people are struggling with questions about the benefits of social-distancing guidelines especially in places that still have few reported cases. Is the epidemic here yet? Is staying home and limiting contact with others really worth the trouble? A new study by disease modelers at the University of Texas at Austin gives an answer: Even counties with just a single reported case have more than 50 percent likelihood that a sustained, undetected outbreak an epidemic is already taking place. (Glanz, Bloch and Singhvi, 4/3)

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Unpredictable Human Behavior Just One Of Many Complicated Factors To Consider When Trying To Predict Deaths - Kaiser Health News

Coronavirus case counts are going to go up but that doesnt mean social distancing is a bust – PBS NewsHour

The last few weeks have brought previously unimaginable changes to the lives of people throughout the United States. Americans everywhere are waking up to a new reality in which they cant go to work or school outside the home and they have to stay six feet away from others. More than 80% of Americans are under such stay-at-home orders.

People are also seeing charts in the news showing rapidly increasing case counts. This is likely to continue to occur. The United States surpassed Italy and China to have the most confirmed cases of any country.

Americans might begin to wonder if these social distancing measures are working if the case numbers keep climbing. The problem is that the number of reported cases is not the same as the number of people who are infected. It takes time for people to develop symptoms, seek treatment and get tested and for the results to come back. So the effects of social distancing might not be obvious from the numbers for a while. As an epidemiologist at the University of Michigan, I can assure you that staying at home is one of the most effective ways to slow the spread of COVID-19.

A key reason for the delay between people severely restricting their movements and a drop in the number of new cases is that COVID-19 can have a long incubation period, the time between getting infected and becoming sick. The average incubation period is around 5 days, but it can be as long as 14 days or more. This means that a person infected before a stay-at-home order might not get diagnosed until days later.

Testing is another factor in the delay between the start of social distancing and seeing the results. Many Americans dont even know if theyve been infected with the new coronavirus SARS-CoV-2. Though the United States is finally ramping up production of test kits in federal, state and private laboratories, there are stringent criteria on who can get tested. Testing is mostly limited to people with symptoms, frontline health care workers and first responders, and older people. However, scientists have found asymptomatic and presymptomatic transmission of COVID-19.

Asymptomatic spread has probably contributed to the explosive growth of COVID-19 in the United States. Overall, as restrictions on testing ease, case counts are going to rise because more people, including those with mild or no illness, will be able to get tested.

Finally, its important to note that current COVID-19 tests take 24 to 72 hours to generate a result. Even in China, where testing is widely available, the average time from the onset of symptoms to a diagnosis of COVID-19 is five days. It takes one to three days to get test results because the tests discover whether the viruss genetic material is present inside a patients body. This requires replicating the viruss genome using specialized laboratory equipment. Scientists are developing tests that look for telltale signs of the patients immune system response to virus, and these blood tests should provide quicker results.

Unfortunately, people will, for the next few weeks, see increasing case counts even as they might be rigorously complying with government directives to avoid contact with other people. The lag time in reporting cases could make people feel that the actions theyre taking staying at home and limiting in-person social interactions arent working.

When people think that what they do works, theyre more likely to do it, a concept known as self-efficacy. It turns out to be an important predictor of human behavior. For example, people who expect to be able to quit smoking are more likely to quit. As self-efficacy diminishes, people could become less motivated and relax their adherence to stay-at-home orders.

Experience from previous pandemics in the 21st century shows that peoples behaviors and attitudes change over the course of the outbreak. As the 2009 H1N1 pandemic progressed, people became less likely to want a vaccine and to perceive themselves at risk. Researchers who conducted monthly interviews with Hong Kong residents over the course of the SARS outbreak found that peoples perceptions of the effectiveness of staying at home and avoiding going to work decreased as the outbreak wore on.

If Americans see increases in case counts and believe that their own actions are ineffective, they might be less inclined to follow through on social distancing. This could lead to increased contact among people, which could make it more difficult to bring the pandemic under control. Hopefully widespread testing and faster test results will lead to a more accurate understanding of who is and is not infected with the disease, not unlike what South Korea has accomplished so far. In the meantime, Americans should not take an increase in COVID-19 cases to mean that their sacrifices arent worth sustaining.

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

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Coronavirus case counts are going to go up but that doesnt mean social distancing is a bust - PBS NewsHour

The Pandemic Is Giving Animals a Temporary New World – Slate

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There are now so many lockdowns, shelter-in-place orders, and quarantines in effect around the world that half of global humanity is essentially indoors (minus walks). Its all very weird for us, but the natural world is entering uncharted territory too. No, swans havent returned to the canals of Venice (they were always there), and no, elephants arent taking advantage of temporary human absence to get drunk and pass out on vacated farmland. Those, among other online reports of chaos in the animal kingdom, have largely turned out to be fabricated or oversimplified. Still, a significant shift is about to take place. If we really do live in the Anthropocenean epoch of natural history defined by the effects of human beings on the planetthen a drastic curtailing of our activity (reduced air and car travel; our disappearance from streets, parks, and beaches; changes in hunting, fishing, and wildlife management practices) will have effects that are felt throughout nature. What are animals, and other wild things, going to get up to in our absence?

Its easy to imagine Jumanji-esque chaos ensuinga mass exodus of scavengers out of beaches, parks, and shadows and into city streets. Fire escapes overrun by bands of raccoons, rats pouring out of subway stations. Desperate gulls and pigeons circling the skies, trying to steal food from garbage trucks and grocery shoppers. Maybe even hawks dragging small dogs to tree limbs and consuming them, leash and all, as shocked parkgoers gaze up in horror. In mythology and literature, visions of social collapse, crisis, and apocalypse have always featured breakdowns in the natural order. Locust plagues, swarms of rats, and ominous bird signs regularly accompany narratives of human calamity. Its unlikely that all of these things will happen, but still, it seems safe to assume that well see animals behaving strangely in the coming weeks.

The first thing I started to think about is people who live adjacent to restaurants or above restaurants, where there are rats that have a daily habit of eating the trash thats putout. Matthew Combs

Its hard to predict what the effects are going to be, explained Kaitlyn Parkins, senior conservation biologist for New York City Audubon, because we dont know how long this will last. Parkins is in a unique position to imagine what the changes might actually look like; her work is heavily focused on the areas where people and animals exist in close proximity. Throughout the year, she facilitates wildlife surveys and other wildlife conservation and research projects around New York City.

Some days, Parkins works at the Javits Centers Green Roof, a 6.75-acre human-built habitat that rests above the convention hall that has recently filled up with men and women in uniform as the Army transforms it into a makeshift hospital. The Green Roof is the second-largest facility of its kind in the country. Thirty bird species and five bat species use it as habitat in some form or another. Herring gulls nest there in the summer, and the fledglings spend their first months flying to the river to eat everything from fish stunned by boat propellers to sandwich scraps left behind in Hudson River Park. A lot of the human-animal interaction in the city revolves around food, Parkins said.

The animals that are most likely to undergo rapid, dramatic shifts in behavior when human beings go inside are also some of the most visible. In New York City, squirrels, pigeons, rats, raccoons, and a few gull species will have to adapt right away. In places where bears and coyotes are more common, those populations will also have to quickly recalibrate. Any animal that depends on human scraps for a significant portion of its diet will have to either find alternative local food sources or fan out to new places. Its interesting, Parkins noted, because a lot of the wildlife that tends to thrive in cities are generalists. They take advantage of any resource they can get, and have a lot of behavioral plasticity. That sets them up to be able to adapt to short-term changes in the environment quickly and easily.

In New York, that could mean moving to new places. The mass closure of restaurants and dramatic declines in subway ridership could force rat populations that inhabit those places to fan out and look elsewhere for meals. If parks close or empty out significantly, raccoons and squirrels will no longer be able to depend on the steady supply of food offered by public garbage cans. If public beaches abbreviate their seasons, the herring gulls and laughing gulls that spend the warmer months chasing down potato chips and other food left unattended by beachgoers will have to seek out a new food supply.

Photo illustration by Slate. Photos by Getty Images Plus.

Its pretty clear where all those animals will need to go if they want to keep eating scraps. Although human garbage will disappear from some public spaces, people are still eating the same amount they were before. I thought about all of the cooking Id been doing recently, and the pictures of home-cooked meals that my friends and co-workers had been sending me. If theres an influx of trash in residential neighborhoods, Parkins said, thats a smorgasbord for rats.

Parkins fianc, Matthew Combs, is a research scientist at Columbia. He got his Ph.D. at Fordham University studying New York Citys rat population. The two of them had been discussing the possible outcomes for animals, particularly in urban areas. Rats have among the closest relationships with humans, Combs explained. The first thing I started to think about is people who live adjacent to restaurants or above restaurants, where there are rats that have a daily habit of eating the trash thats put out on the sidewalk every night. Rats like to stick to a routine, if its working outexploring involves risks. But if those food sources dry up, theyll likely start looking elsewhere. The properties that are adjacent will maybe experience rats coming to look for food.

The pandemics effect on the natural world could go further than rats coming out of the subways, though. Natural systems, when you look at them closely, are deeply interconnected. A year that produces a high yield of pine cones, for example, even in just a single area, can have ripples that lead to population spikes, changes in migration patterns, and habitat realignment for multiple species across vast areas for more than one season. We can make predictions based on what we know about wildlife, Parkins said, but the urban system is more complex than we realize. Animals have learned to adapt to and make use of human behavior in countless ways, from the birds that follow fishing boats out to sea, to the peregrine falcons and red-tailed hawks that nest on bridges and skyscrapers, to the eels that writhe through our sewers.

One of the most significant human-made impositions on wildlife, our highways and streets that hum constantly with traffic, is about to empty out as travel declines dramatically. Roadways, cars, and traffic have the ability to contain wildlife in particular places, Parkins said. An animal might have to be really brave to cross to the next habitat. As those barriers come down, animals will suddenly find their movements less inhibited, as footage from Wales showing Kashmiri goats romping through the empty streets of a small town called Llandudno demonstrated. Ranges will expand, and some animals that were suffering from constricted habitat might find themselves in a more secure situation. It sounds delightful, but its a dramatic change that will come on quickly. Imagine all the animals in a zoo waking up one day to find that the walls and bars had disappeared overnight. Theres a kind of pastoral beauty to imagining herds of deer roaming freely across the highway system to graze in backyards and public parks, sure. Just remember that roaming deer mean coyotes and bears could also be less inhibited.

Indeed, every animal exists in the wider food web. In urban settings, rats, squirrels, and pigeons are prey for larger animals like feral cats and raptors, which might have to adjust their behavior as their food sources fan out. A change in behavior even in a single species could set off a chain reaction that affects animal behavior in countless unpredictable ways. There are a dizzying number of moving parts, all of them connected in a giant chain of causes and effects that are nearly impossible to predict. A large population of white-tailed deer, for example, can devastate a forests understory by grazing on low-lying plants and saplings. Even a modest population bump from a reduction in automobile collisions could set back woodlands for years to come.

It would be idiotic to indulge in the fantasy that human beings going inside for a few months will somehow allow the natural systems weve damaged over centuries toheal.

There are other species that might stand to benefit, at least in the short term. If public beaches dont fill up this summer, shorebirds that nest there will likely have a better breeding season than they would otherwise, with thousands of miles of new habitat suddenly available to them when they arrive. Birds and mammals that are highly sensitive to noises or susceptible to being killed by car traffic will probably fare slightly better too. There are hints that things are already starting to change. A credible-seeming news report from New Orleans described an emboldened rat population leaving the shadows to scavenge out on Bourbon Street. A video shot in Thailand showed dozens of monkeys in a near-empty tourist square brawling over a single container of yogurt. The oldest national park in AfricaVirunga, in the Democratic Republic of the Congoclosed its doors to tourists out of fear that the virus, like other similar viruses, could make a leap to the dwindling populations of great apes that survive there. Its hard not to feel like these are tremors of things to comea realignment in the way animals interact with the world that will match, in some ways, the extraordinary intensity and insanity of whats unfolding in the human world every day.

Still, it would be idiotic to indulge in the fantasy that human beings going inside for a few months will somehow allow the natural systems weve damaged over centuries to heal. The wear and tear of human behavior on wildlife has been long-term and extensive. Many of the ways humans affect wildlife are more permanent than us scaring them into staying put or accidentally feeding them with our trash. When I first met Parkins two years ago, she was leading a training for volunteers with Project Safe Flight, NYC Audubons initiative to research and document bird collisions with windows in the city. Birds, she explained at the time, perceive the world differently than human beings do. Reality comes at them all at once, a constantly moving set of spatial reference points. Building glass, which can confuse them by reflecting images of clear sky or nearby trees, kills somewhere between 90,000 and 230,000 birds in New York City each year. Theres no reason to suspect that those collisions will decline just because humans are inside. A day after we spoke, in fact, Parkins sent along a photo of a dead golden-crowned kinglet that shed watched collide with a window near Central Park.

A picture of a dead bird would have made me upset a month ago, but now a window strike seemed like a reassuring sign. All around the world, animals are still participating in their normal habits, which right now means migrating up toward breeding habitat. Seals that haul out in Staten Island and the Bronx in the winter are swimming north with dolphins, porpoises, and whales, as they do each year. Striped bass and Atlantic sturgeon are surging up the Hudson River to spawn. New York Citys terrestrial spaces are about to be inundated with what might be the most remarkable natural phenomenon in this part of the world: the rapid arrival and departure of millions of birds in a two-month period.

Our disappearance from the world is happening at a time of massive flux for creatures. This might have its own set of influences. Our abrupt retreat inside was preemptive, a reaction to information from scientists and journalists about an event we knew was coming. Animals dont have newspapers or epidemiologists to tell them how to prepare; theyre still just out there, migrating, hunting, trying to survive. Its one of the reasons their reactions are so difficult to anticipate. Just like us, wildlife is at the beginning of this crisisthey just dont know it yet. While we get guidance from political figures and doctors, animals will respond with reflex and instinct. And even as they adapt to us being gone, they cant realize that, eventually, well come back. Which means that despite modest benefits that might reach specific populations due to our absence, theres no reason to believe that the factors that initially put stress on those animals wont come roaring back whenever people head outside again.

My best guess is that there will be dramatic developments in the coming weeksreports of animals spreading out into new areas to explore habitat options or look for food. Some will become aggressive or behave in other strange ways. Human beings and bears will come into closer contact than either species is accustomed to. It might be a systemic unraveling, or a modest shift, or a series of isolated, temporary incidents. Combs and Parkins were cautious with their predictionscareful to mention that they can envision scenarios where little changes. I cant help but imagine the extremes: the Boschian nightmares where primates stop traffic to tip over trucks full of food and gulls invade homes and grocery stores, Hitchcock-style. Natural systems are incredibly intricate, and its almost impossible to predict exactly how theyll respond.

Recently, with all this in mind, I picked up my binoculars and went out for a long walk through Manhattan and along the Hudson River. House sparrows were beginning to form nests inside the metal fixtures that hold up traffic lights. I saw a red-throated loon bobbing in the wind shadow of a hulking dinner boat at Pier 60a locally rare bird that made me wonder if the reduction in boat traffic had already made Manhattans shoreline a more inviting place for solitary animals. None of this was scientific, of course. I was projecting the human crisis onto the natural details around me. Still, I couldnt help but feel that something was going onthat I was witnessing the early moments of what might turn out to be sea change. There was a cache of emptied acorn lids on the sidewalk, dug up and devoured, and I imagined the squirrels of Hudson River Park panicking at the sight of empty garbage cans and gnawing through their savings all at once. As I was getting ready to leave, a red-tailed hawk caught me off guard. It was staring from the top of a chain-link fence a few feet away, ignoring the rain the wind. It was well within the 6-foot bubble that Id been keeping between myself and other peoplecloser, in fact, than any hawk had ever let me get. We looked at each other for a long minute before a car horn from the Westside Highway scared it out over the river.

Link:
The Pandemic Is Giving Animals a Temporary New World - Slate

Renewable energy must be the future, if we are to have one – Los Angeles Times

The world still relies far too much on burning fossil fuels for energy, but an annual accounting of new energy sources carries some heartening news: Nearly 75% of new electricity generation capacity last year involved renewable energy an all-time record.

Yes, the world still relies too much on burning fossil fuel to create energy. But the 2019 annual report from the International Renewable Energy Agency shows that the world continues to move in the right direction, at least in some areas, as it has for the past decade.

Carbon Brief, a British-based nonprofit covering climate science, notes that too many countries are still building too many coal-fired power plants, particularly in Asia, Africa and the Middle East.

Over the last 20 years, the world driven by China and India has doubled its coal-fired capacity to about 2,045 gigawatts, Carbon Brief reports, adding that another 200 gigawatts in coal-fired capacity are under construction, with 300 gigawatts more on planning boards. That growth contrasts with significant net reductions in coal-fired capacity through the retirement of plants in the U.S. and Europe, and a slowdown of new construction.

Notably, much of that coal power is being replaced by natural-gas-fueled plants, which produce far less greenhouse gas emissions than coal plants but nonetheless contribute to global warming.

So the faster the world can minimize reliance on burning fossil fuels, the better chance we have at limiting the rise in global temperatures to 1.5 degrees Celsius over pre-industrial levels, the limit scientists (yes there are such people walking among us) say we need to observe if we are to avoid the worst effects of our profligate carbon emissions.

According to Carbon Brief, observing that 1.5-degree Celsius limit will require us to reduce global coal use by 80% this decade.

The current coronavirus pandemic has, at least temporarily, made a significant impact on greenhouse gas emissions. But that reflects a stalled economy rather than smart energy consumption choices. The pandemic is a naturally occurring threat to humans, as were SARS and MERS before it. Global warming, by contrast, is being driven by human behavior; it is a self-inflicted crisis.

We can best address the climate crisis by changing practices, by converting our global economy from fossil fuels to renewable sources, by using the force of our collective will to change our collective behavior and reduce the damage our actions inflict on the environment, which we rely on for our very survival.

The stats that show we are moving in the right direction, albeit it too slowly, are a positive sign during these trying days.

But they are also a further spur to action. We can see where decisions, policies and actions lead to positive effects, but also where continued self-destructive actions beginning with burning coal imperil us all.

And that threat lies far beyond the reach of a vaccine.

The rest is here:
Renewable energy must be the future, if we are to have one - Los Angeles Times