espresso machines

An espresso machine brews coffee by forcing pressurized water near boiling point through a "puck" of ground coffee and a filter in order to produce a thick, concentrated coffee called espresso. The first machine for making espresso was built and patented in 1884 by Angelo Moriondo of Turin, Italy. An improved design was patented on April 28, 1903, by Luigi Bezzera. The founder of the La Pavoni company bought the patent and from 1905 produced espresso machines commercially on a small scale in Milan. Multiple machine designs have been created to produce espresso. Several machines share some common elements, such as a grouphead and a portafilter. An espresso machine may also have a steam wand which is used to steam and froth liquids (such as milk) for coffee drinks such as cappuccino and caffe latte. Espresso machines may be steam-driven, piston-driven, pump-driven, or air-pump-driven. Machines may also be manual or automatic.

Is the Universe Expanding at an Accelerated Rate?

A new study challenges the cosmological model and suggests that the universe is not expanding at an accelerated rate.

The standard model of cosmology assumes that the universe is isotropic with no preferred direction and no preferred frame of reference; that is, we are not special and our position in the universe is not from a privileged vantage point. Within this framework, observational data led us to the conclusion that 70% of the universe is expanding at an accelerated rate, and this accelerating force is due to an unknown form of energy known as ‘dark energy’. This so-called ‘dark energy’ is now thought to be due to quantum fluctuations of the vacuum energy.

However, a new study by a team of European scientists explored these ideas further. They wanted to see what would happen when they measure the deceleration parameter – the measurement of cosmic acceleration – from our own ‘special’ frame of reference.

The expansion of the universe is measured in terms of the Hubble constant, which is currently measured by two different methods. One method looks at the early universe through the observation of the Cosmic Microwave Background (CMB) and the other method looks at the local universe through the light emitted by galaxies, Cepheid variables and/or Type 1a supernovae. It was the latter method that led to the conclusion that the universe was expanding at an accelerating rate, resulting in astrophysicists Adam Reiss, Brian Paul Schmidt and Saul Perlmutter receiving the 2011 Nobel Prize in Physics.

However, in each case, the measurements are taken in the framework of the cosmological model which assumes that the universe is isotropic and homogeneous. This assumption is contradicted by the inhomogeneous distribution of galaxies and the lack of correlations on large angular scales, with the only confirmation coming from studies of the early universe through observed temperature fluctuations in the CMB radiation. It has therefore been suggested that this isotropic and homogeneous universe only exists at the larger scales, although this has yet to be confirmed.

The team therefore decided to see what happens when they remove this assumption from their analysis and measure the expansion in our own ‘heliocentric’ frame of reference.

“In the absence of any evidence of convergence to the CMB rest frame, this assumption is unjustified since it is very possible that the observed bulk flow stretches out to much larger scales.”
– Jacques Colin, Roya Mohayaee, Mohammed Rameez and Subir Sarkar

Utilising the latest extended sample size of supernovae data from the Joint Lightcurve Analysis catalogue, they were able to extract the redshifts for 740 Type 1a supernovae. To convert from a heliocentric frame of reference to a CMB frame of reference, the observed redshifts are generally corrected for ‘peculiar’ velocities – that is, velocities relative to a standard frame of rest. Therefore, to obtain the redshifts in our local frame of reference – the heliocentric frame – these corrections had to be undone.

Intriguingly, their results showed that the acceleration is a relatively local effect with a significant dipole component directed along the direction we are moving with respect to the CMB. This dipole component, in alignment with the CMB dipole moment, rejects the assumption of isotropy. It could therefore be that the cosmic acceleration inferred from supernovae observations is not due to dark energy and instead due to us being tilted observers located in a bulk flow.

RSF in perspective

Everything in the universe – including the universe itself – is in a continuous dance of expansion, contraction and rotation. This is true from the smallest system, to fundamental particles, to stars and galaxies, and right up to the universe itself. Depending on our perspective, the different systems will appear as coherent systems within systems or as areas of apparent randomness. So, although the universe is expanding, it could appear to be accelerating or decelerating depending on the scale of the observation and the vantage point of the observer.

How many breaths we have.

On average, a person at rest takes 

about 16 breaths per minute. This 

means we breathe about 960 breaths 

an hour, 23,040 breaths a day, 8,409,600 

a year. Unless we get a lot of exercise. 

The person who lives to 80 will take about 

672,768,000 breaths in a lifetime.

Ultrafast quantum simulations: A new twist to an old approach

Ultrafast quantum simulations

Billions of tiny interactions occur between thousands of particles in every piece of matter in the blink of an eye. Simulating these interactions in their full dynamics was said to be elusive but has now been made possible by new work of researchers from Oxford and Warwick.

In doing so, they have paved the way for new insights into the complex mutual interactions between the particles in extreme environments such as at the heart of large planets or laser nuclear fusion.

Researchers at the University of Warwick and University of Oxford have developed a new way to simulate quantum systems of many particles, that allows for the investigation of the dynamic properties of quantum systems fully coupled to slowly moving ions.

Effectively, they have made the simulation of the quantum electrons so fast that it could run extremely long without restrictions and the effect of their motion on the movement of the slow ions would be visible.

Reported in the journal Science Advances, it is based on a long-known alternative formulation of quantum mechanics (Bohm dynamics) which the scientists have now empowered to allow to study the dynamics of large quantum systems.

Many quantum phenomena have been studied for single or just a few interacting particles as large complex quantum systems overpower scientists' theoretical and computational capabilities to make predictions. This is complicated by the vast difference in timescale the different particle species act on: ions evolve thousands of times more slowly than electrons due to their larger mass. To overcome this problem, most methods involve decoupling electrons and ions and ignoring the dynamics of their interactions -- but this severely limits our knowledge on quantum dynamics.

To develop a method that allows scientists to account for the full electron-ion interactions, the researchers revived an old alternative formulation of quantum mechanics developed by David Bohm. In quantum mechanics, one needs to know the wave function of a particle. It turns out that describing it by the mean trajectory and a phase, as done by Bohm, is very advantageous. However, it took an additional suit of approximations and many tests to speed up the calculations as dramatic as required. Indeed, the new methods demonstrated an increase of speed by more than a factor of 10,000 (four orders of magnitude) yet is still consistent with previous calculations for static properties of quantum systems.

The new approach was then applied to a simulation of warm dense matter, a state between solids and hot plasmas, that is known for its inherent coupling of all particle types and the need for a quantum description. In such systems, both the electrons and the ions can have excitations in the form of waves and both waves will influence each other. Here, the new approach can show its strength and determined the influence of the quantum electrons on the waves of the classical ions while the static properties were proven to agree with previous data.

Many-body quantum systems are the core of many scientific problem ranging from the complex biochemistry in our bodies to the behaviour of matter inside of large planets or even technological challenges like high-temperature superconductivity or fusion energy which demonstrates the possible range of applications of the new approach.

Prof Gianluca Gregori (Oxford), who led the investigation, said: "Bohm quantum mechanics has often been treated with skepticism and controversy. In its original formulation, however, this is just a different reformulation of quantum mechanics. The advantage in employing this formalism is that different approximations become simpler to implement and this can increase the speed and accuracy of simulations involving many-body systems."

Dr Dirk Gericke from the University of Warwick, who assisted the design of the new computer code, said: "With this huge increase of numerical efficiency, it is now possible to follow the full dynamics of fully interacting electron-ion systems. This new approach thus opens new classes of problems for efficient solutions, in particular, where either the system is evolving or where the quantum dynamics of the electrons has a significant effect on the heavier ions or the entire system.

"This new numerical tool will be a great asset when designing and interpreting experiments on warm dense matter. From its results, and especially when combined with designated experiments, we can learn much about matter in large planets and for laser fusion research. However, I believe its true strength lies in its universality and possible applications in quantum chemistry or strongly driven solids."

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Materials provided by University of Warwick. Note: Content may be edited for style and length.

The difference between an expert's brain and a novice's

Neurons illustration

When mice learn to do a new task, their brain activities change over time as they advance from 'novice' to 'expert.' The changes are reflected in the wiring of cell circuits and activities of neurons.

Using a two-photon imaging microscope and a wealth of genetic tools, researchers from Cold Spring Harbor Laboratory (CSHL), Columbia University, University College London, and Flatiron Institute found that neural networks become more focused as mice got better at performing a trained task. They used the data to construct computational models that can inform their understanding of the neuroscience behind decision-making.

"We recorded the activity from hundreds of neurons all at the same time, and studied what the neurons did over learning," said CSHL Associate Professor Anne Churchland. "Nobody really knew how animals or humans learn the structure of a task and how the neural activity supports that."

The team, including Farzaneh Najafi, the first author on the study and a postdoctoral fellow in Churchland's lab, started by training mice on perceptual decision-making tasks. The mice received multisensory stimuli in the form of a sequence of clicks and flashes that were presented together. Their job was to tell researchers whether those are happening at a high or low rate by licking one of three waterspouts in front of them.

They licked the middle spout to start the trial, one side to report a high-rate decision and the other side for a low-rate decision. When the mice made the correct decision, they received a reward.

"Most decision-making studies focused on the period where the animals are really experts. But we were able to see how they arrive at the state by measuring the neurons in their brain all the way through learning," said Churchland, the senior author on the study. "We found that in all the animals, their learning occurs gradually over about four weeks. And we found that what supports learning is activity changes in a whole bunch of neurons."

The neurons, the team discovered, became more selective in responding to an activity associated with a particular task. The also started reacting faster and more immediately.

"They'll respond really strongly in advance of one choice and much less so in advance of the other choice," Churchland said.

When the animals are just beginning to learn, the neurons don't respond until around the time it makes the choice. But as the animal gains expertise, the neurons respond much further in advance.

"We can kind of read the animal's mind in a way, we can predict what the animal is going to do before he does it," Churchland said. "When you're a novice at something your brain is doing all different things, so you have neurons engaged in all different things. But then when you're an expert, you hone in on exactly what you're going to do and we can pick up that activity."

The researchers decoded neural activity by training a small artificial network called the 'Linear Support Vector Machine' using machine learning algorithms. It collects performance data from multiple trials and combines it with the activity of all the neurons, weighing them to make a guess about what the animal's going to do. As the animal gets better at the task, its neural networks get more refined, precise and specific. The researchers are able to mirror that onto the artificial network, which can then predict the animal's decision with about 90 percent accuracy.

The learning models also offer another way of looking at specific types of neurons in the brain involved in cognition, like excitatory and inhibitory neurons, which trigger positive and negative changes, respectively. In this study, published in Neuron (Cell Press), the team found that the inhibitory neurons are part of very selective sub-networks in the brain, and they're strongly selective for the choice that the animal's going to make.

These neurons are part of a biophysical model that helps researchers understand how decision making works. As researchers refine these models, they're able to make more sense of how cognition informs behavior.

"We've learned a lot about perceptual decision-making-the decisions that a subject would get right and wrong, how long it takes to make those decisions, what the neural activity would look like during decision-making-by making different kinds of models that make really concrete predictions," Churchland said. "Now we can understand, hopefully better, why these very selective sub-networks are there, how they help us make better decisions, and how they are wired up during learning."

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Story Source:

Materials provided by Cold Spring Harbor Laboratory. Original written by Charlotte Hu. Note: Content may be edited for style and length.

The difference between an expert's brain and a novice's

Neurons illustration (stock image).

When mice learn to do a new task, their brain activities change over time as they advance from 'novice' to 'expert.' The changes are reflected in the wiring of cell circuits and activities of neurons.

Using a two-photon imaging microscope and a wealth of genetic tools, researchers from Cold Spring Harbor Laboratory (CSHL), Columbia University, University College London, and Flatiron Institute found that neural networks become more focused as mice got better at performing a trained task. They used the data to construct computational models that can inform their understanding of the neuroscience behind decision-making.

"We recorded the activity from hundreds of neurons all at the same time, and studied what the neurons did over learning," said CSHL Associate Professor Anne Churchland. "Nobody really knew how animals or humans learn the structure of a task and how the neural activity supports that."

The team, including Farzaneh Najafi, the first author on the study and a postdoctoral fellow in Churchland's lab, started by training mice on perceptual decision-making tasks. The mice received multisensory stimuli in the form of a sequence of clicks and flashes that were presented together. Their job was to tell researchers whether those are happening at a high or low rate by licking one of three waterspouts in front of them.

They licked the middle spout to start the trial, one side to report a high-rate decision and the other side for a low-rate decision. When the mice made the correct decision, they received a reward.

"Most decision-making studies focused on the period where the animals are really experts. But we were able to see how they arrive at the state by measuring the neurons in their brain all the way through learning," said Churchland, the senior author on the study. "We found that in all the animals, their learning occurs gradually over about four weeks. And we found that what supports learning is activity changes in a whole bunch of neurons."

The neurons, the team discovered, became more selective in responding to an activity associated with a particular task. The also started reacting faster and more immediately.

"They'll respond really strongly in advance of one choice and much less so in advance of the other choice," Churchland said.

When the animals are just beginning to learn, the neurons don't respond until around the time it makes the choice. But as the animal gains expertise, the neurons respond much further in advance.

"We can kind of read the animal's mind in a way, we can predict what the animal is going to do before he does it," Churchland said. "When you're a novice at something your brain is doing all different things, so you have neurons engaged in all different things. But then when you're an expert, you hone in on exactly what you're going to do and we can pick up that activity."

The researchers decoded neural activity by training a small artificial network called the 'Linear Support Vector Machine' using machine learning algorithms. It collects performance data from multiple trials and combines it with the activity of all the neurons, weighing them to make a guess about what the animal's going to do. As the animal gets better at the task, its neural networks get more refined, precise and specific. The researchers are able to mirror that onto the artificial network, which can then predict the animal's decision with about 90 percent accuracy.

The learning models also offer another way of looking at specific types of neurons in the brain involved in cognition, like excitatory and inhibitory neurons, which trigger positive and negative changes, respectively. In this study, published in Neuron (Cell Press), the team found that the inhibitory neurons are part of very selective sub-networks in the brain, and they're strongly selective for the choice that the animal's going to make.

These neurons are part of a biophysical model that helps researchers understand how decision making works. As researchers refine these models, they're able to make more sense of how cognition informs behavior.

"We've learned a lot about perceptual decision-making-the decisions that a subject would get right and wrong, how long it takes to make those decisions, what the neural activity would look like during decision-making-by making different kinds of models that make really concrete predictions," Churchland said. "Now we can understand, hopefully better, why these very selective sub-networks are there, how they help us make better decisions, and how they are wired up during learning."

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Story Source:

Materials provided by Cold Spring Harbor Laboratory. Original written by Charlotte Hu. Note: Content may be edited for style and length.

Probing the role of an inflammation resolution sensor in obesity and heart failure

After heart attack injury, several fatty-acid-derived bioactive molecules -- including one called resolvin D1 -- play an essential signaling role to safely clear inflammation and help repair heart muscle. The mechanism of how this resolution occurs is not well-understood.

There is a receptor on the surface of many immune cells called ALX/FRP2, and in models of atherosclerosis, ALX/FPR2 is known to act as a sensor to help resolve inflammation.

In a 2015 study using a mouse model, University of Alabama at Birmingham researcher Ganesh Halade, Ph.D., observed that, after heart attack injury, ALX/FPR2 was highly expressed in immune myeloid cells and was activated by resolvin D1 in immune cells in the spleen and in immune cells at the heart attack site. The result was an expedited resolution of the heart attack injury. Resolvin D1 is one of the omega 3 fatty-acid metabolites known as specialized pro-resolving mediators, or SPMs, that help clear inflammation.

Now, Halade and colleagues at UAB, Boston and France have used mice that completely lack ALX/FPR2 to learn more about the pathways this resolution sensor uses to target inflammation. Such knowledge will help in finding treatments to delay the human heart failure that often follows a heart attack.

Before beginning the mouse studies, Halade and colleagues examined heart muscle tissue from patients with heart failure. They found that ALX/FPR2 was plentiful in these human ischemic hearts, and it was located in the cytoplasm of the myocardium cells. In contrast, in healthy human heart tissue, ALX/FPR2 was limited to the cell membrane. To learn more, they then expanded study of the precise and comprehensive role of the resolution receptor using mice having an ALX/FPR2 gene deletion.

The researchers found that mice lacking ALX/FPR2 showed spontaneous, age-related obesity. With the obesity, the ALX/FPR2-null mice developed heart disease that weakened the heart's ability to pump blood, and they had a shortened lifespan with aging. The aging mice also developed kidney inflammation, as shown by increased inflammation markers like NGAL, TNF-alpha and CCL2, and elevated plasma creatinine levels.

After a heart attack in normal mice, leukocyte immune cells in the spleen produce SPMs. However, in the ALX/FPR2-null mice, the researchers found lower levels of SPMs in the heart and the spleen after heart attack, indicative of non-resolving inflammation. Halade says this suggested impaired cross-talk between the injured heart and splenic leukocytes, a cross-talk that is required for the resolution of inflammation. In addition to the lower levels of SPMs, the ALX/FPR2-null mice showed dysregulation of several immune responsive enzymes -- lower levels of LOX enzymes and increased levels of the pro-inflammatory COX-1 and COX-2 enzymes.

Finally, the ALX/FPR2-null mice showed impairment of activated macrophage cells to phagocytose -- that is, to "eat" infecting microbes or dead human cells, one of the macrophage's prime functions. After heart attack, the ALX/FPR2-null mice had increased numbers of neutrophils, the first phagocytic responders after heart injury, in both the spleen and the left ventricle of the heart. Also, there were reduced numbers of reparative macrophages in both the spleen and the heart.

Altogether, says Halade, an associate professor in the UAB Department of Medicine Division of Cardiovascular Disease, these findings demonstrate the integrative role of ALX/FPR2 as a primary target to manage cardiometabolic health, inflammation-resolution processes and cardiorenal syndrome in aging.

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Materials provided by University of Alabama at Birmingham. Note: Content may be edited for style and length.