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The influence of physical activity in the progression of experimental lung cancer in mice
- PMID: 22683274
- DOI: 10.1016/j.prp.2012.04.006
GRUPO_AF1 – GROUP AFA1 – Aerobic Physical Activity – Atividade Física Aeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto
GRUPO AFAN 1 – GROUP AFAN1 – Anaerobic Physical Activity – Atividade Física Anaeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto
GRUPO_AF2 – GROUP AFA2 – Aerobic Physical Activity – Atividade Física Aeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto
GRUPO AFAN 2 – GROUP AFAN 2 – Anaerobic Physical Activity – Atividade Física Anaeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto
Slides – mestrado – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto
DMBA CARCINOGEN IN EXPERIMENTAL MODELS
Avaliação da influência da atividade física aeróbia e anaeróbia na progressão do câncer de pulmão experimental – Summary – Resumo – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto
Lung cancer is one of the most incident neoplasms in the world, representing the main cause of mortality for cancer. Many epidemiologic studies have suggested that physical activity may reduce the risk of lung cancer, other works evaluate the effectiveness of the use of the physical activity in the suppression, remission and reduction of the recurrence of tumors. The aim of this study was to evaluate the effects of aerobic and anaerobic physical activity in the development and the progression of lung cancer. Lung tumors were induced with a dose of 3mg of urethane/kg, in 67 male Balb – C type mice, divided in three groups: group 1_24 mice treated with urethane and without physical activity; group 2_25 mice with urethane and subjected to aerobic swimming free exercise; group 3_18 mice with urethane, subjected to anaerobic swimming exercise with gradual loading 5-20% of body weight. All the animals were sacrificed after 20 weeks, and lung lesions were analyzed. The median number of lesions (nodules and hyperplasia) was 3.0 for group 1, 2.0 for group 2 and 1.5-3 (p=0.052). When comparing only the presence or absence of lesion, there was a decrease in the number of lesions in group 3 as compared with group 1 (p=0.03) but not in relation to group 2. There were no metastases or other changes in other organs. The anaerobic physical activity, but not aerobic, diminishes the incidence of experimental lung tumors.
Copyright © 2012 Elsevier GmbH. All rights reserved.
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SEPTEMBER 25, 2019
New research brings scientists one step closer to a fully functioning quantum computer
Quantum computing has the potential to revolutionize technology, medicine, and science by providing faster and more efficient processors, sensors, and communication devices.
But transferring information and correcting errors within a quantum system remains a challenge to making effective quantum computers.
In a paper in the journal Nature, researchers from Purdue University and the University of Rochester, including John Nichol, an assistant professor of physics, and Rochester Ph.D. students Yadav P. Kandel and Haifeng Qiao, demonstrate their method of relaying information by transferring the state of electrons. The research brings scientists one step closer to creating fully functional quantum computers and is the latest example of Rochester’s initiative to better understand quantum behavior and develop novel quantum systems. The University recently received a $4 million grant from the Department of Energy to explore quantum materials.
A quantum computer operates on the principles of quantum mechanics, a unique set of rules that govern at the extremely small scale of atoms and subatomic particles. When dealing with particles at these scales, many of the rules that govern classical physics no longer apply and quantum effects emerge; a quantum computer is able to perform complex calculations, factor extremely large numbers, and simulate the behaviors of atoms and particles at levels that classical computers cannot.
Quantum computers have the potential to provide more insight into principles of physics and chemistry by simulating the behavior of matter at unusual conditions at the molecular level. These simulations could be useful in developing new energy sources and studying the conditions of planets and galaxies or comparing compounds that could lead to new drug therapies.
“You and I are quantum systems. The particles in our body obey quantum physics. But, if you try to compute what happens with all of the atoms in our body, you cannot do it on a regular computer,” Nichol says. “A quantum computer could easily do this.”
Quantum computers could also open doors for faster database searches and cryptography.
“It turns out that almost all of modern cryptography is based on the extreme difficulty for regular computers to factor large numbers,” Nichol says. “Quantum computers can easily factor large numbers and break encryption schemes, so you can imagine why lots of governments are interested in this.”
Bits vs. qubits
A regular computer consists of billions of transistors, called bits. Quantum computers, on the other hand, are based on quantum bits, also known as qubits, which can be made from a single electron. Unlike ordinary transistors, which can be either “0” or “1,” qubits can be both “0” and “1” at the same time. The ability for individual qubits to occupy these “superposition states,” where they are simultaneously in multiple states, underlies the great potential of quantum computers. Just like ordinary computers, however, quantum computers need a way to transfer information between qubits, and this presents a major experimental challenge.
“A quantum computer needs to have many qubits, and they’re really difficult to make and operate,” Nichol says. “The state-of-the art right now is doing something with only a few qubits, so we’re still a long ways away from realizing the full potential of quantum computers.”
All computers, including both regular and quantum computers and devices like smart phones, also have to perform error correction. A regular computer contains copies of bits so if one of the bits goes bad, “the rest are just going to take a majority vote” and fix the error. However, quantum bits cannot be copied, Nichol says, “so you have to be very clever about how you correct for errors. What we’re doing here is one step in that direction.”
Quantum error correction requires that individual qubits interact with many other qubits. This can be difficult because an individual electron is like a bar magnet with a north pole and a south pole that can point either up or down. The direction of the pole—whether the north pole is pointing up or down, for instance—is known as the electron’s magnetic moment or quantum state.
If certain kinds of particles have the same magnetic moment, they cannot be in the same place at the same time. That is, two electrons in the same quantum state cannot sit on top of each other.
“This is one of the main reasons something like a penny, which is made out of metal, doesn’t collapse on itself,” Nichol says. “The electrons are pushing themselves apart because they cannot be in the same place at the same time.”
If two electrons are in opposite states, they can sit on top of each other. A surprising consequence of this is that if the electrons are close enough, their states will swap back and forth in time.
“If you have one electron that’s up and another electron that’s down and you push them together for just the right amount of time, they will swap,” Nichol says. “They did not switch places, but their states switched.”
To force this phenomenon, Nichol and his colleagues cooled down a semiconductor chip to extremely low temperatures. Using quantum dots—nanoscale semiconductors—they trapped four electrons in a row, then moved the electrons so they came in contact and their states switched.
“There’s an easy way to switch the state between two neighboring electrons, but doing it over long distances—in our case, it’s four electrons—requires a lot of control and technical skill,” Nichol says. “Our research shows this is now a viable approach to send information over long distances.”
A first step
Transmitting the state of an electron back and forth across an array of qubits, without moving the position of electrons, provides a striking example of the possibilities allowed by quantum physics for information science.
“This experiment demonstrates that information in quantum states can be transferred without actually transferring the individual electron spins down the chain,” says Michael Manfra, a professor of physics and astronomy at Purdue University. “It is an important step for showing how information can be transmitted quantum-mechanically—in manners quite different than our classical intuition would lead us to believe.”
Nichol likens this to the steps that led from the first computing devices to today’s computers. That said, will we all someday have quantum computers to replace our desktop computers? “If you had asked that question of IBM in the 1960s, they probably would’ve said no, there’s no way that’s going to happen,” Nichol says. “That’s my reaction now. But, who knows?”
Explore furtherNew method for detecting quantum states of electrons
More information: Coherent spin-state transfer via Heisenberg exchange, Nature (2019). DOI: 10.1038/s41586-019-1566-8 , https://nature.com/articles/s41586-019-1566-8Journal information:NatureProvided by University of Rochester2143 shares
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danR Sep 25, 2019″A regular computer consists of billions of transistors, called bits.”
Castrogiovanni Sep 25, 2019
“A regular computer consists of billions of transistors, called bits.”
A regular brain consists of billions of neurons, called chromosomes.
Hui Zhang Sep 26, 2019Atoms, electrons and other microscopic build-ups have duality which is determined by Schrodinger’s wave function. They have particle properties which is the same as classical particle. But at the same time, they have wave properties which is not the same as classical wave. They are energies that is distributed across the space without medium. (Classical wave is distributed by medium.) 0 Report Block
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Lincoln Laboratory’s new AI supercomputer is the most powerful at a university
TX-GAIA is tailor-made for crunching through deep neural network operations.SEPTEMBER 26, 2019 Kylie Foy | Communications & Community Outreach Office
The new TX-GAIA (Green AI Accelerator) computing system at Lincoln Laboratory’s Supercomputing Center (LLSC) has been ranked as the most powerful artificial intelligence (AI) supercomputer at any university in the world. The ranking comes from TOP500, which publishes a list of the top supercomputers in various categories biannually. The system, which was built by Hewlett Packard Enterprise, combines traditional high-performance computing hardware — nearly 900 Intel processors — with hardware optimized for AI applications — 900 Nvidia GPU accelerators.
“We are thrilled by the opportunity to enable researchers across Lincoln and MIT to achieve incredible scientific and engineering breakthroughs,” said Jeremy Kepner, a Lincoln Laboratory Fellow who heads the LLSC. “TX-GAIA will play a large role in supporting AI, physical simulation, and data analysis across all Laboratory missions.”
TOP500 rankings are based on a LINPACK Benchmark, which is a measure of a system’s floating-point computing power, or how fast a computer solves a dense system of linear equations. TX-GAIA’s TOP500 benchmark performance is 3.9 quadrillion floating-point operations per second, or petaflops (though since the ranking was announced in June 2019, Hewlett Packard Enterprise has updated the system’s benchmark to 4.725 petaflops.). The June TOP500 benchmark performance places the system #1 in the Northeast, #20 in the United States, and #51 in the world for supercomputing power. The system’s peak performance is more than 6 petaflops.
But more notably, TX-GAIA has a peak performance of 100 AI petaflops, which makes it #1 for AI flops at any university in the world. An AI flop is a measure of how fast a computer can perform deep neural network (DNN) operations. DNNs are a class of AI algorithms that learn to recognize patterns in huge amounts of data. This ability has given rise to “AI miracles,” as Kepner put it, in speech recognition and computer vision; the technology is what allows Amazon’s Alexa to understand questions and self-driving cars to recognize objects in their surroundings. As these DNNs grow more complex, the longer it takes for them to process the massive datasets they learn from. TX-GAIA’s Nvidia GPU accelerators are specially designed for performing these DNN operations quickly.
TX-GAIA is housed in a new modular data center, called an EcoPOD, at the LLSC’s green, hydroelectrically powered site in Holyoke, Massachusetts. It joins the ranks of other powerful systems at the LLSC, such as the TX-E1, which supports collaborations with MIT campus and other institutions, and TX-Green, which is currently ranked #490 on the TOP500 list.
Kepner says that the system’s integration into the LLSC will be completely transparent to users when it comes online this fall. “The only thing users should see is that many of their computations will be dramatically faster,” he said.
Among its AI applications, TX-GAIA will be tapped for training machine learning algorithms, including those that use DNNs. It will more quickly crunch through terabytes of data — for example, hundreds of thousands of images or years’ worth of speech samples —to teach these algorithms to figure out solutions on their own. The system’s compute power will also expedite simulations and data analysis. These capabilities will support projects across the Laboratory’s R&D areas, such as improving weather forecasting, accelerating medical data analysis, building autonomous systems, designing synthetic DNA, and developing new materials and devices.
TX-GAIA, which is also ranked the #1 system in the Department of Defense, will also support the recently announced MIT–Air Force AI Accelerator. The partnership will combine the expertise and resources of MIT, including those at the LLSC, and the Air Force to conduct fundamental research directed at enabling rapid prototyping, scaling, and application of AI algorithms and systems.TOPIC:artificial intelligenceR&D AREA:
- Cyber Security and Information Sciences
- Lincoln Laboratory Supercomputing Center
- U.S. Air Force
- artificial intelligence
- computer science
- machine learning
© 2019 LINCOLN LABORATORY, MASSACHUSETTS INSTITUTE OF TECHNOLOGY
How long can humans live?
Humans are living longer around the world. While there have been obvious ups and downs, life expectancy at birth overall has been steadily increasing for many years. It has more than doubled in the last two centuries.
This increase was previously driven by reductions in infant mortality. But since around the 1950s, the main driver has been reductions in mortality at older ages. In Sweden, for example, where national population data have been collected since the mid-16th century and are of a very high quality, the maximum lifespan has been increasing for almost 150 years. Increasing lifespans have been observed in many other countries, including in Western Europe, North America, and Japan.
This has contributed to a rapid increase in the number of very old people—those living up to 100, 110, or even more. The first verified supercentenarian (aged 110 and above) was Geert Adrians-Boomgaard, who died in 1899 aged 110 years and four months. His record has been broken by others since. The first verified female supercentenarian, Margaret Ann Neve, died in 1903 aged 110 years and ten months, and held the record for almost 23 years. Delina Filkins passed away in 1928 aged 113 years and seven months. She kept the record for just over 52 years.
The current record holder is the French woman Jeanne Calment, who died on August 4, 1997, aged 122 years and five months. Despite the near exponential increase in the number of supercentenarians since the early 1970s, her record holds firm—but she’s unlikely to hold it for much longer.
Surviving past 100
Although these upward lifespan trends are widespread, they are not a given. Recent improvements in Danish mortality after a period of stagnation has led to the suspicion that centenarian lifespans could be increasing there. This is rather different from what has been recently observed in Sweden, where there has been some slow down at the highest ages.
We studied 16,931 centenarians (10,955 Swedes and 5,976 Danes) born between 1870 and 1904 in Denmark and Sweden, neighboring countries with close cultural and historical ties, to see if our suspicions may be correct. Although Sweden generally has lower mortality rates than Denmark at most ages, no evidence of an increase in Sweden was found in recent years. In Denmark, however, the very oldest were observed to die at higher and higher ages, and the age at which only 6% of centenarians survive rose consistently over the period.
Denmark and Sweden are similar in many ways, yet these lifespan trends are very different. The disparity could be due to several causes, which are not easy to fully disentangle. But we have a few ideas.
First, there are different levels of health among the two elderly populations. Recent studies have shown improvements in health as measured by Activities of Daily Living (ADL)—the basic tasks necessary for leading an independent life, such as bathing or getting dressed—in cohorts of female centenarians in Denmark. In Sweden, by contrast, such trends for the elderly have been less optimistic. One study found that there was no improvement in ADL, with deterioration in mobility, cognition, and performance tests.
The difference in the two healthcare systems, especially in recent times, could therefore also go some way towards explaining the difference. Spending on public services was reduced in Sweden in the early 1990s, due to a series of economic crises. Healthcare for the elderly was affected. For instance, with inpatient elder care, there was a shift away from hospitals to nursing homes and a reduction in the number of nursing home beds. The cost cuts left some older people at risk, particularly those in the lowest socioeconomic groups.
In addition, the two countries have since followed slightly different paths to elderly care: Sweden tends to target the frailest whereas Denmark takes a slightly broader approach. Some studies suggest that Sweden’s approach has resulted in some who require care not receiving it, with the least well-off segments of the elderly population relying more heavily on family care, which can be of lower quality.
People who reach advanced ages are a select group and are obviously very durable. Perhaps because of their inherent resilience and particular physiology, they are best able to benefit from the improvements in living conditions and technology.
Our comparative study suggests some interesting things for other nations, particularly where there are developing and emerging economies. These findings demonstrate that it may be possible to lengthen lifespans further if improvements in health at the highest ages can be realized and if high quality elderly care is widely available. Indeed, if this is so, then the human longevity revolution is set to continue for some time still.
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