Human Brain Has a Lymphatic System

NIH Researchers Uncover
Drain Pipes in our Brains
Results provide first evidence of the body’s waste system in the human brain

Tuesday, October 3, 2017 -- By scanning the brains of healthy volunteers, researchers at the National Institutes of Health saw the first, long-sought evidence that our brains may drain some waste out through lymphatic vessels, the body’s sewer system. The results further suggest the vessels could act as a pipeline between the brain and the immune system.

“We literally watched people’s brains drain fluid into these vessels,” said Daniel S. Reich, M.D., Ph.D., senior investigator at the NIH’s National Institute of Neurological Disorders and Stroke (NINDS) and the senior author of the study published online in eLife (link is external). “We hope that our results provide new insights to a variety of neurological disorders.”

Dr. Reich is a radiologist and neurologist who primarily uses magnetic resonance imaging (MRI) to investigate multiple sclerosis and other neurological disorders which are thought to involve the immune system. Led by post-doctoral fellows, Martina Absinta, Ph.D. and Seung-Kwon Ha, Ph.D., along with researchers from the National Cancer Institute, the team discovered lymphatic vessels in the dura, the leathery outer coating of the brain.

Lymphatic vessels are part of the body’s circulatory system. In most of the body they run alongside blood vessels. They transport lymph, a colorless fluid containing immune cells and waste, to the lymph nodes. Blood vessels deliver white blood cells to an organ and the lymphatic system removes the cells and recirculates them through the body. The process helps the immune system detect whether an organ is under attack from bacteria or viruses or has been injured.

In 1816, an Italian anatomist reported finding lymphatic vessels on the surface of the brain, but for two centuries, it was forgotten. Until very recently, researchers in the modern era found no evidence of a lymphatic system in the brain, leaving some puzzled about how the brain drains waste, and others to conclude that brain is an exceptional organ. Then in 2015, two studies of mice found evidence of the brain’s lymphatic system in the dura. Coincidentally, that year, Dr. Reich saw a presentation by Jonathan Kipnis, Ph.D., a professor at the University of Virginia and an author of one the mouse studies.

“I was completely surprised. In medical school, we were taught that the brain has no lymphatic system,” said Dr. Reich. “After Dr. Kipnis’ talk, I thought, maybe we could find it in human brains?”

To look for the vessels, Dr. Reich’s team used MRI to scan the brains of five healthy volunteers who had been injected with gadobutrol, a magnetic dye typically used to visualize brain blood vessels damaged by diseases, such as multiple sclerosis or cancer. The dye molecules are small enough to leak out of blood vessels in the dura but too big to pass through the blood-brain barrier and enter other parts of the brain.

At first, when the researchers set the MRI to see blood vessels, the dura lit up brightly, and they could not see any signs of the lymphatic system. But, when they tuned the scanner differently, the blood vessels disappeared, and the researchers saw that dura also contained smaller but almost equally bright spots and lines which they suspected were lymph vessels. The results suggested that the dye leaked out of the blood vessels, flowed through the dura and into neighboring lymphatic vessels.

To test this idea, the researchers performed another round of scans on two subjects after first injecting them with a second dye made up of larger molecules that leak much less out of blood vessels. In contrast with the first round of scans, the researchers saw blood vessels in the dura but no lymph vessels regardless of how they tuned the scanner, confirming their suspicions.

They also found evidence for blood and lymph vessels in the dura of autopsied human brain tissue. Moreover, their brain scans and autopsy studies of brains from nonhuman primates confirmed the results seen in humans, suggesting the lymphatic system is a common feature of mammalian brains.

“These results could fundamentally change the way we think about how the brain and immune system inter-relate,” said Walter J. Koroshetz, M.D., NINDS director.

Dr. Reich’s team plans to investigate whether the lymphatic system works differently in patients who have multiple sclerosis or other neuroinflammatory disorders.

“For years we knew how fluid entered the brain. Now we may finally see that, like other organs in the body, brain fluid can drain out through the lymphatic system,” said Dr. Reich.

Article:  Absinta, Ha et al. Human and nonhuman primate meninges harbor lymphatic vessels that can be visualized noninvasively by MRI, October 3, 2017, eLife: 10.7554/eLife.29738

https://www.ninds.nih.gov/News-Events/News-and-Press-Releases/Press-Releases/NIH-researchers-uncover-drain-pipes-our-brains

Structure of 'Amorphous Ice'

Studies of ‘Amorphous Ice’
Reveal Hidden Order in Glass
By Catherine Zandonella, Office of the Dean for Research

Princeton, October 2, 2017 -- A new study challenges the notion that the atomic structure of glass is indistinguishable from that of a liquid­ ­— at least for a certain kind of glass called “amorphous ice” that forms when water is cooled to very low temperatures.

In the study, researchers at Princeton University and the City University of New York used computer simulations to show that the water molecules in amorphous ice are arranged in a previously undetected order, which the original liquid did not contain. The finding, published Sept. 29 in the journal Physics Review Letters, may help explain water’s curious, life-giving properties. It also challenges the very definition of what it means to be a glass.

Glasses are typically made by cooling a liquid rapidly, and, according to current understanding, a glass inherits the order that was present in the original liquid. In the case of amorphous ice, however, as the liquid water cools, a new and orderly arrangement of molecules emerges.

“According to our results, these types of glass are not simply frozen liquids — this picture doesn’t hold anymore,” said Fausto Martelli, an associate research scholar in the Department of Chemistry at Princeton. “We are essentially saying that a notion that scientists have believed for many years is partially wrong.”

Prior to this study, researchers knew that the rapid freezing of water, which can occur at the extremely cold temperatures found in outer space, leads to the formation of a very different material than the ice of everyday experience. This material, known as amorphous ice, lacks regular ice’s highly ordered crystalline structure, leading scientists to categorize it as a glass — a liquid whose movement has slowed to a glacial pace. Amorphous ices are not common on Earth, but they are the most abundant form of water in the universe.

The new study found that the molecules in these amorphous ices arrange into a previously undetected internal pattern. This pattern, known as disordered hyperuniformity, is defined as order across large spatial distances even when there is no order across short distances. Disordered hyperuniform materials fall somewhere between a crystal, which is highly organized over long distances, and a liquid, which is only ordered over short distances.

“The existence of these large-scale structural correlations has not been fully appreciated, and that is really what we wanted to address in this study,” said study co-author Salvatore Torquato, a professor of chemistry who, with Princeton senior scientist Frank Stillinger, first identified hyperuniformity more than a decade ago (Physical Review E, 2003). “The information present in these systems is quite striking, and leads to completely new insights about materials,” he said. He and his colleagues have since identified hyperuniformity in a number of places, including the arrangement of cells in a chicken’s eye (Physical Review E, 2014).

In addition to Martelli and Torquato, the study authors included Roberto Car, Princeton’s Ralph W. *31 Dornte Professor in Chemistry, and Nicolas Giovambattista, an associate professor at Brooklyn College-The City University of New York. Torquato and Car are associated with the Princeton Institute for the Science and Technology of Materials.

To explore the internal structure of amorphous ices, Martelli used a computer model that tracks the behavior of over 8,000 water molecules to simulate what would happen if he cooled the water down to about 80 degrees Kelvin (about -316 degrees Fahrenheit). At this temperature, water molecules are so deprived of heat that they can no longer move from place to place, nor rotate in place. At this temperature and below, the researchers observed the hyperuniform pattern emerge in the data from the computer simulation.

“We are not used to looking for order on such large length-scales,” Martelli said. “However, mathematics allows us to shed light on patterns that our eyes are not able to see.”

The simulations required months of time on high-performance research computers, including Princeton University’s TIGRESS clusters through the Princeton Institute for Computational Science and Engineering.

The simulation enabled the researchers to ask questions about the nature of water, which has many anomalous behaviors that make it uniquely suited to supporting life. One such anomaly is that the crystalline form of ice is less dense than liquid water, allowing ice to float, which in turn allows life to exist under the ice in lakes and oceans.

One possible explanation for water’s anomalies is that, at very cold temperatures, water may come in two liquid phases — one denser than the other ­­— rather than just the one liquid state we find familiar. Detecting the transition of water between the high-density and low-density forms has proven elusive due to technical challenges.

The current study provides indirect support for the existence of the two forms, at least in computer simulations. Giovambattista simulated the application of high pressure to the model and observed that the pressure converted the low-density form of amorphous ice into a high-density form. The transition between the two forms of amorphous ice is consistent with the existence of two liquid forms of water.

Understanding the long-range order present in amorphous materials is an active area of study because harnessing hyperuniformity could lead to practical applications. The hyperuniformity present in amorphous silicon may enable new ways to tune electronic properties. The ability to manipulate a material’s hyperuniform long-range order may help scientists build stronger ceramics or longer-lasting glasses.

Amorphous ices can be produced in laboratory settings, Martelli said, and it may be possible to find evidence of hyperuniformity in these experiments.

https://www.princeton.edu/news/2017/10/02/studies-amorphous-ice-reveal-hidden-order-glass

 

Intel Chip Imitates Brain Synapses

Intel’s new ‘Loihi’ Chip Mimics Neurons and Synapses in the Human Brain
Automatically gets smarter over time

September 29, 2017 -- Intel announced this week a self-learning, energy-efficient neuromorphic (brain-like) research chip codenamed “Loihi” that mimics how the human brain functions. Under development for six years, the chip uses 130,000 “neurons” and 130 million “synapses” and learns in real time, based on feedback from the environment.

Neuromorphic chip models are inspired by how neurons communicate and learn, using spikes (brain pulses) and synapses capable of learning.

The idea is to help computers self-organize and make decisions based on patterns and associations,” Michael Mayberry, PhD, corporate vice president and managing director of Intel Labs at Intel Corporation, explained in a blog post.

He said the chip automatically gets smarter over time and doesn’t need to be trained in the traditional way. He sees applications in areas that would benefit from autonomous operation and continuous learning in an unstructured environment, such as automotive, industrial, and personal-robotics areas.

For example, a cybersecurity system could identify a breach or a hack based on an abnormality or difference in data streams. Or the chip could learn a person’s heartbeat reading under various conditions — after jogging, following a meal or before going to bed — to determine a “normal” heartbeat. The system could then continuously monitor incoming heart data to flag patterns that don’t match the “normal” pattern, and could be personalized for any user.

“Machine learning models such as deep learning have made tremendous recent advancements by using extensive training datasets to recognize objects and events. However, unless their training sets have specifically accounted for a particular element, situation or circumstance, these machine learning systems do not generalize well,” Mayberry notes.

The Loihi test chip

Loihi currently exists as a research test chip that offers flexible on-chip learning and combines training and inference. Researchers have demonstrated it learning at a rate that is a 1 million times improvement compared with other typical spiking neural nets, as measured by total operations to achieve a given accuracy when solving MNIST digit recognition problems, Mayberry said. “Compared to technologies such as convolutional neural networks and deep learning neural networks, the Loihi test chip uses many fewer resources on the same task.”

Fabricated on Intel’s 14 nm process technology, the chip is also up to 1,000 times more energy-efficient than general-purpose computing required for typical training systems, he added.

In the first half of 2018, Intel plans to share the Loihi test chip with leading university and research institutions with a focus on advancing AI. The goal is to develop and test several algorithms with high efficiency for problems including path planning, constraint satisfaction, sparse coding, dictionary learning, and dynamic pattern learning and adaptation.

“Looking to the future, Intel believes that neuromorphic computing offers a way to provide exascale performance in a construct inspired by how the brain works,” Mayberry said.

* “Loihi seamount, sometimes known as the ‘youngest volcano’ in the Hawaiian chain, is an undersea mountain rising more than 3000 meters above the floor of the Pacific Ocean … submerged in the Pacific off of the south-eastern coast of the Big Island of Hawaii.” — Hawaii Center for Volcanology

** For comparison, IBM’s TrueNorth neuromorphic chip currently has 1 million neurons and 256 million synapses.

                        http://www.kurzweilai.net/intels-new-loihi-chip-mimics-neurons-and-synapses-in-the-human-brain

A.I. via Neuromorphic Engineering

Neuromorphic engineering, also known as neuromorphic computing, is a concept developed by Carver Mead, in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. In recent times the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors*, threshold switches, and transistors.

A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change.

Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.

Examples

As early as 2006, researchers at Georgia Tech published a field programmable neural array. This chip was the first in a line of many increasingly complex arrays of floating gate transistors that allowed programmability of charge on the gates of MOSFETs to model the channel-ion characteristics of neurons in the brain and was one of the first cases of a silicon programmable array of neurons.

In November 2011, a group of MIT researchers created a computer chip that mimics the analog, ion-based communication in a synapse between two neurons using 400 transistors and standard CMOS manufacturing techniques.

In June 2012, Spintronic Researchers at Purdue presented a paper on design for a neuromorphic chip using lateral spin valves and memristors. They argue that the architecture they have designed works in a similar way to neurons and can therefore be used to test various ways of reproducing the brain's processing ability. In addition, these chips are significantly more energy-efficient than conventional ones.

Research at HP Labs on Mott memristors has shown that while they can be non-volatile, the volatile behavior exhibited at temperatures significantly below the phase transition temperature can be exploited to fabricate a neuristor, a biologically-inspired device that mimics behavior found in neurons. In September 2013 they presented models and simulations that show how the spiking behavior of these neuristors can be used to form the components required for a Turing machine.

Neurogrid, built by Brains in Silicon at Stanford University, is an example of hardware designed using neuromorphic engineering principles. The circuit board is composed of 16 custom-designed chips, referred to as NeuroCores. Each NeuroCore's analog circuitry is designed to emulate neural elements for 65536 neurons, maximizing energy efficiency. The emulated neurons are connected using digital circuitry designed to maximize spiking throughput.

A research project with implications for neuromorphic engineering is the Human Brain Project, a 10-year collaboration that is attempting to simulate a complete human brain in a supercomputer using biological data. It is made up of a group of researchers in neuroscience, medicine, and computing. Henry Markram, the project's co-director, has stated that the project is proposing to establish a new foundation, to explore and understand the brain and its diseases, and to use that knowledge to build new computing technologies. The three primary goals of the project are to better understand how the pieces of the brain fit and work together, to understand how to objectively diagnose and treat brain diseases, and to use the understanding of the human brain to develop neuromorphic computers. That the simulation of a complete human brain will require a supercomputer a thousand times more powerful than today's encourages the current focus on neuromorphic computers. $1.3 billion has been allocated to the project by The European Commission.

Other research with implications for neuromorphic engineering involves the BRAIN Initiative and the TrueNorth chip from IBM. Neuromorphic devices have also been demonstrated using nanocrystals, nanowires, and conducting polymers.

Neuromemristive Systems

Neuromemristive systems are a subclass of neuromorphic computing systems that focus on the use of memristors to implement neuroplasticity. While neuromorphic engineering focuses on mimicking biological behavior, neuromemristive systems focus on abstraction. For example, a neuromemristive system may replace the details of a cortical microcircuit's behavior with an abstract neural network model.

There exist several neuron inspired threshold logic functions implemented with memristors that have applications in high level pattern recognition applications. Some of the applications reported in recently include speech recognition, face recognition and object recognition. They also find applications in replacing conventional digital logic gates.

For ideal passive memristive circuits, it is possible to derive a differential equation for evolution of the internal memory of the circuit.

                                 https://en.wikipedia.org/wiki/Neuromorphic_engineering

= = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

     *Memristor

A memristor (a portmanteau of memory resistor) is a hypothetical non-linear passive two-terminal electrical component relating electric charge and magnetic flux linkage. It was envisioned, and its name coined, in 1971 by circuit theorist Leon Chua. According to the characterizing mathematical relations, the memristor would hypothetically operate in the following way: The memristor's electrical resistance is not constant but depends on the history of current that had previously flowed through the device, i.e., its present resistance depends on how much electric charge has flowed in what direction through it in the past; the device remembers its history — the so-called non-volatility property. When the electric power supply is turned off, the memristor remembers its most recent resistance until it is turned on again.

In 2008, a team at HP Labs claimed to have found Chua's missing memristor based on an analysis of a thin film of titanium dioxide thus connecting the operation of RRAM devices to the memristor concept. The HP result was published in the scientific journal Nature. Following this claim, Leon Chua has argued that the memristor definition could be generalized to cover all forms of two-terminal non-volatile memory devices based on resistance switching effects. There are, however, some serious doubts as to whether the memristor can actually exist in physical reality. Additionally, some experimental evidence contradicts Chua's generalization since a non-passive nanobattery effect is observable in resistance switching memory. Chua also argued that the memristor is the oldest known circuit element, with its effects predating the resistor, capacitor and inductor.

These devices are intended for applications in nanoelectronic memories, computer logic and neuromorphic/neuromemristive computer architectures. In 2013, Hewlett-Packard CTO Martin Fink suggested that commercial availability of memristor memory could arrive as early as 2018. In March 2012, a team of researchers from HRL Laboratories and the University of Michigan announced the first functioning memristor array built on a CMOS chip.

                                          https://en.wikipedia.org/wiki/Memristor

Inventor Raymond Kurzweil

Raymond "Ray" Kurzweil (born February 12, 1948) is an American author, computer scientist, inventor and futurist. Aside from futurism, he is involved in fields such as optical character recognition (OCR), text-to-speech synthesis, speech recognition technology, and electronic keyboard instruments. He has written books on health, artificial intelligence (AI), transhumanism, the technological singularity, and futurism. Kurzweil is a public advocate for the futurist and transhumanist movements, and gives public talks to share his optimistic outlook on life extension technologies and the future of nanotechnology, robotics, and biotechnology.

                                                        Raymond Kurzweil (circa 2005)

Kurzweil was the principal inventor of the first charge-coupled device flatbed scanner, the first omni-font optical character recognition, the first print-to-speech reading machine for the blind, the first commercial text-to-speech synthesizer, the Kurzweil K250 music synthesizer capable of simulating the sound of the grand piano and other orchestral instruments, and the first commercially marketed large-vocabulary speech recognition.

Kurzweil received the 1999 National Medal of Technology and Innovation, the United States' highest honor in technology, from President Clinton in a White House ceremony. He was the recipient of the $500,000 Lemelson-MIT Prize for 2001, the world's largest for innovation. And in 2002 he was inducted into the National Inventors Hall of Fame, established by the U.S. Patent Office. He has received twenty-one honorary doctorates, and honors from three U.S. presidents. Kurzweil has been described as a "restless genius" by The Wall Street Journal and "the ultimate thinking machine" by Forbes. PBS included Kurzweil as one of 16 "revolutionaries who made America" along with other inventors of the past two centuries. Inc. magazine ranked him #8 among the "most fascinating" entrepreneurs in the United States and called him "Edison's rightful heir".

Kurzweil has written seven books, five of which have been national bestsellers. The Age of Spiritual Machines has been translated into 9 languages and was the #1 best-selling book on Amazon in science. Kurzweil's book The Singularity Is Near was a New York Times bestseller, and has been the #1 book on Amazon in both science and philosophy. Kurzweil speaks widely to audiences both public and private and regularly delivers keynote speeches at industry conferences like DEMO, SXSW and TED. He maintains the news website KurzweilAI.net, which has over three million readers annually

Views on the future of genetics, nanotechnology and robotics

Kurzweil is working with the Army Science Board to develop a rapid response system to deal with the possible abuse of biotechnology. He suggests that the same technologies that are empowering us to reprogram biology away from cancer and heart disease could be used by a bioterrorist to reprogram a biological virus to be more deadly, communicable, and stealthy. However, he suggests that we have the scientific tools to successfully defend against these attacks, similar to the way we defend against computer software viruses. He has testified before Congress on the subject of nanotechnology, advocating that nanotechnology has the potential to solve serious global problems such as poverty, disease, and climate change. "Nanotech Could Give Global Warming a Big Chill".

In media appearances, Kurzweil has stressed the extreme potential dangers of nanotechnology but argues that in practice, progress cannot be stopped because that would require a totalitarian system, and any attempt to do so would drive dangerous technologies underground and deprive responsible scientists of the tools needed for defense. He suggests that the proper place of regulation is to ensure that technological progress proceeds safely and quickly, but does not deprive the world of profound benefits. He stated, "To avoid dangers such as unrestrained nanobot replication, we need relinquishment at the right level and to place our highest priority on the continuing advance of defensive technologies, staying ahead of destructive technologies. An overall strategy should include a streamlined regulatory process, a global program of monitoring for unknown or evolving biological pathogens, temporary moratoriums, raising public awareness, international cooperation, software reconnaissance, and fostering values of liberty, tolerance, and respect for knowledge and diversity."

Kurzweil’s View of the Human NeoCortex

According to Kurzweil, technologists will be creating synthetic neocortexes based on the operating principles of the human neocortex with the primary purpose of extending our own neocortexes. He claims to believe that the neocortex of an adult human consists of approximately 300 million pattern recognizers. He draws on the commonly accepted belief that the primary anatomical difference between humans and other primates that allowed for superior intellectual abilities was the evolution of a larger neocortex. He claims that the six-layered neocortex deals with increasing abstraction from one layer to the next. He says that at the low levels, the neocortex may seem cold and mechanical because it can only make simple decisions, but at the higher levels of the hierarchy, the neocortex is likely to be dealing with concepts like being funny, being sexy, expressing a loving sentiment, creating a poem or understanding a poem, etc. He claims to believe that these higher levels of the human neocortex were the enabling factors to permit the human development of language, technology, art, and science. He stated, "If the quantitative improvement from primates to humans with the big forehead was the enabling factor to allow for language, technology, art, and science, what kind of qualitative leap can we make with another quantitative increase? Why not go from 300 million pattern recognizers to a billion?”

Future Predictions

In 1999, Kurzweil published a second book titled The Age of Spiritual Machines, which goes into more depth explaining his futurist ideas. The third and final part of the book is devoted to predictions over the coming century, from 2009 through 2099. In The Singularity Is Near he makes fewer concrete short-term predictions, but includes many longer-term visions.

He states that with radical life extension will come radical life enhancement. He says he is confident that within 10 years we will have the option to spend some of our time in 3D virtual environments that appear just as real as real reality, but these will not yet be made possible via direct interaction with our nervous system. "If you look at video games and how we went from pong to the virtual reality we have available today, it is highly likely that immortality in essence will be possible." He believes that 20 to 25 years from now, we will have millions of blood-cell sized devices, known as nanobots, inside our bodies fighting against diseases, improving our memory, and cognitive abilities. Kurzweil says that a machine will pass the Turing test by 2029, and that around 2045, "the pace of change will be so astonishingly quick that we won't be able to keep up, unless we enhance our own intelligence by merging with the intelligent machines we are creating". Kurzweil states that humans will be a hybrid of biological and non-biological intelligence that becomes increasingly dominated by its non-biological component. He stresses that "AI is not an intelligent invasion from Mars. These are brain extenders that we have created to expand our own mental reach. They are part of our civilization. They are part of who we are. So over the next few decades our human-machine civilization will become increasingly dominated by its non-biological component. In Transcendent Man Kurzweil states "We humans are going to start linking with each other and become a metaconnection we will all be connected and all be omnipresent, plugged into this global network that is connected to billions of people, and filled with data." Kurzweil states in a press conference that we are the only species that goes beyond our limitations- "we didn't stay in the caves, we didn't stay on the planet, and we're not going to stay with the limitations of our biology". In his singularity based documentary he is quoted saying "I think people are fooling themselves when they say they have accepted death".

In 2008, Kurzweil said in an expert panel in the National Academy of Engineering that solar power will scale up to produce all the energy needs of Earth's people in 20 years. According to Kurzweil, we only need to capture 1 part in 10,000 of the energy from the Sun that hits Earth's surface to meet all of humanity's energy needs

                               https://en.wikipedia.org/wiki/Ray_Kurzweil

Basics of Geriatric Psychology

Geriatric psychology is a subfield of psychology that specializes in the mental and physical health of those in the later stage of life. These specialized psychologists study a variety of psychological abilities that deplete as aging occurs such as memory, learning capabilities, and coordination. Geriatric psychologists work with elderly clients to conduct the diagnosis, study, and treatment of certain mental illnesses in a variety of workplace settings. Geriatric psychology is a competitive, but growing field, and interested individuals must complete many years of schooling, including a Ph.D. program, an internship, and postdoctoral fellowships.

Role of the Geriatric Psychologist

A geriatric psychologist specializes in the treatment of the elderly. This treatment can include mental health problems or aid in the understanding and coping with the aging process. In order to become a geriatric psychologist, one must obtain a doctoral degree in psychology and specialize in adult development and aging. Once the degree is obtained, geriatric psychologists will usually work in hospitals, mental health clinics, rehab centers, nursing homes, and research centers. Most geriatric psychologists work in medical facilities, however. Geriatric psychologists spend most of their work day addressing mental health issues in older adults and counseling those that need it. They also aid in the diagnosis of age-related problems. In order to check for mental health issues, geriatric psychologists will conduct clinical interview, neuropsychological tests and behavioral observations.

Psychology versus Psychiatry

Geriatric psychology is based upon the study of mental functions with aging. The psychologist's purpose is to observe how the neurological and physiological process of an elderly adults brain affects their behaviors in daily life. When a psychologist is specialized in geriatrics they provide care for declining brain functions to make life better. Geriatric psychiatry is more about the diagnosis and treatment of certain mental disorders such as the ones listed below. A geriatric psychiatrist is also a licensed doctor that can prescribe medications for elderly patients. To become a psychiatrist you must attend and graduate from medical school.

History of Geriatric Psychology

It all began in 1978, there was a spike in interest in mental health and the effects of aging. There was slowly an increase in the number of aging adults in the U.S. population. There was a small group of 11 people who met together to talk about late-life mental health needs and the field of geriatrics. This meeting later created the American Association of Geriatric Psychology (AAGP). As time has gone on the small group has turned into a very large group of people dedicated to the well being of the aging population.

Research Areas

Loneliness in elderly people

It is important to study the phenomenon of loneliness in the aging community, as many older individuals face a variety of problems including physical illness when faced with this common problem. It is important for nurses and other individuals who work in association with the elderly to understand the various theories of loneliness, as they may need to implement these perspectives into enhancing the lives of their patients.

Depression

Depression in the elderly community can have severe consequences, which is why it is an important public health problem and research area. Older adults facing this debilitating condition are less likely to endorse affective symptoms and are more likely to instead display cognitive changes, somatic symptoms, and loss of interest than are younger adults. It is co-morbid with “morbidity, increased risk of suicide, decreased physical, cognitive and social functioning, and greater self-neglect”, all of which are associated with an increase in mortality.

Dementia

Dementia is a variety of symptoms that apply to a decline in mental ability, not a specific disease. Geriatric psychologists work with dementia by aiding in the diagnosis of this disease. This is done through various cognitive tests and assessments. They will also look at research and potential treatment for dementia.

Alzheimer's disease

Alzheimer's disease is the most common type of dementia, accounting for 60-80 percent of dementia cases. Similar to dementia, a geriatric psychologist's role regarding Alzheimer's disease is the assessment, treatment, and research of the disease.

Vascular dementia

Vascular dementia, the second most common type of dementia, is the result of a stroke. A geriatric psychologist aids in the assessment, diagnosing, treatment, and research of vascular dementia.

Parkinson's disease

Parkinson’s disease is a movement disorder that has symptoms like tremors, slowing down, stiffness, and impaired balance. A geriatric psychologist's role for those with Parkinson's disease would be helping the person diagnosed deal with the stress they may encounter regarding Parkinson's Disease. Since it is not a brain disorder, the geriatric psychologist would not help with diagnosing or the treatment of the disease.

                             https://en.wikipedia.org/wiki/Geriatric_psychology

"Cascading Failure" of Systems

A cascading failure is a failure in a system of interconnected parts in which the failure of a part can trigger the failure of successive parts. Such a failure may happen in many types of systems, including power transmission, computer networking, finance, human bodily systems, and bridges.

Cascading failures usually begin when one part of the system fails. When this happens, nearby nodes must then compensate for the failed component. This in turn overloads these nodes, causing them to fail as well, prompting additional nodes to fail one after another in a vicious circle.

Cascading Failure in Power Transmission

Cascading failure is common in power grids when one of the elements fails (completely or partially) and shifts its load to nearby elements in the system. Those nearby elements are then pushed beyond their capacity so they become overloaded and shift their load onto other elements. Cascading failure is a common effect seen in high voltage systems, where a single point of failure (SPF) on a fully loaded or slightly overloaded system results in a sudden spike across all nodes of the system. This surge current can induce the already overloaded nodes into failure, setting off more overloads and thereby taking down the entire system in a very short time.

This failure process cascades through the elements of the system like a ripple on a pond and continues until substantially all of the elements in the system are compromised and/or the system becomes functionally disconnected from the source of its load. For example, under certain conditions a large power grid can collapse after the failure of a single transformer.

Monitoring the operation of a system, in real-time, and judicious disconnection of parts can help stop a cascade. Another common technique is to calculate a safety margin for the system by computer simulation of possible failures, to establish safe operating levels below which none of the calculated scenarios is predicted to cause cascading failure, and to identify the parts of the network which are most likely to cause cascading failures.  One of the primary problems with preventing electrical grid failures is that the speed of the control signal is no faster than the speed of the propagating power overload, i.e. since both the control signal and the electrical power are moving at the same speed, it is not possible to isolate the outage by sending a warning ahead to isolate the element.

Cascading Failure in Computer Networks

Cascading failures can also occur in computer networks (such as the Internet) in which network traffic is severely impaired or halted to or between larger sections of the network, caused by failing or disconnected hardware or software. In this context, the cascading failure is known by the term cascade failure. A cascade failure can affect large groups of people and systems.

The cause of a cascade failure is usually the overloading of a single, crucial router or node, which causes the node to go down, even briefly. It can also be caused by taking a node down for maintenance or upgrades. In either case, traffic is routed to or through another (alternative) path. This alternative path, as a result, becomes overloaded, causing it to go down, and so on. It will also affect systems which depend on the node for regular operation.

Symptoms

The symptoms of a cascade failure include: packet loss and high network latency, not just to single systems, but to whole sections of a network or the internet. The high latency and packet loss is caused by the nodes that fail to operate due to congestion collapse, which causes them to still be present in the network but without much or any useful communication going through them. As a result, routes can still be considered valid, without them actually providing communication.

If enough routes go down because of a cascade failure, a complete section of the network or internet can become unreachable. Although undesired, this can help speed up the recovery from this failure as connections will time out, and other nodes will give up trying to establish connections to the section(s) that have become cut off, decreasing load on the involved nodes.

A common occurrence during a cascade failure is a walking failure, where sections go down, causing the next section to fail, after which the first section comes back up. This ripple can make several passes through the same sections or connecting nodes before stability is restored.

History

Cascade failures are a relatively recent development, with the massive increase in traffic and the high interconnectivity between systems and networks. The term was first applied in this context in the late 1990s by a Dutch IT professional and has slowly become a relatively common term for this kind of large-scale failure.

Example

Network failures typically start when a single network node fails. Initially, the traffic that would normally go through the node is stopped. Systems and users get errors about not being able to reach hosts. Usually, the redundant systems of an ISP respond very quickly, choosing another path through a different backbone. The routing path through this alternative route is longer, with more hops and subsequently going through more systems that normally do not process the amount of traffic suddenly offered.

This can cause one or more systems along the alternative route to go down, creating similar problems of their own.

Also, related systems are affected in this case. As an example, DNS resolution might fail and what would normally cause systems to be interconnected, might break connections that are not even directly involved in the actual systems that went down. This, in turn, may cause seemingly unrelated nodes to develop problems, that can cause another cascade failure all on its own.

In December 2012, a partial loss (40%) of GMail service occurred globally, for 18 minutes. This loss of service was caused by a routine update of load balancing software which contained faulty logic—in this case, the error was caused by logic using an inappropriate all instead of the more appropriate some.  The cascading error was fixed by fully updating a single node in the network instead of partially updating all nodes at one time.

Cascading Structural Failure

Certain load-bearing structures with discrete structural components can be subject to the "zipper effect", where the failure of a single structural member increases the load on adjacent members. In the case of the Hyatt Regency walkway collapse, a suspended walkway (which was already overstressed due to an error in construction) failed when a single vertical suspension rod failed, overloading the neighboring rods which failed sequentially (i.e. like a zipper). A bridge that can have such a failure is called fracture critical, and numerous bridge collapses have been caused by the failure of a single part. Properly designed structures use an adequate factor of safety and/or alternate load paths to prevent this type of mechanical cascade failure.

                           https://en.wikipedia.org/wiki/Cascading_failure