Explainable artificial intelligence

From Wikipedia, the free encyclopedia

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

Explainable AI (XAI), also known as Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the reasoning behind decisions or predictions made by the AI. It contrasts with the "black box" concept in machine learning, where even the AI's designers cannot explain why it arrived at a specific decision.

XAI hopes to help users of AI-powered systems perform more effectively by improving their understanding of how those systems reason. XAI may be an implementation of the social right to explanation. Even if there is no such legal right or regulatory requirement, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. XAI aims to explain what has been done, what is being done, and what will be done next, and to unveil which information these actions are based on. This makes it possible to confirm existing knowledge, challenge existing knowledge, and generate new assumptions.

Machine learning (ML) algorithms used in AI can be categorized as white-box or black-box. White-box models provide results that are understandable to experts in the domain. Black-box models, on the other hand, are extremely hard to explain and can hardly be understood even by domain experts. XAI algorithms follow the three principles of transparency, interpretability, and explainability. A model is transparent “if the processes that extract model parameters from training data and generate labels from testing data can be described and motivated by the approach designer.” Interpretability describes the possibility of comprehending the ML model and presenting the underlying basis for decision-making in a way that is understandable to humans. Explainability is a concept that is recognized as important, but a consensus definition is not available. One possibility is “the collection of features of the interpretable domain that have contributed, for a given example, to producing a decision (e.g., classification or regression)”. If algorithms fulfill these principles, they provide a basis for justifying decisions, tracking them and thereby verifying them, improving the algorithms, and exploring new facts.

Sometimes it is also possible to achieve a high-accuracy result with a white-box ML algorithm that is interpretable in itself. This is especially important in domains like medicine, defense, finance, and law, where it is crucial to understand decisions and build trust in the algorithms. Many researchers argue that, at least for supervised machine learning, the way forward is symbolic regression, where the algorithm searches the space of mathematical expressions to find the model that best fits a given dataset.

AI systems optimize behavior to satisfy a mathematically specified goal system chosen by the system designers, such as the command "maximize accuracy of assessing how positive film reviews are in the test dataset." The AI may learn useful general rules from the test set, such as "reviews containing the word "horrible" are likely to be negative." However, it may also learn inappropriate rules, such as "reviews containing '"Daniel Day-Lewis" are usually positive"; such rules may be undesirable if they are likely to fail to generalize outside the training set, or if people consider the rule to be "cheating" or "unfair." A human can audit rules in an XAI to get an idea of how likely the system is to generalize to future real-world data outside the test set.

Goals

Cooperation between agents --- in this case, algorithms and humans—depends on trust. If humans are to accept algorithmic prescriptions, they need to trust them. Incompleteness in the formalization of trust criteria is a barrier to straightforward optimization approaches. Transparency, interpretability, and explainability are intermediate goals on the road to these more comprehensive trust criteria. This is particularly relevant in medicine, especially with clinical decision support systems (CDSS), in which medical professionals should be able to understand how and why a machine-based decision was made in order to trust the decision and augment their decision-making process.

AI systems sometimes learn undesirable tricks that do an optimal job of satisfying explicit pre-programmed goals on the training data but do not reflect the more nuanced implicit desires of the human system designers or the full complexity of the domain data. For example, a 2017 system tasked with image recognition learned to "cheat" by looking for a copyright tag that happened to be associated with horse pictures rather than learning how to tell if a horse was actually pictured. In another 2017 system, a supervised learning AI tasked with grasping items in a virtual world learned to cheat by placing its manipulator between the object and the viewer in a way such that it falsely appeared to be grasping the object.

One transparency project, the DARPA XAI program, aims to produce "glass box" models that are explainable to a "human-in-the-loop" without greatly sacrificing AI performance. Human users of such a system can understand the AI's cognition (both in real-time and after the fact) and can determine whether to trust the AI. Other applications of XAI are knowledge extraction from black-box models and model comparisons. In the context of monitoring systems for ethical and socio-legal compliance, the term "glass box" is commonly used to refer to tools that track the inputs and outputs of the system in question, and provide value-based explanations for their behavior. These tools aim to ensure that the system operates in accordance with ethical and legal standards, and that its decision-making processes are transparent and accountable. The term "glass box" is often used in contrast to "black box" systems, which lack transparency and can be more difficult to monitor and regulate. The term is also used to name a voice assistant that produces counterfactual statements as explanations.

History and methods

During the 1970s to 1990s, symbolic reasoning systems, such as MYCIN, GUIDON, SOPHIE, and PROTOS could represent, reason about, and explain their reasoning for diagnostic, instructional, or machine-learning (explanation-based learning) purposes. MYCIN, developed in the early 1970s as a research prototype for diagnosing bacteremia infections of the bloodstream, could explain which of its hand-coded rules contributed to a diagnosis in a specific case. Research in intelligent tutoring systems resulted in developing systems such as SOPHIE that could act as an "articulate expert", explaining problem-solving strategy at a level the student could understand, so they would know what action to take next. For instance, SOPHIE could explain the qualitative reasoning behind its electronics troubleshooting, even though it ultimately relied on the SPICE circuit simulator. Similarly, GUIDON added tutorial rules to supplement MYCIN's domain-level rules so it could explain strategy for medical diagnosis. Symbolic approaches to machine learning, especially those relying on explanation-based learning, such as PROTOS, explicitly relied on representations of explanations, both to explain their actions and to acquire new knowledge.

In the 1980s through early 1990s, truth maintenance systems (TMS) extended the capabilities of causal-reasoning, rule-based, and logic-based inference systems. A TMS explicitly tracks alternate lines of reasoning, justifications for conclusions, and lines of reasoning that lead to contradictions, allowing future reasoning to avoid these dead ends. To provide explanation, they trace reasoning from conclusions to assumptions through rule operations or logical inferences, allowing explanations to be generated from the reasoning traces. As an example, consider a rule-based problem solver with just a few rules about Socrates that concludes he has died from poison:

By just tracing through the dependency structure the problem solver can construct the following explanation: "Socrates died because he was mortal and drank poison, and all mortals die when they drink poison. Socrates was mortal because he was a man and all men are mortal. Socrates drank poison because he held dissident beliefs, the government was conservative, and those holding conservative dissident beliefs under conservative governments must drink poison."

By the 1990s researchers began studying whether it is possible to meaningfully extract the non-hand-coded rules being generated by opaque trained neural networks. Researchers in clinical expert systems creating neural network-powered decision support for clinicians sought to develop dynamic explanations that allow these technologies to be more trusted and trustworthy in practice. In the 2010s public concerns about racial and other bias in the use of AI for criminal sentencing decisions and findings of creditworthiness may have led to increased demand for transparent artificial intelligence. As a result, many academics and organizations are developing tools to help detect bias in their systems.

Marvin Minsky et al. raised the issue that AI can function as a form of surveillance, with the biases inherent in surveillance, suggesting HI (Humanistic Intelligence) as a way to create a more fair and balanced "human-in-the-loop" AI.

Modern complex AI techniques, such as deep learning and genetic algorithms, are naturally opaque. To address this issue, methods have been developed to make new models more explainable and interpretable. This includes layerwise relevance propagation (LRP), a technique for determining which features in a particular input vector contribute most strongly to a neural network's output. Other techniques explain some particular prediction made by a (nonlinear) black-box model, a goal referred to as "local interpretability". The mere transposition of the concepts of local interpretability into a remote context (where the black-box model is executed at a third party) is currently under scrutiny.

There has been work on making glass-box models which are more transparent to inspection. This includes decision trees, Bayesian networks, sparse linear models, and more. The Association for Computing Machinery Conference on Fairness, Accountability, and Transparency (ACM FAccT) was established in 2018 to study transparency and explainability in the context of socio-technical systems, many of which include artificial intelligence.

Some techniques allow visualisations of the inputs which individual software neurons respond to most strongly. Several groups found that neurons can be aggregated into circuits that perform human-comprehensible functions, some of which reliably arise across different networks trained independently.

There are various techniques to extract compressed representations of the features of given inputs, which can then be analysed by standard clustering techniques. Alternatively, networks can be trained to output linguistic explanations of their behaviour, which are then directly human-interpretable. Model behaviour can also be explained with reference to training data—for example, by evaluating which training inputs influenced a given behaviour the most.

Regulation

As regulators, official bodies, and general users come to depend on AI-based dynamic systems, clearer accountability will be required for automated decision-making processes to ensure trust and transparency. The first global conference exclusively dedicated to this emerging discipline was the 2017 International Joint Conference on Artificial Intelligence: Workshop on Explainable Artificial Intelligence (XAI).

The European Union introduced a right to explanation in the General Data Protection Right (GDPR) to address potential problems stemming from the rising importance of algorithms. The implementation of the regulation began in 2018. However, the right to explanation in GDPR covers only the local aspect of interpretability. In the United States, insurance companies are required to be able to explain their rate and coverage decisions. In France the Loi pour une République numérique (Digital Republic Act) grants subjects the right to request and receive information pertaining to the implementation of algorithms that process data about them.

Limitations

Despite efforts to increase the explainability of AI models, they still have a number of limitations.

Adversarial parties

By making an AI system more explainable, we also reveal more of its inner workings. For example, the explainability method of feature importance identifies features or variables that are most important in determining the model's output, while the influential samples method identifies the training samples that are most influential in determining the output, given a particular input. Adversarial parties could take advantage of this knowledge.

For example, competitor firms could replicate aspects of the original AI system in their own product, thus reducing competitive advantage. An explainable AI system is also susceptible to being “gamed”—influenced in a way that undermines its intended purpose. One study gives the example of a predictive policing system; in this case, those who could potentially “game” the system are the criminals subject to the system's decisions. In this study, developers of the system discussed the issue of criminal gangs looking to illegally obtain passports, and they expressed concerns that, if given an idea of what factors might trigger an alert in the passport application process, those gangs would be able to “send guinea pigs” to test those triggers, eventually finding a loophole that would allow them to “reliably get passports from under the noses of the authorities”.

Technical complexity

A fundamental barrier to making AI systems explainable is the technical complexity of such systems. End users often lack the coding knowledge required to understand software of any kind. Current methods used to explain AI are mainly technical ones, geared toward machine learning engineers for debugging purposes, rather than toward the end users who are ultimately affected by the system, causing “a gap between explainability in practice and the goal of transparency”. Proposed solutions to address the issue of technical complexity include either promoting the coding education of the general public so technical explanations would be more accessible to end users, or providing explanations in layperson terms.

The solution must avoid oversimplification. It is important to strike a balance between accuracy – how faithfully the explanation reflects the process of the AI system – and explainability – how well end users understand the process. This is a difficult balance to strike, since the complexity of machine learning makes it difficult for even ML engineers to fully understand, let alone non-experts.

Understanding versus trust

The goal of explainability to end users of AI systems is to increase trust in the systems, even “address concerns about lack of ‘fairness’ and discriminatory effects”. However, even with a good understanding of an AI system, end users may not necessarily trust the system. In one study, participants were presented with combinations of white-box and black-box explanations, and static and interactive explanations of AI systems. While these explanations served to increase both their self-reported and objective understanding, it had no impact on their level of trust, which remained skeptical.

This outcome was especially true for decisions that impacted the end user in a significant way, such as graduate school admissions. Participants judged algorithms to be too inflexible and unforgiving in comparison to human decision-makers; instead of rigidly adhering to a set of rules, humans are able to consider exceptional cases as well as appeals to their initial decision. For such decisions, explainability will not necessarily cause end users to accept the use of decision-making algorithms. We will need to either turn to another method to increase trust and acceptance of decision-making algorithms, or question the need to rely solely on AI for such impactful decisions in the first place.

Criticism

Scholars have suggested that explainability in AI should be considered a goal secondary to AI effectiveness, and that encouraging the exclusive development of XAI may limit the functionality of AI more broadly. Critiques of XAI rely on developed concepts of mechanistic and empiric reasoning from evidence-based medicine to suggest that AI technologies can be clinically validated even when their function cannot be understood by their operators.

Moreover, XAI systems have primarily focused on making AI systems understandable to AI practitioners rather than end users, and their results on user perceptions of these systems have been somewhat fragmented. Some researchers advocate the use of inherently interpretable machine learning models, rather than using post-hoc explanations in which a second model is created to explain the first. This is partly because post-hoc models increase the complexity in a decision pathway and partly because it is often unclear how faithfully a post-hoc explanation can mimic the computations of an entirely separate model. However, another view is that what is important is that the explanation accomplishes the given task at hand, and whether it is pre or post-hoc doesn't matter. If a post-hoc explanation method helps a doctor diagnose cancer better, it is of secondary importance whether it is a correct/incorrect explanation.

The goals of XAI amount to a form of lossy compression that will become less effective as AI models grow in their number of parameters. Along with other factors this leads to a theoretical limit for explainability.

Supercomputer

From Wikipedia, the free encyclopedia

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

 

The IBM Blue Gene/P supercomputer "Intrepid" at Argonne National Laboratory runs 164,000 processor cores using normal data center air conditioning, grouped in 40 racks/cabinets connected by a high-speed 3D torus network.

 

Computing power of the top 1 supercomputer each year, measured in FLOPS

A supercomputer is a computer with a high level of performance as compared to a general-purpose computer. The performance of a supercomputer is commonly measured in floating-point operations per second (FLOPS) instead of million instructions per second (MIPS). Since 2017, there have existed supercomputers which can perform over 10¹⁷ FLOPS (a hundred quadrillion FLOPS, 100 petaFLOPS or 100 PFLOPS). For comparison, a desktop computer has performance in the range of hundreds of gigaFLOPS (10¹¹) to tens of teraFLOPS (10¹³). Since November 2017, all of the world's fastest 500 supercomputers run on Linux-based operating systems. Additional research is being conducted in the United States, the European Union, Taiwan, Japan, and China to build faster, more powerful and technologically superior exascale supercomputers.

Supercomputers play an important role in the field of computational science, and are used for a wide range of computationally intensive tasks in various fields, including quantum mechanics, weather forecasting, climate research, oil and gas exploration, molecular modeling (computing the structures and properties of chemical compounds, biological macromolecules, polymers, and crystals), and physical simulations (such as simulations of the early moments of the universe, airplane and spacecraft aerodynamics, the detonation of nuclear weapons, and nuclear fusion). They have been essential in the field of cryptanalysis.

Supercomputers were introduced in the 1960s, and for several decades the fastest were made by Seymour Cray at Control Data Corporation (CDC), Cray Research and subsequent companies bearing his name or monogram. The first such machines were highly tuned conventional designs that ran more quickly than their more general-purpose contemporaries. Through the decade, increasing amounts of parallelism were added, with one to four processors being typical. In the 1970s, vector processors operating on large arrays of data came to dominate. A notable example is the highly successful Cray-1 of 1976. Vector computers remained the dominant design into the 1990s. From then until today, massively parallel supercomputers with tens of thousands of off-the-shelf processors became the norm.

The US has long been the leader in the supercomputer field, first through Cray's almost uninterrupted dominance of the field, and later through a variety of technology companies. Japan made major strides in the field in the 1980s and 90s, with China becoming increasingly active in the field. As of May 2022, the fastest supercomputer on the TOP500 supercomputer list is Frontier, in the US, with a LINPACK benchmark score of 1.102 ExaFlop/s, followed by Fugaku. The US has five of the top 10; China has two; Japan, Finland, and France have one each. In June 2018, all combined supercomputers on the TOP500 list broke the 1 exaFLOPS mark.

History

Main article: History of supercomputing

 

A circuit board from the IBM 7030

 

The CDC 6600. Behind the system console are two of the "arms" of the plus-sign shaped cabinet with the covers opened. Each arm of the machine had up to four such racks. On the right is the cooling system.

 

A Cray-1 preserved at the Deutsches Museum

In 1960, UNIVAC built the Livermore Atomic Research Computer (LARC), today considered among the first supercomputers, for the US Navy Research and Development Center. It still used high-speed drum memory, rather than the newly emerging disk drive technology. Also, among the first supercomputers was the IBM 7030 Stretch. The IBM 7030 was built by IBM for the Los Alamos National Laboratory, which in 1955 had requested a computer 100 times faster than any existing computer. The IBM 7030 used transistors, magnetic core memory, pipelined instructions, prefetched data through a memory controller and included pioneering random access disk drives. The IBM 7030 was completed in 1961 and despite not meeting the challenge of a hundredfold increase in performance, it was purchased by the Los Alamos National Laboratory. Customers in England and France also bought the computer, and it became the basis for the IBM 7950 Harvest, a supercomputer built for cryptanalysis.

The third pioneering supercomputer project in the early 1960s was the Atlas at the University of Manchester, built by a team led by Tom Kilburn. He designed the Atlas to have memory space for up to a million words of 48 bits, but because magnetic storage with such a capacity was unaffordable, the actual core memory of the Atlas was only 16,000 words, with a drum providing memory for a further 96,000 words. The Atlas operating system swapped data in the form of pages between the magnetic core and the drum. The Atlas operating system also introduced time-sharing to supercomputing, so that more than one program could be executed on the supercomputer at any one time. Atlas was a joint venture between Ferranti and the Manchester University and was designed to operate at processing speeds approaching one microsecond per instruction, about one million instructions per second.

The CDC 6600, designed by Seymour Cray, was finished in 1964 and marked the transition from germanium to silicon transistors. Silicon transistors could run more quickly and the overheating problem was solved by introducing refrigeration to the supercomputer design. Thus, the CDC6600 became the fastest computer in the world. Given that the 6600 outperformed all the other contemporary computers by about 10 times, it was dubbed a supercomputer and defined the supercomputing market, when one hundred computers were sold at $8 million each.

Cray left CDC in 1972 to form his own company, Cray Research. Four years after leaving CDC, Cray delivered the 80 MHz Cray-1 in 1976, which became one of the most successful supercomputers in history. The Cray-2 was released in 1985. It had eight central processing units (CPUs), liquid cooling and the electronics coolant liquid Fluorinert was pumped through the supercomputer architecture. It reached 1.9 gigaFLOPS, making it the first supercomputer to break the gigaflop barrier.

Massively parallel designs

Main articles: Supercomputer architecture and Parallel computer hardware

 

A cabinet of the massively parallel Blue Gene/L, showing the stacked blades, each holding many processors

The only computer to seriously challenge the Cray-1's performance in the 1970s was the ILLIAC IV. This machine was the first realized example of a true massively parallel computer, in which many processors worked together to solve different parts of a single larger problem. In contrast with the vector systems, which were designed to run a single stream of data as quickly as possible, in this concept, the computer instead feeds separate parts of the data to entirely different processors and then recombines the results. The ILLIAC's design was finalized in 1966 with 256 processors and offer speed up to 1 GFLOPS, compared to the 1970s Cray-1's peak of 250 MFLOPS. However, development problems led to only 64 processors being built, and the system could never operate more quickly than about 200 MFLOPS while being much larger and more complex than the Cray. Another problem was that writing software for the system was difficult, and getting peak performance from it was a matter of serious effort.

But the partial success of the ILLIAC IV was widely seen as pointing the way to the future of supercomputing. Cray argued against this, famously quipping that "If you were plowing a field, which would you rather use? Two strong oxen or 1024 chickens?" But by the early 1980s, several teams were working on parallel designs with thousands of processors, notably the Connection Machine (CM) that developed from research at MIT. The CM-1 used as many as 65,536 simplified custom microprocessors connected together in a network to share data. Several updated versions followed; the CM-5 supercomputer is a massively parallel processing computer capable of many billions of arithmetic operations per second.

In 1982, Osaka University's LINKS-1 Computer Graphics System used a massively parallel processing architecture, with 514 microprocessors, including 257 Zilog Z8001 control processors and 257 iAPX 86/20 floating-point processors. It was mainly used for rendering realistic 3D computer graphics. Fujitsu's VPP500 from 1992 is unusual since, to achieve higher speeds, its processors used GaAs, a material normally reserved for microwave applications due to its toxicity. Fujitsu's Numerical Wind Tunnel supercomputer used 166 vector processors to gain the top spot in 1994 with a peak speed of 1.7 gigaFLOPS (GFLOPS) per processor. The Hitachi SR2201 obtained a peak performance of 600 GFLOPS in 1996 by using 2048 processors connected via a fast three-dimensional crossbar network. The Intel Paragon could have 1000 to 4000 Intel i860 processors in various configurations and was ranked the fastest in the world in 1993. The Paragon was a MIMD machine which connected processors via a high speed two-dimensional mesh, allowing processes to execute on separate nodes, communicating via the Message Passing Interface.

Software development remained a problem, but the CM series sparked off considerable research into this issue. Similar designs using custom hardware were made by many companies, including the Evans & Sutherland ES-1, MasPar, nCUBE, Intel iPSC and the Goodyear MPP. But by the mid-1990s, general-purpose CPU performance had improved so much in that a supercomputer could be built using them as the individual processing units, instead of using custom chips. By the turn of the 21st century, designs featuring tens of thousands of commodity CPUs were the norm, with later machines adding graphic units to the mix.

The CPU share of TOP500

 

Diagram of a three-dimensional torus interconnect used by systems such as Blue Gene, Cray XT3, etc.

Systems with a massive number of processors generally take one of two paths. In the grid computing approach, the processing power of many computers, organized as distributed, diverse administrative domains, is opportunistically used whenever a computer is available. In another approach, many processors are used in proximity to each other, e.g. in a computer cluster. In such a centralized massively parallel system the speed and flexibility of the interconnect becomes very important and modern supercomputers have used various approaches ranging from enhanced Infiniband systems to three-dimensional torus interconnects. The use of multi-core processors combined with centralization is an emerging direction, e.g. as in the Cyclops64 system.

As the price, performance and energy efficiency of general-purpose graphics processing units (GPGPUs) have improved, a number of petaFLOPS supercomputers such as Tianhe-I and Nebulae have started to rely on them. However, other systems such as the K computer continue to use conventional processors such as SPARC-based designs and the overall applicability of GPGPUs in general-purpose high-performance computing applications has been the subject of debate, in that while a GPGPU may be tuned to score well on specific benchmarks, its overall applicability to everyday algorithms may be limited unless significant effort is spent to tune the application to it. However, GPUs are gaining ground, and in 2012 the Jaguar supercomputer was transformed into Titan by retrofitting CPUs with GPUs.

High-performance computers have an expected life cycle of about three years before requiring an upgrade. The Gyoukou supercomputer is unique in that it uses both a massively parallel design and liquid immersion cooling.

Special purpose supercomputers

A number of special-purpose systems have been designed, dedicated to a single problem. This allows the use of specially programmed FPGA chips or even custom ASICs, allowing better price/performance ratios by sacrificing generality. Examples of special-purpose supercomputers include Belle, Deep Blue, and Hydra for playing chess, Gravity Pipe for astrophysics, MDGRAPE-3 for protein structure prediction and molecular dynamics, and Deep Crack for breaking the DES cipher.

Energy usage and heat management

See also: Computer cooling and Green500

 

The Summit supercomputer was as of November 2018 the fastest supercomputer in the world. With a measured power efficiency of 14.668 GFlops/watt it is also the third most energy efficient in the world.

Throughout the decades, the management of heat density has remained a key issue for most centralized supercomputers. The large amount of heat generated by a system may also have other effects, e.g. reducing the lifetime of other system components. There have been diverse approaches to heat management, from pumping Fluorinert through the system, to a hybrid liquid-air cooling system or air cooling with normal air conditioning temperatures. A typical supercomputer consumes large amounts of electrical power, almost all of which is converted into heat, requiring cooling. For example, Tianhe-1A consumes 4.04 megawatts (MW) of electricity. The cost to power and cool the system can be significant, e.g. 4 MW at $0.10/kWh is $400 an hour or about $3.5 million per year.

An IBM HS20 blade

Heat management is a major issue in complex electronic devices and affects powerful computer systems in various ways. The thermal design power and CPU power dissipation issues in supercomputing surpass those of traditional computer cooling technologies. The supercomputing awards for green computing reflect this issue.

The packing of thousands of processors together inevitably generates significant amounts of heat density that need to be dealt with. The Cray-2 was liquid cooled, and used a Fluorinert "cooling waterfall" which was forced through the modules under pressure. However, the submerged liquid cooling approach was not practical for the multi-cabinet systems based on off-the-shelf processors, and in System X a special cooling system that combined air conditioning with liquid cooling was developed in conjunction with the Liebert company.

In the Blue Gene system, IBM deliberately used low power processors to deal with heat density. The IBM Power 775, released in 2011, has closely packed elements that require water cooling. The IBM Aquasar system uses hot water cooling to achieve energy efficiency, the water being used to heat buildings as well.

The energy efficiency of computer systems is generally measured in terms of "FLOPS per watt". In 2008, Roadrunner by IBM operated at 3.76 MFLOPS/W. In November 2010, the Blue Gene/Q reached 1,684 MFLOPS/W and in June 2011 the top two spots on the Green 500 list were occupied by Blue Gene machines in New York (one achieving 2097 MFLOPS/W) with the DEGIMA cluster in Nagasaki placing third with 1375 MFLOPS/W.

Because copper wires can transfer energy into a supercomputer with much higher power densities than forced air or circulating refrigerants can remove waste heat, the ability of the cooling systems to remove waste heat is a limiting factor. As of 2015, many existing supercomputers have more infrastructure capacity than the actual peak demand of the machine – designers generally conservatively design the power and cooling infrastructure to handle more than the theoretical peak electrical power consumed by the supercomputer. Designs for future supercomputers are power-limited – the thermal design power of the supercomputer as a whole, the amount that the power and cooling infrastructure can handle, is somewhat more than the expected normal power consumption, but less than the theoretical peak power consumption of the electronic hardware.

Software and system management

Operating systems

Main article: Supercomputer operating systems

Since the end of the 20th century, supercomputer operating systems have undergone major transformations, based on the changes in supercomputer architecture. While early operating systems were custom tailored to each supercomputer to gain speed, the trend has been to move away from in-house operating systems to the adaptation of generic software such as Linux.

Since modern massively parallel supercomputers typically separate computations from other services by using multiple types of nodes, they usually run different operating systems on different nodes, e.g. using a small and efficient lightweight kernel such as CNK or CNL on compute nodes, but a larger system such as a Linux-derivative on server and I/O nodes.

While in a traditional multi-user computer system job scheduling is, in effect, a tasking problem for processing and peripheral resources, in a massively parallel system, the job management system needs to manage the allocation of both computational and communication resources, as well as gracefully deal with inevitable hardware failures when tens of thousands of processors are present.

Although most modern supercomputers use Linux-based operating systems, each manufacturer has its own specific Linux-derivative, and no industry standard exists, partly due to the fact that the differences in hardware architectures require changes to optimize the operating system to each hardware design.

Software tools and message passing

Main article: Message passing in computer clusters

See also: Parallel computing and Parallel programming model

 

Wide-angle view of the ALMA correlator

The parallel architectures of supercomputers often dictate the use of special programming techniques to exploit their speed. Software tools for distributed processing include standard APIs such as MPI and PVM, VTL, and open source software such as Beowulf.

In the most common scenario, environments such as PVM and MPI for loosely connected clusters and OpenMP for tightly coordinated shared memory machines are used. Significant effort is required to optimize an algorithm for the interconnect characteristics of the machine it will be run on; the aim is to prevent any of the CPUs from wasting time waiting on data from other nodes. GPGPUs have hundreds of processor cores and are programmed using programming models such as CUDA or OpenCL.

Moreover, it is quite difficult to debug and test parallel programs. Special techniques need to be used for testing and debugging such applications.

Distributed supercomputing

Opportunistic approaches

Main article: Grid computing

 

Example architecture of a grid computing system connecting many personal computers over the internet

Opportunistic supercomputing is a form of networked grid computing whereby a "super virtual computer" of many loosely coupled volunteer computing machines performs very large computing tasks. Grid computing has been applied to a number of large-scale embarrassingly parallel problems that require supercomputing performance scales. However, basic grid and cloud computing approaches that rely on volunteer computing cannot handle traditional supercomputing tasks such as fluid dynamic simulations.

The fastest grid computing system is the volunteer computing project Folding@home (F@h). As of April 2020, F@h reported 2.5 exaFLOPS of x86 processing power. Of this, over 100 PFLOPS are contributed by clients running on various GPUs, and the rest from various CPU systems.

The Berkeley Open Infrastructure for Network Computing (BOINC) platform hosts a number of volunteer computing projects. As of February 2017, BOINC recorded a processing power of over 166 petaFLOPS through over 762 thousand active Computers (Hosts) on the network.

As of October 2016, Great Internet Mersenne Prime Search's (GIMPS) distributed Mersenne Prime search achieved about 0.313 PFLOPS through over 1.3 million computers. The [1] supports GIMPS's grid computing approach, one of the earliest volunteer computing projects, since 1997.

Quasi-opportunistic approaches

Main article: Quasi-opportunistic supercomputing

Quasi-opportunistic supercomputing is a form of distributed computing whereby the "super virtual computer" of many networked geographically disperse computers performs computing tasks that demand huge processing power. Quasi-opportunistic supercomputing aims to provide a higher quality of service than opportunistic grid computing by achieving more control over the assignment of tasks to distributed resources and the use of intelligence about the availability and reliability of individual systems within the supercomputing network. However, quasi-opportunistic distributed execution of demanding parallel computing software in grids should be achieved through implementation of grid-wise allocation agreements, co-allocation subsystems, communication topology-aware allocation mechanisms, fault tolerant message passing libraries and data pre-conditioning.

High-performance computing clouds

Cloud computing with its recent and rapid expansions and development have grabbed the attention of high-performance computing (HPC) users and developers in recent years. Cloud computing attempts to provide HPC-as-a-service exactly like other forms of services available in the cloud such as software as a service, platform as a service, and infrastructure as a service. HPC users may benefit from the cloud in different angles such as scalability, resources being on-demand, fast, and inexpensive. On the other hand, moving HPC applications have a set of challenges too. Good examples of such challenges are virtualization overhead in the cloud, multi-tenancy of resources, and network latency issues. Much research is currently being done to overcome these challenges and make HPC in the cloud a more realistic possibility.

In 2016, Penguin Computing, Parallel Works, R-HPC, Amazon Web Services, Univa, Silicon Graphics International, Rescale, Sabalcore, and Gomput started to offer HPC cloud computing. The Penguin On Demand (POD) cloud is a bare-metal compute model to execute code, but each user is given virtualized login node. POD computing nodes are connected via non-virtualized 10 Gbit/s Ethernet or QDR InfiniBand networks. User connectivity to the POD data center ranges from 50 Mbit/s to 1 Gbit/s. Citing Amazon's EC2 Elastic Compute Cloud, Penguin Computing argues that virtualization of compute nodes is not suitable for HPC. Penguin Computing has also criticized that HPC clouds may have allocated computing nodes to customers that are far apart, causing latency that impairs performance for some HPC applications.

Performance measurement

Capability versus capacity

Supercomputers generally aim for the maximum in capability computing rather than capacity computing. Capability computing is typically thought of as using the maximum computing power to solve a single large problem in the shortest amount of time. Often a capability system is able to solve a problem of a size or complexity that no other computer can, e.g. a very complex weather simulation application.

Capacity computing, in contrast, is typically thought of as using efficient cost-effective computing power to solve a few somewhat large problems or many small problems. Architectures that lend themselves to supporting many users for routine everyday tasks may have a lot of capacity but are not typically considered supercomputers, given that they do not solve a single very complex problem.

Performance metrics

See also: LINPACK benchmarks and Grid computing § Fastest virtual supercomputers

 

Top supercomputer speeds: logscale speed over 60 years

In general, the speed of supercomputers is measured and benchmarked in FLOPS (floating-point operations per second), and not in terms of MIPS (million instructions per second), as is the case with general-purpose computers. These measurements are commonly used with an SI prefix such as tera-, combined into the shorthand TFLOPS (10¹² FLOPS, pronounced teraflops), or peta-, combined into the shorthand PFLOPS (10¹⁵ FLOPS, pronounced petaflops.) Petascale supercomputers can process one quadrillion (10¹⁵) (1000 trillion) FLOPS. Exascale is computing performance in the exaFLOPS (EFLOPS) range. An EFLOPS is one quintillion (10¹⁸) FLOPS (one million TFLOPS).

No single number can reflect the overall performance of a computer system, yet the goal of the Linpack benchmark is to approximate how fast the computer solves numerical problems and it is widely used in the industry. The FLOPS measurement is either quoted based on the theoretical floating point performance of a processor (derived from manufacturer's processor specifications and shown as "Rpeak" in the TOP500 lists), which is generally unachievable when running real workloads, or the achievable throughput, derived from the LINPACK benchmarks and shown as "Rmax" in the TOP500 list.^[102] The LINPACK benchmark typically performs LU decomposition of a large matrix. The LINPACK performance gives some indication of performance for some real-world problems, but does not necessarily match the processing requirements of many other supercomputer workloads, which for example may require more memory bandwidth, or may require better integer computing performance, or may need a high performance I/O system to achieve high levels of performance.

The TOP500 list

Main article: TOP500

Further information: List of fastest computers and History of supercomputing

 

Top 20 supercomputers in the world (June 2014)

Since 1993, the fastest supercomputers have been ranked on the TOP500 list according to their LINPACK benchmark results. The list does not claim to be unbiased or definitive, but it is a widely cited current definition of the "fastest" supercomputer available at any given time.

Applications

The stages of supercomputer application may be summarized in the following table:

Decade	Uses and computer involved
1970s	Weather forecasting, aerodynamic research (Cray-1).
1980s	Probabilistic analysis, radiation shielding modeling (CDC Cyber).
1990s	Brute force code breaking (EFF DES cracker).
2000s	3D nuclear test simulations as a substitute for legal conduct Nuclear Non-Proliferation Treaty (ASCI Q).
2010s	Molecular dynamics simulation (Tianhe-1A)
2020s	Scientific research for outbreak prevention/Electrochemical Reaction Research

The IBM Blue Gene/P computer has been used to simulate a number of artificial neurons equivalent to approximately one percent of a human cerebral cortex, containing 1.6 billion neurons with approximately 9 trillion connections. The same research group also succeeded in using a supercomputer to simulate a number of artificial neurons equivalent to the entirety of a rat's brain.

Modern-day weather forecasting also relies on supercomputers. The National Oceanic and Atmospheric Administration uses supercomputers to crunch hundreds of millions of observations to help make weather forecasts more accurate.

In 2011, the challenges and difficulties in pushing the envelope in supercomputing were underscored by IBM's abandonment of the Blue Waters petascale project.

The Advanced Simulation and Computing Program currently uses supercomputers to maintain and simulate the United States nuclear stockpile.

In early 2020, COVID-19 was front and center in the world. Supercomputers used different simulations to find compounds that could potentially stop the spread. These computers run for tens of hours using multiple paralleled running CPU's to model different processes.

Taiwania 3 is a Taiwanese supercomputer which assisted the scientific community in fighting COVID-19. It was launched in 2020 and has a capacity of about two to three PetaFLOPS.

Development and trends

Distribution of TOP500 supercomputers among different countries, in November 2015

In the 2010s, China, the United States, the European Union, and others competed to be the first to create a 1 exaFLOP (10¹⁸ or one quintillion FLOPS) supercomputer. Erik P. DeBenedictis of Sandia National Laboratories has theorized that a zettaFLOPS (10²¹ or one sextillion FLOPS) computer is required to accomplish full weather modeling, which could cover a two-week time span accurately. Such systems might be built around 2030.

Many Monte Carlo simulations use the same algorithm to process a randomly generated data set; particularly, integro-differential equations describing physical transport processes, the random paths, collisions, and energy and momentum depositions of neutrons, photons, ions, electrons, etc. The next step for microprocessors may be into the third dimension; and specializing to Monte Carlo, the many layers could be identical, simplifying the design and manufacture process.

The cost of operating high performance supercomputers has risen, mainly due to increasing power consumption. In the mid-1990s a top 10 supercomputer required in the range of 100 kilowatts, in 2010 the top 10 supercomputers required between 1 and 2 megawatts. A 2010 study commissioned by DARPA identified power consumption as the most pervasive challenge in achieving Exascale computing. At the time a megawatt per year in energy consumption cost about 1 million dollars. Supercomputing facilities were constructed to efficiently remove the increasing amount of heat produced by modern multi-core central processing units. Based on the energy consumption of the Green 500 list of supercomputers between 2007 and 2011, a supercomputer with 1 exaFLOPS in 2011 would have required nearly 500 megawatts. Operating systems were developed for existing hardware to conserve energy whenever possible. CPU cores not in use during the execution of a parallelized application were put into low-power states, producing energy savings for some supercomputing applications.

The increasing cost of operating supercomputers has been a driving factor in a trend toward bundling of resources through a distributed supercomputer infrastructure. National supercomputing centers first emerged in the US, followed by Germany and Japan. The European Union launched the Partnership for Advanced Computing in Europe (PRACE) with the aim of creating a persistent pan-European supercomputer infrastructure with services to support scientists across the European Union in porting, scaling and optimizing supercomputing applications. Iceland built the world's first zero-emission supercomputer. Located at the Thor Data Center in Reykjavík, Iceland, this supercomputer relies on completely renewable sources for its power rather than fossil fuels. The colder climate also reduces the need for active cooling, making it one of the greenest facilities in the world of computers.

Funding supercomputer hardware also became increasingly difficult. In the mid-1990s a top 10 supercomputer cost about 10 million euros, while in 2010 the top 10 supercomputers required an investment of between 40 and 50 million euros. In the 2000s national governments put in place different strategies to fund supercomputers. In the UK the national government funded supercomputers entirely and high performance computing was put under the control of a national funding agency. Germany developed a mixed funding model, pooling local state funding and federal funding.

In fiction

Main article: AI takeover

Many science fiction writers have depicted supercomputers in their works, both before and after the historical construction of such computers. Much of such fiction deals with the relations of humans with the computers they build and with the possibility of conflict eventually developing between them. Examples of supercomputers in fiction include HAL 9000, Multivac, The Machine Stops, GLaDOS, The Evitable Conflict, Vulcan's Hammer, Colossus, WOPR, and Deep Thought.

Myasthenia gravis

From Wikipedia, the free encyclopedia

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

 

Myasthenia gravis
Eye deviation and a drooping eyelid in a person with myasthenia gravis trying to open her eyes
Specialty	Neurology
Symptoms	Varying degrees muscle weakness, double vision, drooping eyelids, trouble talking, trouble walking
Usual onset	Women under 40, men over 60
Duration	Long term
Causes	Autoimmune disease
Diagnostic method	Blood tests for specific antibodies, edrophonium test, nerve conduction studies
Differential diagnosis	Guillain–Barré syndrome, botulism, organophosphate poisoning, brainstem stroke
Treatment	Medications, surgical removal of the thymus, plasmapheresis
Medication	Acetylcholinesterase inhibitors (neostigmine, pyridostigmine), immunosuppressants
Frequency	50 to 200 per million

Myasthenia gravis (MG) is a long-term neuromuscular junction disease that leads to varying degrees of skeletal muscle weakness. The most commonly affected muscles are those of the eyes, face, and swallowing. It can result in double vision, drooping eyelids, trouble talking, and trouble walking. Onset can be sudden. Those affected often have a large thymus or develop a thymoma.

Myasthenia gravis is an autoimmune disease of the neuro-muscular junction which results from antibodies that block or destroy nicotinic acetylcholine receptors (AChR) at the junction between the nerve and muscle. This prevents nerve impulses from triggering muscle contractions. Most cases are due to immunoglobulin G1 (IgG1) and IgG3 antibodies that attack AChR in the postsynaptic membrane, causing complement-mediated damage and muscle weakness. Rarely, an inherited genetic defect in the neuromuscular junction results in a similar condition known as congenital myasthenia. Babies of mothers with myasthenia may have symptoms during their first few months of life, known as neonatal myasthenia. Diagnosis can be supported by blood tests for specific antibodies, the edrophonium test, or a nerve conduction study.

MG is generally treated with medications known as acetylcholinesterase inhibitors, such as neostigmine and pyridostigmine. Immunosuppressants, such as prednisone or azathioprine, may also be used. The surgical removal of the thymus may improve symptoms in certain cases. Plasmapheresis and high-dose intravenous immunoglobulin may be used during sudden flares of the condition. If the breathing muscles become significantly weak, mechanical ventilation may be required. Once intubated acetylcholinesterase inhibitors may be temporarily held to reduce airway secretions.

MG affects 50 to 200 per million people. It is newly diagnosed in three to 30 per million people each year. Diagnosis has become more common due to increased awareness. MG most commonly occurs in women under the age of 40 and in men over the age of 60. It is uncommon in children. With treatment, most of those affected lead relatively normal lives and have a normal life expectancy. The word is from the Greek mys, "muscle" and astheneia "weakness", and the Latin gravis, "serious".

Signs and symptoms

The initial, main symptom in MG is painless weakness of specific muscles, not fatigue. The muscle weakness becomes progressively worse during periods of physical activity and improves after periods of rest. Typically, the weakness and fatigue are worse toward the end of the day. MG generally starts with ocular (eye) weakness; it might then progress to a more severe generalized form, characterized by weakness in the extremities or in muscles that govern basic life functions.

Eyes

In about two-thirds of individuals, the initial symptom of MG is related to the muscles around the eye. Eyelid drooping (ptosis may occur due to weakness of m. levator palpebrae superioris) and double vision (diplopia, due to weakness of the extraocular muscles). Eye symptoms tend to get worse when watching television, reading, or driving, particularly in bright conditions. Consequently, some affected individuals choose to wear sunglasses. The term "ocular myasthenia gravis" describes a subtype of MG where muscle weakness is confined to the eyes, i.e. extraocular muscles, m. levator palpebrae superioris, and m. orbicularis oculi. Typically, this subtype evolves into generalized MG, usually after a few years.

Eating

The weakness of the muscles involved in swallowing may lead to swallowing difficulty (dysphagia). Typically, this means that some food may be left in the mouth after an attempt to swallow, or food and liquids may regurgitate into the nose rather than go down the throat (velopharyngeal insufficiency). Weakness of the muscles that move the jaw (muscles of mastication) may cause difficulty chewing. In individuals with MG, chewing tends to become more tiring when chewing tough, fibrous foods. Difficulty in swallowing, chewing, and speaking is the first symptom in about one-sixth of individuals.

Speaking

Weakness of the muscles involved in speaking may lead to dysarthria and hypophonia. Speech may be slow and slurred, or have a nasal quality. In some cases, a singing hobby or profession must be abandoned.

Head and neck

Due to weakness of the muscles of facial expression and muscles of mastication, facial weakness may manifest as the inability to hold the mouth closed (the "hanging jaw sign") and as a snarling expression when attempting to smile. With drooping eyelids, facial weakness may make the individual appear sleepy or sad. Difficulty in holding the head upright may occur.

Other

The muscles that control breathing and limb movements can also be affected; rarely do these present as the first symptoms of MG, but develop over months to years.^[19] In a myasthenic crisis, a paralysis of the respiratory muscles occurs, necessitating assisted ventilation to sustain life.^[20] Crises may be triggered by various biological stressors such as infection, fever, an adverse reaction to medication, or emotional stress.^[20]

Pathophysiology

MG is an autoimmune synaptopathy. The disorder occurs when the immune system malfunctions and generates antibodies that attack the body's tissues. The antibodies in MG attack a normal human protein, the nicotinic acetylcholine receptor, or a related protein called MuSK, a muscle-specific kinase. Other, less frequent antibodies are found against LRP4, agrin, and titin proteins.

Human leukocyte antigen haplotypes are associated with increased susceptibility to myasthenia gravis and other autoimmune disorders. Relatives of people with myasthenia gravis have a higher percentage of other immune disorders.

The thymus gland cells form part of the body's immune system. In those with myasthenia gravis, the thymus gland is large and abnormal. It sometimes contains clusters of immune cells that indicate lymphoid hyperplasia, and the thymus gland may give wrong instructions to immune cells.

• 

  Neuromuscular junction: 1. Axon 2. Muscle cell membrane 3. Synaptic vesicle 4. Nicotinic acetylcholine receptor 5. Mitochondrion

• 

  A juvenile thymus shrinks with age.

• 

  The nicotinic acetylcholine receptor

In pregnancy

For women who are pregnant and already have MG, in a third of cases, they have been known to experience an exacerbation of their symptoms, and in those cases, it usually occurs in the first trimester of pregnancy. Signs and symptoms in pregnant mothers tend to improve during the second and third trimesters. Complete remission can occur in some mothers. Immunosuppressive therapy should be maintained throughout pregnancy, as this reduces the chance of neonatal muscle weakness, and controls the mother's myasthenia.

About 10–20% of infants with mothers affected by the condition are born with transient neonatal myasthenia (TNM), which generally produces feeding and respiratory difficulties that develop about 12 hours to several days after birth. A child with TNM typically responds very well to acetylcholinesterase inhibitors, and the condition generally resolves over a period of three weeks, as the antibodies diminish, and generally does not result in any complications. Very rarely, an infant can be born with arthrogryposis multiplex congenita, secondary to profound intrauterine weakness. This is due to maternal antibodies that target an infant's acetylcholine receptors. In some cases, the mother remains asymptomatic.

Diagnosis

MG can be difficult to diagnose, as the symptoms can be subtle and hard to distinguish from both normal variants and other neurological disorders.

Three types of myasthenic symptoms in children can be distinguished:

1. Transient neonatal myasthenia occurs in 10 to 15% of babies born to mothers afflicted with the disorder, and disappears after a few weeks.
2. Congenital myasthenia, the rarest form, occurs when genes are present from both parents.
3. Juvenile myasthenia gravis is most common in females.

Congenital myasthenias cause muscle weakness and fatigability similar to those of MG. The signs of congenital myasthenia usually are present in the first years of childhood, although they may not be recognized until adulthood.

Classification

Myasthenia Gravis Foundation of America Clinical Classification

Class	Description
I	Any eye muscle weakness, possible ptosis, no other evidence of muscle weakness elsewhere
II	Eye muscle weakness of any severity, mild weakness of other muscles
IIa	Predominantly limb or axial muscles
IIb	Predominantly bulbar and/or respiratory muscles
III	Eye muscle weakness of any severity, moderate weakness of other muscles
IIIa	Predominantly limb or axial muscles
IIIb	Predominantly bulbar and/or respiratory muscles
IV	Eye muscle weakness of any severity, severe weakness of other muscles
IVa	Predominantly limb or axial muscles
IVb	Predominantly bulbar and/or respiratory muscles
V	Intubation needed to maintain airway

When diagnosed with MG, a person is assessed for his or her neurological status and the level of illness is established. This is usually done using the accepted Myasthenia Gravis Foundation of America Clinical Classification scale.

Physical examination

During a physical examination to check for MG, a doctor might ask the person to perform repetitive movements. For instance, the doctor may ask one to look at a fixed point for 30 seconds and to relax the muscles of the forehead, because a person with MG and ptosis of the eyes might be involuntarily using the forehead muscles to compensate for the weakness in the eyelids. The clinical examiner might also try to elicit the "curtain sign" in a person by holding one of the person's eyes open, which in the case of MG will lead the other eye to close.

Blood tests

If the diagnosis is suspected, serology can be performed:

• One test is for antibodies against the acetylcholine receptor; the test has a reasonable sensitivity of 80–96%, but in ocular myasthenia, the sensitivity falls to 50%.
• A proportion of the people without antibodies against the acetylcholine receptor have antibodies against the MuSK protein.
• In specific situations, testing is performed for Lambert-Eaton syndrome.

Electrodiagnostics

A chest CT-scan showing a thymoma (red circle)

 

Photograph of a person showing right partial ptosis (left picture), the left lid shows compensatory pseudo lid retraction because of equal innervation of the m. levator palpabrae superioris (Hering's law of equal innervation): Right picture: after an edrophonium test, note the improvement in ptosis.

Muscle fibers of people with MG are easily fatigued, which the repetitive nerve stimulation test can help diagnose. In single-fiber electromyography, which is considered to be the most sensitive (although not the most specific) test for MG, a thin needle electrode is inserted into different areas of a particular muscle to record the action potentials from several samplings of different individual muscle fibers. Two muscle fibers belonging to the same motor unit are identified, and the temporal variability in their firing patterns is measured. Frequency and proportion of particular abnormal action potential patterns, called "jitter" and "blocking", are diagnostic. Jitter refers to the abnormal variation in the time interval between action potentials of adjacent muscle fibers in the same motor unit. Blocking refers to the failure of nerve impulses to elicit action potentials in adjacent muscle fibers of the same motor unit.

Ice test

Applying ice for 2–5 minutes to the muscles reportedly has a sensitivity and specificity of 76.9% and 98.3%, respectively, for the identification of MG. Acetylcholinesterase is thought to be inhibited at the lower temperature, which is the basis for this diagnostic test. This generally is performed on the eyelids when ptosis is present and is deemed positive if a ≥2-mm rise in the eyelid occurs after the ice is removed.

Edrophonium test

This test requires the intravenous administration of edrophonium chloride or neostigmine, drugs that block the breakdown of acetylcholine by cholinesterase (acetylcholinesterase inhibitors). This test is no longer typically performed, as its use can lead to life-threatening bradycardia (slow heart rate) which requires immediate emergency attention. Production of edrophonium was discontinued in 2008.

Imaging

A chest X-ray may identify widening of the mediastinum suggestive of thymoma, but computed tomography or magnetic resonance imaging (MRI) are more sensitive ways to identify thymomas and are generally done for this reason. MRI of the cranium and orbits may also be performed to exclude compressive and inflammatory lesions of the cranial nerves and ocular muscles.

Pulmonary function test

The forced vital capacity may be monitored at intervals to detect increasing muscular weakness. Acutely, negative inspiratory force may be used to determine adequacy of ventilation; it is performed on those individuals with MG.

Management

Treatment is by medication and/or surgery. Medication consists mainly of acetylcholinesterase inhibitors to directly improve muscle function and immunosuppressant drugs to reduce the autoimmune process. Thymectomy is a surgical method to treat MG.

Medication

Neostigmine, chemical structure

 

Azathioprine, chemical structure

Worsening may occur with medication such as fluoroquinolones, aminoglycosides, and magnesium. About 10% of people with generalized MG are considered treatment-refractory. Autologous hematopoietic stem cell transplantation (HSCT) is sometimes used in severe, treatment-refractory MG. Available data provide preliminary evidence that HSCT can be an effective therapeutic option in carefully selected cases.

Efgartigimod alfa was approved for medical use in the United States in December 2021.

Acetylcholinesterase inhibitors

Acetylcholinesterase inhibitors can provide symptomatic benefit and may not fully remove a person's weakness from MG. While they might not fully remove all symptoms of MG, they still may allow a person the ability to perform normal daily activities. Usually, acetylcholinesterase inhibitors are started at a low dose and increased until the desired result is achieved. If taken 30 minutes before a meal, symptoms will be mild during eating, which is helpful for those who have difficulty swallowing due to their illness. Another medication used for MG, atropine, can reduce the muscarinic side effects of acetylcholinesterase inhibitors. Pyridostigmine is a relatively long-acting drug (when compared to other cholinergic agonists), with a half-life around four hours with relatively few side effects. Generally, it is discontinued in those who are being mechanically ventilated, as it is known to increase the amount of salivary secretions. A few high-quality studies have directly compared cholinesterase inhibitors with other treatments (or placebo); their practical benefit may be so significant that conducting studies in which they would be withheld from some people would be difficult.

Immune suppressants

The steroid prednisone might also be used to achieve a better result, but it can lead to the worsening of symptoms and takes weeks to achieve its maximal effectiveness. Due to the myriad symptoms that steroid treatments can cause, it is not the preferred method of treatment. Other immune suppressing medications may also be used including rituximab or azathioprine.

Plasmapheresis and IVIG

If the myasthenia is serious (myasthenic crisis), plasmapheresis can be used to remove the putative antibodies from the circulation. Also, intravenous immunoglobulins (IVIGs) can be used to bind the circulating antibodies. Both of these treatments have relatively short-lived benefits, typically measured in weeks, and often are associated with high costs, which make them prohibitive; they are generally reserved for when MG requires hospitalization.

Surgery

As thymomas are seen in 10% of all people with the MG, they are often given a chest X-ray and CT scan to evaluate their need for surgical removal of their thymus glands and any cancerous tissue that may be present. Even if surgery is performed to remove a thymoma, it generally does not lead to the remission of MG. Surgery in the case of MG involves the removal of the thymus, although in 2013, no clear benefit was indicated except in the presence of a thymoma. A 2016 randomized, controlled trial, however, found some benefits.

Physical measures

People with MG should be educated regarding the fluctuating nature of their symptoms, including weakness and exercise-induced fatigue. Exercise participation should be encouraged with frequent rest. In people with generalized MG, some evidence indicates a partial home program including training in diaphragmatic breathing, pursed-lip breathing, and interval-based muscle therapy may improve respiratory muscle strength, chest wall mobility, respiratory pattern, and respiratory endurance.

Medical imaging

In people with myasthenia gravis, older forms of iodinated contrast used for medical imaging have caused an increased risk of exacerbation of the disease, but modern forms have no immediate increased risk.

Prognosis

The prognosis of people with MG is generally good, as is quality of life, when given very good treatment. Monitoring of a person with MG is very important, as at least 20% of people diagnosed with it will experience a myasthenic crisis within two years of their diagnosis, requiring rapid medical intervention. Generally, the most disabling period of MG might be years after the initial diagnosis. In the early 1900s, 70% of detected cases died from lung problems; now, that number is estimated to be around 3–5%, an improvement attributed to increased awareness and medications to manage symptoms.

Epidemiology

MG occurs in all ethnic groups and both sexes. It most commonly affects women under 40 and people from 50 to 70 years old of either sex, but it has been known to occur at any age. Younger people rarely have thymoma. Prevalence in the United States is estimated at between 0.5 and 20.4 cases per 100,000, with an estimated 60,000 Americans affected. In the United Kingdom, an estimated 15 cases of MG occur per 100,000 people.

History

The first to write about MG were Thomas Willis, Samuel Wilks, Erb, and Goldflam. The term "myasthenia gravis pseudo-paralytica" was proposed in 1895 by Jolly, a German physician. Mary Walker treated a person with MG with physostigmine in 1934. Simpson and Nastuck detailed the autoimmune nature of the condition. In 1973, Patrick and Lindstrom used rabbits to show that immunization with purified muscle-like acetylcholine receptors caused the development of MG-like symptoms.

Research

Immunomodulating substances, such as drugs that prevent acetylcholine receptor modulation by the immune system, are currently being researched. Some research recently has been on anti-c5 inhibitors for treatment research as they are safe and used in the treatment of other diseases. Ephedrine seems to benefit some people more than other medications, but it has not been properly studied as of 2014. In the laboratory, MG is mostly studied in model organisms, such as rodents. In addition, in 2015, scientists developed an in vitro functional, all-human, neuromuscular junction assay from human embryonic stem cells and somatic-muscle stem cells. After the addition of pathogenic antibodies against the acetylcholine receptor and activation of the complement system, the neuromuscular co-culture shows symptoms such as weaker muscle contractions.