Computer simulation

From Wikipedia, the free encyclopedia

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

A 48-hour computer simulation of Typhoon Mawar using the Weather Research and Forecasting model

Process of building a computer model, and the interplay between experiment, simulation, and theory

Computer simulation is the running of a mathematical model on a computer, the model being designed to represent the behaviour of, or the outcome of, a real-world or physical system. The reliability of some mathematical models can be determined by comparing their results to the real-world outcomes they aim to predict. Computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics (computational physics), astrophysics, climatology, chemistry, biology and manufacturing, as well as human systems in economics, psychology, social science, health care and engineering. Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions.

Computer simulations are realized by running computer programs that can be either small, running almost instantly on small devices, or large-scale programs that run for hours or days on network-based groups of computers. The scale of events being simulated by computer simulations has far exceeded anything possible (or perhaps even imaginable) using traditional paper-and-pencil mathematical modeling. In 1997, a desert-battle simulation of one force invading another involved the modeling of 66,239 tanks, trucks and other vehicles on simulated terrain around Kuwait, using multiple supercomputers in the DoD High Performance Computer Modernization Program. Other examples include a 1-billion-atom model of material deformation; a 2.64-million-atom model of the complex protein-producing organelle of all living organisms, the ribosome, in 2005; a complete simulation of the life cycle of Mycoplasma genitalium in 2012; and the Blue Brain project at EPFL (Switzerland), begun in May 2005 to create the first computer simulation of the entire human brain, right down to the molecular level.

Because of the computational cost of simulation, computer experiments are used to perform inference such as uncertainty quantification.

Simulation versus model

A model consists of the equations used to capture the behavior of a system. By contrast, computer simulation is the actual running of the program that perform algorithms which solve those equations, often in an approximate manner. Simulation, therefore, is the process of running a model. Thus one would not "build a simulation"; instead, one would "build a model (or a simulator)", and then either "run the model" or equivalently "run a simulation".

History

Computer simulation developed hand-in-hand with the rapid growth of the computer, following its first large-scale deployment during the Manhattan Project in World War II to model the process of nuclear detonation. It was a simulation of 12 hard spheres using a Monte Carlo algorithm. Computer simulation is often used as an adjunct to, or substitute for, modeling systems for which simple closed form analytic solutions are not possible. There are many types of computer simulations; their common feature is the attempt to generate a sample of representative scenarios for a model in which a complete enumeration of all possible states of the model would be prohibitive or impossible.

Data preparation

The external data requirements of simulations and models vary widely. For some, the input might be just a few numbers (for example, simulation of a waveform of AC electricity on a wire), while others might require terabytes of information (such as weather and climate models).

Input sources also vary widely:

• Sensors and other physical devices connected to the model;
• Control surfaces used to direct the progress of the simulation in some way;
• Current or historical data entered by hand;
• Values extracted as a by-product from other processes;
• Values output for the purpose by other simulations, models, or processes.

Lastly, the time at which data is available varies:

• "invariant" data is often built into the model code, either because the value is truly invariant (e.g., the value of π) or because the designers consider the value to be invariant for all cases of interest;
• data can be entered into the simulation when it starts up, for example by reading one or more files, or by reading data from a preprocessor;
• data can be provided during the simulation run, for example by a sensor network.

Because of this variety, and because diverse simulation systems have many common elements, there are a large number of specialized simulation languages. The best-known may be Simula. There are now many others.

Systems that accept data from external sources must be very careful in knowing what they are receiving. While it is easy for computers to read in values from text or binary files, what is much harder is knowing what the accuracy (compared to measurement resolution and precision) of the values are. Often they are expressed as "error bars", a minimum and maximum deviation from the value range within which the true value (is expected to) lie. Because digital computer mathematics is not perfect, rounding and truncation errors multiply this error, so it is useful to perform an "error analysis" to confirm that values output by the simulation will still be usefully accurate.

Types

Models used for computer simulations can be classified according to several independent pairs of attributes, including:

• Stochastic or deterministic (and as a special case of deterministic, chaotic) – see external links below for examples of stochastic vs. deterministic simulations
• Steady-state or dynamic
• Continuous or discrete (and as an important special case of discrete, discrete event or DE models)
• Dynamic system simulation, e.g. electric systems, hydraulic systems or multi-body mechanical systems (described primarily by DAE:s) or dynamics simulation of field problems, e.g. CFD of FEM simulations (described by PDE:s).
• Local or distributed.

Another way of categorizing models is to look at the underlying data structures. For time-stepped simulations, there are two main classes:

• Simulations which store their data in regular grids and require only next-neighbor access are called stencil codes. Many CFD applications belong to this category.
• If the underlying graph is not a regular grid, the model may belong to the meshfree method class.

For steady-state simulations, equations define the relationships between elements of the modeled system and attempt to find a state in which the system is in equilibrium. Such models are often used in simulating physical systems, as a simpler modeling case before dynamic simulation is attempted.

• Dynamic simulations attempt to capture changes in a system in response to (usually changing) input signals.
• Stochastic models use random number generators to model chance or random events;
• A discrete event simulation (DES) manages events in time. Most computer, logic-test and fault-tree simulations are of this type. In this type of simulation, the simulator maintains a queue of events sorted by the simulated time they should occur. The simulator reads the queue and triggers new events as each event is processed. It is not important to execute the simulation in real time. It is often more important to be able to access the data produced by the simulation and to discover logic defects in the design or the sequence of events.
• A continuous dynamic simulation performs numerical solution of differential-algebraic equations or differential equations (either partial or ordinary). Periodically, the simulation program solves all the equations and uses the numbers to change the state and output of the simulation. Applications include flight simulators, construction and management simulation games, chemical process modeling, and simulations of electrical circuits. Originally, these kinds of simulations were actually implemented on analog computers, where the differential equations could be represented directly by various electrical components such as op-amps. By the late 1980s, however, most "analog" simulations were run on conventional digital computers that emulate the behavior of an analog computer.
• A special type of discrete simulation that does not rely on a model with an underlying equation, but can nonetheless be represented formally, is agent-based simulation. In agent-based simulation, the individual entities (such as molecules, cells, trees or consumers) in the model are represented directly (rather than by their density or concentration) and possess an internal state and set of behaviors or rules that determine how the agent's state is updated from one time-step to the next.
• Distributed models run on a network of interconnected computers, possibly through the Internet. Simulations dispersed across multiple host computers like this are often referred to as "distributed simulations". There are several standards for distributed simulation, including Aggregate Level Simulation Protocol (ALSP), Distributed Interactive Simulation (DIS), the High Level Architecture (simulation) (HLA) and the Test and Training Enabling Architecture (TENA).

Visualization

Formerly, the output data from a computer simulation was sometimes presented in a table or a matrix showing how data were affected by numerous changes in the simulation parameters. The use of the matrix format was related to traditional use of the matrix concept in mathematical models. However, psychologists and others noted that humans could quickly perceive trends by looking at graphs or even moving-images or motion-pictures generated from the data, as displayed by computer-generated-imagery (CGI) animation. Although observers could not necessarily read out numbers or quote math formulas, from observing a moving weather chart they might be able to predict events (and "see that rain was headed their way") much faster than by scanning tables of rain-cloud coordinates. Such intense graphical displays, which transcended the world of numbers and formulae, sometimes also led to output that lacked a coordinate grid or omitted timestamps, as if straying too far from numeric data displays. Today, weather forecasting models tend to balance the view of moving rain/snow clouds against a map that uses numeric coordinates and numeric timestamps of events.

Similarly, CGI computer simulations of CAT scans can simulate how a tumor might shrink or change during an extended period of medical treatment, presenting the passage of time as a spinning view of the visible human head, as the tumor changes.

Other applications of CGI computer simulations are being developed to graphically display large amounts of data, in motion, as changes occur during a simulation run.

In science

Computer simulation of the process of osmosis

Generic examples of types of computer simulations in science, which are derived from an underlying mathematical description:

• a numerical simulation of differential equations that cannot be solved analytically, theories that involve continuous systems such as phenomena in physical cosmology, fluid dynamics (e.g., climate models, roadway noise models, roadway air dispersion models), continuum mechanics and chemical kinetics fall into this category.
• a stochastic simulation, typically used for discrete systems where events occur probabilistically and which cannot be described directly with differential equations (this is a discrete simulation in the above sense). Phenomena in this category include genetic drift, biochemical or gene regulatory networks with small numbers of molecules. (see also: Monte Carlo method).
• multiparticle simulation of the response of nanomaterials at multiple scales to an applied force for the purpose of modeling their thermoelastic and thermodynamic properties. Techniques used for such simulations are Molecular dynamics, Molecular mechanics, Monte Carlo method, and Multiscale Green's function.

Specific examples of computer simulations include:

• statistical simulations based upon an agglomeration of a large number of input profiles, such as the forecasting of equilibrium temperature of receiving waters, allowing the gamut of meteorological data to be input for a specific locale. This technique was developed for thermal pollution forecasting.
• agent based simulation has been used effectively in ecology, where it is often called "individual based modeling" and is used in situations for which individual variability in the agents cannot be neglected, such as population dynamics of salmon and trout (most purely mathematical models assume all trout behave identically).
• time stepped dynamic model. In hydrology there are several such hydrology transport models such as the SWMM and DSSAM Models developed by the U.S. Environmental Protection Agency for river water quality forecasting.
• computer simulations have also been used to formally model theories of human cognition and performance, e.g., ACT-R.
• computer simulation using molecular modeling for drug discovery.
• computer simulation to model viral infection in mammalian cells.
• computer simulation for studying the selective sensitivity of bonds by mechanochemistry during grinding of organic molecules.
• Computational fluid dynamics simulations are used to simulate the behaviour of flowing air, water and other fluids. One-, two- and three-dimensional models are used. A one-dimensional model might simulate the effects of water hammer in a pipe. A two-dimensional model might be used to simulate the drag forces on the cross-section of an aeroplane wing. A three-dimensional simulation might estimate the heating and cooling requirements of a large building.
• An understanding of statistical thermodynamic molecular theory is fundamental to the appreciation of molecular solutions. Development of the Potential Distribution Theorem (PDT) allows this complex subject to be simplified to down-to-earth presentations of molecular theory.

Notable, and sometimes controversial, computer simulations used in science include: Donella Meadows' World3 used in the Limits to Growth, James Lovelock's Daisyworld and Thomas Ray's Tierra.

In social sciences, computer simulation is an integral component of the five angles of analysis fostered by the data percolation methodology, which also includes qualitative and quantitative methods, reviews of the literature (including scholarly), and interviews with experts, and which forms an extension of data triangulation. Of course, similar to any other scientific method, replication is an important part of computational modeling.

In practical contexts

See also: List of geological modelling software

Computer simulations are used in a wide variety of practical contexts, such as:

• analysis of air pollutant dispersion using atmospheric dispersion modeling
• As a possible humane alternative to live animal testing in respect to animal rights.
• design of complex systems such as aircraft and also logistics systems.
• design of noise barriers to effect roadway noise mitigation
• modeling of application performance
• flight simulators to train pilots
• weather forecasting
• Numerical modeling (geology)
• forecasting of risk
• simulation of electrical circuits
• Power system simulation
• simulation of other computers is emulation.
• forecasting of prices on financial markets (for example Adaptive Modeler)
• behavior of structures (such as buildings and industrial parts) under stress and other conditions
• design of industrial processes, such as chemical processing plants
• strategic management and organizational studies
• reservoir simulation for the petroleum engineering to model the subsurface reservoir
• process engineering simulation tools.
• robot simulators for the design of robots and robot control algorithms
• urban simulation models that simulate dynamic patterns of urban development and responses to urban land use and transportation policies.
• traffic engineering to plan or redesign parts of the street network from single junctions over cities to a national highway network to transportation system planning, design and operations. See a more detailed article on Simulation in Transportation.
• modeling car crashes to test safety mechanisms in new vehicle models.
• crop-soil systems in agriculture, via dedicated software frameworks (e.g. BioMA, OMS3, APSIM)

The reliability and the trust people put in computer simulations depends on the validity of the simulation model, therefore verification and validation are of crucial importance in the development of computer simulations. Another important aspect of computer simulations is that of reproducibility of the results, meaning that a simulation model should not provide a different answer for each execution. Although this might seem obvious, this is a special point of attention in stochastic simulations, where random numbers should actually be semi-random numbers. An exception to reproducibility are human-in-the-loop simulations such as flight simulations and computer games. Here a human is part of the simulation and thus influences the outcome in a way that is hard, if not impossible, to reproduce exactly.

Vehicle manufacturers make use of computer simulation to test safety features in new designs. By building a copy of the car in a physics simulation environment, they can save the hundreds of thousands of dollars that would otherwise be required to build and test a unique prototype. Engineers can step through the simulation milliseconds at a time to determine the exact stresses being put upon each section of the prototype.

Computer graphics can be used to display the results of a computer simulation. Animations can be used to experience a simulation in real-time, e.g., in training simulations. In some cases animations may also be useful in faster than real-time or even slower than real-time modes. For example, faster than real-time animations can be useful in visualizing the buildup of queues in the simulation of humans evacuating a building. Furthermore, simulation results are often aggregated into static images using various ways of scientific visualization.

In debugging, simulating a program execution under test (rather than executing natively) can detect far more errors than the hardware itself can detect and, at the same time, log useful debugging information such as instruction trace, memory alterations and instruction counts. This technique can also detect buffer overflow and similar "hard to detect" errors as well as produce performance information and tuning data.

Pitfalls

Although sometimes ignored in computer simulations, it is very important to perform a sensitivity analysis to ensure that the accuracy of the results is properly understood. For example, the probabilistic risk analysis of factors determining the success of an oilfield exploration program involves combining samples from a variety of statistical distributions using the Monte Carlo method. If, for instance, one of the key parameters (e.g., the net ratio of oil-bearing strata) is known to only one significant figure, then the result of the simulation might not be more precise than one significant figure, although it might (misleadingly) be presented as having four significant figures.

Autoimmune disease in women

From Wikipedia, the free encyclopedia

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

Autoimmunity refers to a pathological immune response of the body's immune system against itself. Autoimmune disease is widely recognized to be significantly more common in women than in men, and often presents differently between the sexes. In addition to gender, race and ethnicity can also play a role in prevalence rates. The reasons for these disparities are still under investigation, but may in part involve the presence of an additional X chromosome in women (given that several genes on the X chromosome are associated with immune system development), as well as the higher presence of female sex hormones such as estrogen (which increases immune system response). The risk, incidence, and character of autoimmune disease in women may also be associated with female-specific physiological changes, such as hormonal shifts during menses, pregnancy, and menopause.

Common autoimmune symptoms experienced by both sexes include rashes, fevers, fatigue, and joint pain. Symptoms which are specific to women include irregular menses, pelvic pain, or vaginal dryness, depending on the given disease. Some diseases such as Graves' disease, rheumatoid arthritis, and multiple sclerosis may improve during pregnancy, whereas others such as lupus may worsen.

Currently, it is not possible to cure autoimmune disease, but many treatments are available. Treatment of autoimmune disease can be broadly classified into anti-inflammatory, immunosuppressive, and palliative – i.e., correcting a functional disturbance related to the condition. Some medications used to treat autoimmune diseases might not be safe to use during pregnancy.

Specific roles of discrimination and disparities may impact women with autoimmune diseases in the categories of race and ethnicity, gaps in research, in workplaces, and in healthcare coverage and treatment. Accommodations can be put instituted in workplaces to balance disease profiles and working conditions.

Common diseases

Autoimmune Disease Cases by Sex

Disease	Ratio of female:male cases
Addison's disease	1.5:1
Ankylosing spondylitis	1:3 – 1:4
anti-GBM disease (Goodpasture syndrome)	1:2 – 1:9
Antiphospholipid syndrome	3.5:1
Autoimmune hepatitis	3:1
Behçet's disease	1:5 – 2:1
Celiac disease	1.5:1 – 2:1
Crohn's disease	1:1 (though F>M after age 25)
Dermatomyositis	2:1 – 3:1
Diabetes type 1	1:1.8
Giant cell arteritis	3:1 – 4:1
Graves' disease	5:1
Hashimoto's thyroiditis	4:1 – 9:1
Multiple sclerosis	2:1 – 3:1
Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disease (MOGAD)	1:1
Myasthenia gravis	3:1 (below 40 years of age)
Neuromyelitis Optica (NMOSD)	9:1
Pemphigus vulgaris	2:1
Polymyositis	2:1
Primary biliary cholangitis	9:1
Primary sclerosing cholangitis	1:2
Rheumatoid arthritis	2:1
Sjögren's disease	9:1
Systemic lupus erythematosus	9:1
Systemic sclerosis (scleroderma)	3:1 – 9:1
Takayasu's arteritis	9:1
Ulcerative colitis	1:1 (though M>F after age 45)

There are over 100 autoimmune conditions described, of which the majority are more prevalent in women than in men. Approximately 80% of all patients with autoimmune disease are women.

Autoimmune diseases which overwhelmingly affect women include those which affect the thyroid gland (Hashimoto's thyroiditis, Graves' disease), rheumatic diseases (systemic lupus erythematosus, rheumatoid arthritis, scleroderma, and Sjögren's disease), hepatobiliary diseases (primary biliary cholangitis, autoimmune hepatitis), and neurological diseases (myasthenia gravis, neuromyelitis optica spectrum disorders (NMOSD), and multiple sclerosis). For men who may develop these conditions, epidemiological and symptomological differences may still exist. For example, when multiple sclerosis and rheumatoid arthritis do occur in men, they tend to develop later in life for men (around age 30–40) than for women, when incidence rises after puberty.

Some autoimmune diseases affect both sexes at roughly equal rates, or have only a slight female predominance. These conditions include inflammatory bowel disease (ulcerative colitis, Crohn's disease), immune thrombocytopenic purpura (ITP), and MOG antibody disease, among others. Although the lifetime incidence of these diseases may be similar, there may still exist a difference in disease onset, course, complications, and prognosis, which vary based on sex. For example, men are more likely to develop Crohn's disease in the upper GI tract compared to women. Males and females are equally as likely to be affected by Crohn's disease until around age 25, when women become overrepresented as Crohn's disease patients. Women and men are equally likely to develop ulcerative colitis until age 45, after which this shifts to a significant male predominance.

Very few autoimmune diseases are thought to be more common in men than in women. Examples of these may include ankylosing spondylitis, primary sclerosing cholangitis, type 1 diabetes, and certain vasculitides including anti-GBM disease (Goodpasture syndrome) and Behçet's disease (though whether this represents an autoimmune disease vs autoinflammatory disease remains unclear.) On closer inspection, some diseases initially thought to be overrepresented in men have trended towards sex neutrality over time. For example, early studies of ankylosing spondylitis reported a ratio of 10:1 male to female patients, but more recent reports have indicated this is closer to 3:1. This may reflect a true increased incidence in women over time, or may be due to improvements in diagnostic testing.

Additionally, sex ratios of affected patients can vary widely between geographic regions. For instance, Crohn's disease is slightly more common in women in Western countries, whereas it is slightly more common in men in Asian countries. Behçet's disease is more common in males in regions along the historic Silk Road, but is more common in women in the United States. This suggests that the risks of developing autoimmune disease are multifactorial, and may vary based on race and environment as well as sex.

Race and ethnicity

Race and ethnicity also has data affecting prevalence rates for women with autoimmune diseases. For example, in comparison to white females, Lupus is more prevalent predominately black, but also native American, Asian, and Hispanic females. For Native American women, autoimmune hyperparathyroidism is more prevalent. In addition, among Caucasian women, celiac disease (or gluten intolerance) is more prevalent than in other races or genders.

Signs and symptoms

Autoimmune diseases can result in systemic or localized symptoms, depending on the given disease. Typical systemic symptoms include fevers, fatigue, muscle aches, joint pain, and rashes; these can be seen in diseases such as lupus or rheumatoid arthritis. Other autoimmune diseases have localized effects on specific organs or tissue types. For instance, alopecia areata presents with patchy baldness due to autoimmune destruction of hair follicles, whereas multiple sclerosis presents with neurological symptoms due to autoimmune demyelination of the central nervous system.

Both systemic and localized disease can present with symptoms that are exclusive to women. Women with Sjögren's disease (an autoimmune disease characterized by destruction and inflammation of the salivary and lacrimal glands) are 2–3 times more likely to report vaginal dryness than other postmenopausal women.

Causes

The causes of autoimmunity remain the subject of extensive research and include genetic as well as environmental factors. However, the clear overrepresentation of women as persons with autoimmune disease suggests that sex-specific factors are highly instrumental in the development of these conditions. Posited reasons for this disparity include the differential effects of sex hormones (especially estrogen) on immune response, X-chromosome inactivation, changes associated with pregnancy, and evolutionary pressures that affect the sexes differently. Due to biological development, many of these elements are inextricably linked, and it can be difficult to isolate the individual effects of each factor.

X chromosome inactivation

See also: Lyonization

Many genes involved in the immune response reside on the X chromosome, of which most women have two copies, whereas men typically only have one. During cell division in embryological development, one of the two X chromosomes is inactivated at random, in a process called lyonization. This ensures that the expression of X chromosome genes is randomly suppressed on one of the two copies in females to compensate for the extra copy of these genes. Incomplete suppression of the extra copies of these genes may lead to overexpression of some genes involved in the immune response, resulting in a more robust immune response and an increased risk of developing autoimmune diseases.

Additional support for this hypothesis can be illustrated by the higher rates of autoimmune disease in men with Klinefelter syndrome (47,XXY). Like women, males with Klinefelter syndrome also have two copies of the X chromosome, which may predispose them to increased risk of autoimmune disease through the same mechanism. This risk is highest in autoimmune diseases which are female-predominant (e.g., Addison's disease, multiple sclerosis, Sjögren's disease). With the exception of Type 1 diabetes, which affects both sexes at roughly equal rates, Klinefelter syndrome was not correlated with increased risk of autoimmune diseases which occur in males with greater or equal frequency (e.g., ankylosing spondylitis, psoriasis.)

Despite having only one copy of the X chromosome, women with Turner syndrome (45,XO) are still twice as likely as the general female population to develop autoimmune diseases. Interestingly, the autoimmune diseases for which Turner syndrome patients are at greater risk include inflammatory bowel disease, type 1 diabetes, alopecia areata, and several other autoimmune disorders, which tend to affect the sexes at roughly equal rates. This suggests that the development of autoimmune disease is not solely mediated by differential expression of genes on the X chromosome.

Sex hormones

Sex hormones are instrumental in nearly every aspect of human biology, including the development and response of the adaptive immune system. Sex hormones such as estrogen, progesterone, and testosterone are all present in healthy men and women, albeit at different levels. Estrogen and progesterone are considered primary female sex hormones, while testosterone is the primary male sex hormone. Broadly speaking, estrogen is understood to be immune-activating, while testosterone is considered to be immune-suppressing. The ideal immune system response must be alert enough to recognize and destroy foreign antigens, while also being selective enough to avoid attacking the self. There exists a necessary trade-off between immune system hyperactivity (autoimmunity) versus hypoactivity (immune deficiency). Since men and women have different levels of these sex hormones, they necessarily incur unequal risk for developing these conditions. Very broadly speaking, men are more predisposed to infectious disease, but are less likely to develop autoimmune disease. Women, conversely, are at higher risk for developing autoimmune disease, but are more protected from infectious disease than men. Women have a greater number of circulating antibodies than do men, which has implications for their development of autoimmune disease, as well as their increased resistance to infectious disease.

Estrogen

Estrogen has significant effects on the response of the adaptive immune system. Higher levels of estrogen are correlated with higher levels of circulating antibodies, which are responsible for mounting an immune response. In addition to short-term changes, the immune system may also be influenced by longer-term changes, such as total lifetime exposure. The course of disease may also be related to hormonal fluctuations, especially those of puberty, pregnancy, and menopause.

Testosterone

The immunocompetence handicap hypothesis proposes that testosterone may have utility as a secondary sexual characteristic that signals fitness to prospective mates. As males have higher levels of testosterone, which suppresses immune system activity, signaling fitness despite this handicap is a demonstration of mate quality despite this handicap. Additional proof-of-concept can be demonstrated through testosterone supplementation. Men with Klinefelter syndrome (47,XXY) naturally make very little testosterone; androgen supplementation has been shown to decrease serum levels of all immunoglobulins in these men.

Pregnancy

Pregnancy has both short- and long-term effects on the immune system, and these changes may persist even after the completion of pregnancy. These effects on the course of autoimmune diseases vary widely and are dependent on the specific disease, as well as the individual patient. Conditions such as rheumatoid arthritis often improve throughout pregnancy, especially in the second and third trimesters; however, women often relapse within three months of giving birth. Other conditions, such as lupus, often become much worse throughout pregnancy.

During pregnancy, the hormone estrogen spikes; additionally, hormonal fluctuations may continue long after childbirth. These changes could trigger, improve or even worsen an autoimmune disease. In addition to estrogen, other hormones like progesterone and prolactin may trigger these illnesses.

The mother's immune system tends to be suppressed during pregnancy to prevent fetal rejection from foreign antibodies in the fetus. As stated before, pregnancy causes an increase in estrogen in the female body. The increase of this hormone weakens the functioning of immune cells, thus debilitating the mother's immune system. In addition, fetal cells may continue to circulate in the mother's body for years after childbirth, making it a possible trigger for autoimmune disease.

Diagnosis

Autoantibodies with commonly-associated autoimmune diseases

Autoantibody	Condition
ANA*("anti-nuclear antibody")	Lupus
AHA ("anti-histone antibody")	Drug-induced lupus
ds-DNA ("anti-double-stranded DNA antibody")	Systemic lupus erythematosus with renal involvement
SMA ("anti-smooth muscle antibody")	Autoimmune Hepatitis
AMA ("anti-mitochondrial antibody")	Primary Biliary cholangitis
ACA ("anti-centromere antibody")	Scleroderma (CREST)
SS-A/Ro Ab ("anti-Sjögren's syndrome A"/"anti-Ro" antibody)	Sjögren's disease
CCP ("anti-cyclic citrullinated peptide")	Rheumatoid Arthritis
RF (rheumatoid factor)	Rheumatoid Arthritis
Jo ("anti-Jo antibody")	Polymyositis
anti-Scl-70 ("anti-topoisomerase I antibody")	Systemic Scleroderma

Diagnosis of autoimmune disease is based upon clinical and laboratory evidence. To diagnose autoimmune disease, typical symptoms of a given disorder must be present, along with laboratory evidence of autoantibodies. Autoantibodies develop throughout the course of autoimmune disease, as the immune system mistakenly forms specific antibodies to its own tissues, resulting in inflammation. The presence of autoantibodies alone is not sufficient for diagnosis, as autoantibodies may arise for a variety of other reasons, including malignancy, infection, or injury, and may be present even in completely healthy persons. However, it is possible for persons to have detectable autoantibody levels before clinically developing autoimmune disease; this state may be characterized as pre-autoimmunity. Additionally, it is possible to display clinical signs of autoimmune disease before autoantibody levels are detectable. Most autoantibody assays are more sensitive than they are specific; that is, a negative autoantibody test is better at excluding a given disease than a positive autoantibody test is at diagnosing a disease.

Generally, autoantibody results are reported in the form of titers, with higher titers (e.g., 1:160) indicating greater autoantibody concentration than lower titers (e.g., 1:8). Different autoantibody assays will have different criteria for determining whether a given test is positive, negative, or indeterminate. Other laboratories ordered in the workup of autoimmune disease may include a white blood cell count (WBC), CRP (C-reactive protein), ESR (erythrocyte sedimentation rate), and C3/C4 (complement levels), among others.

Additional circumstantial evidence to indicate a likely autoimmune disease includes family history and clustering of autoimmune diseases within a given family, presence of HLA haplotypes associated with a given disease, sex bias, and proof-of-concept through response to immunosuppressive therapy.

Treatment

See also: Autoimmune disease § Treatment

See also: Pregnancy category

Currently, it is not possible to cure any autoimmune disease. However, treatments exist that can improve the course of a given disease and/or result in long periods of remission. Pharmacological treatment of autoimmune disease can be broadly classified into anti-inflammatory, immunosuppressive, and palliative – e.g., correcting a functional disturbance related to the condition. The overall goals of such treatment are to limit the severity of flare-ups of disease, as well as to limit the total number of flares – that is, to extend periods of disease remission.

Anti-inflammatory

Nonsteroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce inflammation associated with flares of autoimmune illness. NSAIDs work by inhibiting COX-1 and COX-2 enzymes, which are responsible for generating prostaglandins which cause inflammation. They additionally may inhibit chemotaxis, stop neutrophil aggregation, and decrease levels of pro-inflammatory cytokines. They are not considered immunosuppressive agents, as they do not directly target immune cells. Examples of NSAIDs include ibuprofen, naproxen, and diclofenac. These drugs are not recommended past the 20th week of pregnancy, as they may have adverse effects on the development of the fetal circulatory system and kidneys.

Corticosteroids

Corticosteroids also have both anti-inflammatory and immunosuppressive effects,^[59] and are used widely in the treatment of autoimmune disease. They work through promoting the synthesis of multiple proteins such as lipocortin-1 and annexin A1, which stop the downstream production of prostaglandins and leukotrienes which promote inflammation. Examples of corticosteroids used in autoimmune disease include prednisone and methylprednisolone. There are no robust randomized controlled studies in humans regarding the safety of corticosteroid use in pregnancy. Corticosteroid use may be associated with cleft palate formation in the 1st trimester, but the data on this is limited. There is little evidence to suggest that maternal corticosteroid use is associated with early delivery, low birth weight, or preeclampsia. Prednisone and methylprednisolone have been classed as pregnancy category C, in that they should only be used if the maternal benefits outweigh potential risks to the fetus.^[60]

Immunosuppressive

Optimal treatment of autoimmune disease addition to quelling the generalized inflammation which may occur with autoimmune disease, treatment is also focused on specifically targeting the adaptive immune system. The goal of direct immunosuppression is to treat flares as well as extend the period of remission between episodes. Immunosuppressive drugs are categorized into DMARDs (disease-modifying anti-rheumatic drugs), as well as

DMARDs (Disease-Modifying Anti-Rheumatic Drugs)

DMARDs can be further classified into conventional-synthetic, targeted-synthetic, and biologic agents.

Palliative

Some autoimmune diseases with targeted effects on endocrine organs can result in an inability to produce hormones necessary to maintain normal physiology. Palliative treatment of autoimmune disease involves treating the secondary condition by replacing vital hormones that are no longer being produced. Examples of this include the treatment of type-1 diabetes with exogenous insulin. Though this does not cure the primary autoimmune disease, it effectively treats the lack of hormone caused by it.

Non-pharmacological

Non-pharmacological treatments are effective in treating autoimmune disease and contribute to a sense of well-being. Women can:

• Eat healthy, well-balanced meals. A healthy diet limits saturated fat, trans fat, cholesterol, salt, and added sugars. People may alleviate symptoms of inflammation by following the Autoimmune Protocol Diet, which focuses on eliminating food that may trigger inflammation. Those with autoimmune diseases should focus on consuming foods that are very fresh and nutritious.
• Engage in regular physical activity without overdoing it. Patients should speak with a clinician about what types of physical activity are appropriate. A gradual and gentle exercise program often works well for people with long-lasting muscle and joint pain. For example, yoga or tai chi may be helpful.
• Get enough rest. Rest allows body tissues and joints the time they need to repair. Sleeping is a great way to maintain the health of the mind and body. Lack of sleep, along with elevated stress levels, may cause symptoms to worsen. Without proper rest, the body's immune defense remains inadequate. Many people need at least seven to nine hours of sleep each day to feel well-rested.
• Reduce stress. Stress and anxiety can trigger symptoms to flare up in some autoimmune diseases. Simplifying daily stressors will help alleviate symptoms and contribute to a sense of well-being. Meditation, self-hypnosis, and guided imagery may be effective in reducing stress, pain, and boosting people's ability to cope with other effects of autoimmune diseases. Instructional materials can guide people in learning these activities. Some include self-help books, audio sources, tapes, or consulting with an instructor. Joining a support group or talking with a counselor might also help manage stress and cope with the disease. As women are more likely to experience certain kinds stressors, such as in caregiver roles, or in balancing caregiver roles with workplace roles, they may be exposed to epigenetic presentation risks; thus, contribution to looking at each women's different roles and implementing stressor oriented strategies accordingly can aid in management.

Complementary

Some complementary treatments may be effective and include:

• Listening to music
• Taking time to relax in a comfortable position
• Using imagery throughout the day
• Imagining confronting the pain and watching it be destroyed.
• Journaling and daily affirmations
• Traditional herbal medicine

During pregnancy

Concerns about fertility and pregnancy are present in women with autoimmune diseases. Talking with a healthcare provider before becoming pregnant is recommended. They may suggest waiting until the disease is in remission or suggest a change in medication before becoming pregnant. There are endocrinologists that specialize in treating women with high-risk pregnancies.

Some women with autoimmune diseases may have problems getting pregnant. This can happen for many reasons, such as medication types or even disease types. Tests can tell if fertility problems are caused by an autoimmune disease or an unrelated reason. Fertility treatments can help some women with autoimmune disease become pregnant.

Changes in the severity of the disease seem to vary depending on the type of disease. There is an observable trend in pregnant women with rheumatoid arthritis, where the condition seems to improve during pregnancy. Differently, expecting mothers with systemic lupus erythematosus (SLE) may be more likely to have worsened symptoms through pregnancy; however, this is difficult to predict.

Certain medications can hinder women's ability to get pregnant, such as cyclophosphamide or corticosteroids. For this reason, it may be helpful for women with autoimmune diseases to seek treatment when conceiving.

Discrimination and disparities

Race and ethnicity

Varying forms of discrimination have been reported by women with autoimmune diseases of differing races and ethnicities. Black women diagnosed with lupus, when in healthcare settings, have reported the following conditions when interacting with healthcare providers: low compassion, low respect, racial discrimination, ethnic discrimination, hurried communication, distracted discrimination, as well as fears around being stereotyped, and not included in medical decisions. In a similar survey, fears around being given poor medical care, being exploited in research, and loosing legal status where recorded by Hispanic and Latinx men and women with multiple sclerosis. Women of color with autoimmune disease also report more interactions with providers around dismissal of symptoms.

Health outcomes can be impacted by the intersection of race, gender, and autoimmune diseases. Studies on women of color found heightened autoimmune disease activity after experiencing discrimination from healthcare providers.

In the area of treatment, factors related to race and ethnicity can also be noted. Medical care that takes into account research, and training in reducing differing discriminations, and building sensitivity to cultures, is able to account for the population at hand and recognize treatment factors that may be beneficial to the individual. This includes the mentioned and recorded fears, and also factors important to patients. For example, Caribbean black women diagnosed with multiple sclerosis are more likely to use faith related logic in their treatments and diagnosis than the general population.

Research gaps

In autoimmune disease research, there exist gaps for gender, and intersectional race and ethnicities. Historical, areas of research, including for medical conditions, have found their sample sizes with white males. In 2022, only 32 of 2305 federal funded research titles included the words "Sex, gender, female, maternal, or variations of women, pregnancy, or lactating", though women are disproportionately affected and diagnosed with autoimmune diseases. In 2024, a new race category that separated Arab individuals from Caucasians was added in federal research. Arabians have been shown to have higher rates of autoantibodies prevalent in autoimmune diseases, and due to this only recent separation lack research. In all, more gender focused research can be conducted and combined with race and ethnicity data to produce further research in identifying prevalences, gaps, treatment, and diagnosis, for women across a spectrum.

Workplace

The impact of workplace burden, stigma, discrimination have been studied in workplaces across various autoimmune diseases. Higher burden has been recorded in autoimmune conditions such as irritable bowel disease Inflammatory bowel disease (IBD), which includes higher burdens in the workplace due to needing more sick leave, disability support, and in some cases periods (such as with flair ups) unemployment. Women autoimmune diseases also face similar burdens as disability, even if temporary, can occur and limit working capacity and increase cognitive impairment, pain, fatigue, illness, as well as mental symptoms such as anxiety and depression.

Stigma can compound the stress of an autoimmune disease, and can be more pronounced when disability is more evident, and when the disease is more progressive. Fear of stigmatization, as a factor of stigma, can impact multiple factors in a workers life, such as preventing them from requesting accommodations, limiting them from bringing accommodations to work, fearing coworkers negatively changing perspectives, shift beliefs to thinking employers will choose other candidates for a position, and even install ideas that they may be fired for their condition. All of these factors in the fear of stigmatization have been seen to limit those with autoimmune diseases from seeking work, disclosing conditions, and have lead to leaving positions in anticipatory fear.

Improving workplace perceptions can improve conditions for those with autoimmune diseases. For some examples, stigma reducing workplace interventions, improved education about conditions, positive behaviors from coworkers and leaders, legal protections, and safety around trusting employees, can improve a workers feelings of safety and acceptance in the workplace. As many autoimmune conditions can be termed invisible illnesses, and have varied and fluctuating presentations impacting workers in different ways, increasing awareness about conditions and the physical and mental toll they take, not just visually, can improve employer and coworker perceptions.

Healthcare coverage and settings

From a financial perspective, women with autoimmune diseases differ from men. Compared to men, data shows out of pocket costs for healthcare are reported to be 15 billion dollars more women, employee sponsored insurance cover over a billion dollars less for women, and when insured over 30% of women reported healthcare aid to be insufficient for medical costs.  Not having insurance and/or low income can create barriers receiving treatments, and in turn create long term consequences as well impact daily quality of living for women.

In healthcare settings, 41% of rheumatologists are women and under 10% are a racial minority, creating contrast to the 80% of the women population treated by the specialty. Many women who are diagnosed with autoimmune disorders take years and multiple doctors to be diagnosed, which can be impacted by the nature of autoimmune diseases and provider interactions. Improved provider care interactions include listening and education when treating women to look for signs and symptoms. Providers can take measures of education in order to relate to their patients and the unique challenges they face in gender and ethnicity, improving treatment outcomes, progression, and feelings of comfort and safety.

Accommodations

Workplace accommodations

Autoimmune diseases, due to their ability to alter quality of life including day to day tasks- such as in the workplace- can require accommodations. Accommodations, though, are diverse in the same way presentations of autoimmune diseases are diverse. In the workplace, accommodations can include a spectrum from policies for invisible illnesses to mobility devices for progressive illnesses.

A list of possible accommodations includes:

• Telecommuting options.
• Flexible hours, starting times, and amount of workdays.
• Breaks when needed, such as in the case of IBD.
• Temperature and light control, such as for Lupus.
• Modifying tasks during flare ups, such as reducing repetitive movements in joint based autoimmune conditions.
• Pregnancy and childbirth accommodations which may trigger flare ups in conditions such as Lupus.
• Working with a vocational rehabilitation counselor

Cosmic string

From Wikipedia, the free encyclopedia

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

Cosmic strings are hypothetical 1-dimensional topological defects which may have formed during a symmetry-breaking phase transition in the early universe when the topology of the vacuum manifold associated to this symmetry breaking was not simply connected.

In less formal terms, they are hypothetical long, thin defects in the fabric of space. They might have formed in the early universe during a process where certain symmetries were broken. Their existence was first contemplated by the theoretical physicist Tom Kibble in the 1970s.

The formation of cosmic strings is somewhat analogous to the imperfections that form between crystal grains in solidifying liquids, or the cracks that form when water freezes into ice. The phase transitions leading to the production of cosmic strings are likely to have occurred during the earliest moments of the universe's evolution, just after cosmological inflation, and are a fairly generic prediction in both quantum field theory and string theory models of the early universe.

Theories containing cosmic strings

The prototypical example of a field theory with cosmic strings is the Abelian Higgs model. The quantum field theory and string theory cosmic strings are expected to have many properties in common, but more research is needed to determine the precise distinguishing features. The F-strings for instance are fully quantum-mechanical and do not have a classical definition, whereas the field theory cosmic strings are almost exclusively treated classically.

In superstring theory, the role of cosmic strings can be played by the fundamental strings (or F-strings) themselves that define the theory perturbatively, by D-strings which are related to the F-strings by weak-strong or so called S-duality, or higher-dimensional D-, NS- or M-branes that are partially wrapped on compact cycles associated to extra spacetime dimensions so that only one non-compact dimension remains.

Dimensions

Cosmic strings, if they exist, would be extremely thin topological defects with diameters of the same order of magnitude as that of a proton, i.e. ~1 fm, or smaller. Given that this scale is much smaller than any cosmological scale, these strings are often studied in the zero-width, or Nambu–Goto approximation. Under this assumption, strings behave as one-dimensional objects and obey the Nambu–Goto action, which is classically equivalent to the Polyakov action that defines the bosonic sector of superstring theory.

In field theory, the string width is set by the scale of the symmetry-breaking phase transition. In string theory, the string width is set (in the simplest cases) by the fundamental string scale, warp factors (associated to the spacetime curvature of an internal six-dimensional spacetime manifold) and/or the size of internal compact dimensions. (In string theory, the universe is either 10- or 11-dimensional, depending on the strength of interactions and the curvature of spacetime.)

Gravitation

A string is a geometrical deviation from Euclidean geometry in spacetime characterized by an angular deficit: a circle around the outside of a string would comprise a total angle less than 360°. From the general theory of relativity such a geometrical defect must be in tension, and would be manifested by mass. Even though cosmic strings are thought to be extremely thin, they would have immense density, and so would represent significant gravitational wave sources. A cosmic string about a kilometer in length may be more massive than the Earth.

However general relativity predicts that the gravitational potential of a straight string vanishes: there is no gravitational force on static surrounding matter. The only gravitational effect of a straight cosmic string is a relative deflection of matter (or light) passing the string on opposite sides (a purely topological effect). A closed cosmic string gravitates in a more conventional way.

During the expansion of the universe, cosmic strings would form a network of loops, and in the past it was thought that their gravity could have been responsible for the original clumping of matter into galactic superclusters. It is now calculated that their contribution to the structure formation in the universe is less than 10%.

Negative mass cosmic string

The standard model of a cosmic string is a geometrical structure with an angle deficit, which thus is in tension and hence has positive mass. In 1995, Visser et al. proposed that cosmic strings could theoretically also exist with angle excesses, and thus negative tension and hence negative mass. The stability of such exotic matter strings is problematic; however, they suggested that if a negative mass string were to be wrapped around a wormhole in the early universe, such a wormhole could be stabilized sufficiently to exist in the present day.

Super-critical cosmic string

The exterior geometry of a (straight) cosmic string can be visualized in an embedding diagram as follows: Focusing on the two-dimensional surface perpendicular to the string, its geometry is that of a cone which is obtained by cutting out a wedge of angle δ and gluing together the edges. The angular deficit δ is linearly related to the string tension (= mass per unit length), i.e. the larger the tension, the steeper the cone. Therefore, δ reaches 2π for a certain critical value of the tension, and the cone degenerates to a cylinder. (In visualizing this setup one has to think of a string with a finite thickness.) For even larger, "super-critical" values, δ exceeds 2π and the (two-dimensional) exterior geometry closes up (it becomes compact), ending in a conical singularity.

However, this static geometry is unstable in the super-critical case (unlike for sub-critical tensions): Small perturbations lead to a dynamical spacetime which expands in axial direction at a constant rate. The 2D exterior is still compact, but the conical singularity can be avoided, and the embedding picture is that of a growing cigar. For even larger tensions (exceeding the critical value by approximately a factor of 1.6), the string cannot be stabilized in radial direction anymore.

Realistic cosmic strings are expected to have tensions around 6 orders of magnitude below the critical value, and are thus always sub-critical. However, the inflating cosmic string solutions might be relevant in the context of brane cosmology, where the string is promoted to a 3-brane (corresponding to our universe) in a six-dimensional bulk.

Observational evidence

It was once thought that the gravitational influence of cosmic strings might contribute to the large-scale clumping of matter in the universe, but all that is known today through galaxy surveys and precision measurements of the cosmic microwave background (CMB) fits an evolution out of random, gaussian fluctuations. These precise observations therefore tend to rule out a significant role for cosmic strings and currently it is known that the contribution of cosmic strings to the CMB cannot be more than 10%.

The violent oscillations of cosmic strings generically lead to the formation of cusps and kinks. These in turn cause parts of the string to pinch off into isolated loops. These loops have a finite lifespan and decay (primarily) via gravitational radiation. This radiation which leads to the strongest signal from cosmic strings may in turn be detectable in gravitational wave observatories. An important open question is to what extent do the pinched off loops backreact or change the initial state of the emitting cosmic string—such backreaction effects are almost always neglected in computations and are known to be important, even for order of magnitude estimates.

Gravitational lensing of a galaxy by a straight section of a cosmic string would produce two identical, undistorted images of the galaxy. In 2003 a group led by Mikhail Sazhin reported the accidental discovery of two seemingly identical galaxies very close together in the sky, leading to speculation that a cosmic string had been found. However, observations by the Hubble Space Telescope in January 2005 showed them to be a pair of similar galaxies, not two images of the same galaxy. A cosmic string would produce a similar duplicate image of fluctuations in the cosmic microwave background, which it was thought might have been detectable by the Planck Surveyor mission. However, a 2013 analysis of data from the Planck mission failed to find any evidence of cosmic strings.

A piece of evidence supporting cosmic string theory is a phenomenon noticed in observations of the "double quasar" called Q0957+561A,B. Originally discovered by Dennis Walsh, Bob Carswell, and Ray Weymann in 1979, the double image of this quasar is caused by a galaxy positioned between it and the Earth. The gravitational lens effect of this intermediate galaxy bends the quasar's light so that it follows two paths of different lengths to Earth. The result is that we see two images of the same quasar, one arriving a short time after the other (about 417.1 days later). However, a team of astronomers at the Harvard-Smithsonian Center for Astrophysics led by Rudolph Schild studied the quasar and found that during the period between September 1994 and July 1995 the two images appeared to have no time delay; changes in the brightness of the two images occurred simultaneously on four separate occasions. Schild and his team believe that the only explanation for this observation is that a cosmic string passed between the Earth and the quasar during that time period traveling at very high speed and oscillating with a period of about 100 days.

Until 2023 the most sensitive bounds on cosmic string parameters came from the non-detection of gravitational waves by pulsar timing array data. The first detection of gravitational waves with pulsar timing array was confirmed in 2023. The earthbound Laser Interferometer Gravitational-Wave Observatory (LIGO) and especially the space-based gravitational wave detector Laser Interferometer Space Antenna (LISA) will search for gravitational waves and are likely to be sensitive enough to detect signals from cosmic strings, provided the relevant cosmic string tensions are not too small.

String theory and cosmic strings

During the early days of string theory both string theorists and cosmic string theorists believed that there was no direct connection between superstrings and cosmic strings (the names were chosen independently by analogy with ordinary string). The possibility of cosmic strings being produced in the early universe was first envisioned by quantum field theorist Tom Kibble in 1976, and this sprouted the first flurry of interest in the field.

In 1985, during the first superstring revolution, Edward Witten contemplated on the possibility of fundamental superstrings having been produced in the early universe and stretched to macroscopic scales, in which case (following the nomenclature of Tom Kibble) they would then be referred to as cosmic superstrings. He concluded that had they been produced they would have either disintegrated into smaller strings before ever reaching macroscopic scales (in the case of Type I superstring theory), they would always appear as boundaries of domain walls whose tension would force the strings to collapse rather than grow to cosmic scales (in the context of heterotic superstring theory), or having a characteristic energy scale close to the Planck energy they would be produced before cosmological inflation and hence be diluted away with the expansion of the universe and not be observable.

Much has changed since these early days, primarily due to the second superstring revolution. It is now known that string theory contains, in addition to the fundamental strings which define the theory perturbatively, other one-dimensional objects, such as D-strings, and higher-dimensional objects such as D-branes, NS-branes and M-branes partially wrapped on compact internal spacetime dimensions, while being spatially extended in one non-compact dimension. The possibility of large compact dimensions and large warp factors allows strings with tension much lower than the Planck scale.

Furthermore, various dualities that have been discovered point to the conclusion that actually all these apparently different types of string are just the same object as it appears in different regions of parameter space. These new developments have largely revived interest in cosmic strings, starting in the early 2000s.

In 2002, Henry Tye and collaborators predicted the production of cosmic superstrings during the last stages of brane inflation, a string theory construction of the early universe that gives leads to an expanding universe and cosmological inflation. It was subsequently realized by string theorist Joseph Polchinski that the expanding Universe could have stretched a "fundamental" string (the sort which superstring theory considers) until it was of intergalactic size. Such a stretched string would exhibit many of the properties of the old "cosmic" string variety, making the older calculations useful again. As theorist Tom Kibble remarks, "string theory cosmologists have discovered cosmic strings lurking everywhere in the undergrowth". Older proposals for detecting cosmic strings could now be used to investigate superstring theory.

Superstrings, D-strings or the other stringy objects mentioned above stretched to intergalactic scales would radiate gravitational waves, which could be detected using experiments like LIGO and especially the space-based gravitational wave experiment LISA. They might also cause slight irregularities in the cosmic microwave background, too subtle to have been detected yet but possibly within the realm of future observability.

Note that most of these proposals depend, however, on the appropriate cosmological fundamentals (strings, branes, etc.), and no convincing experimental verification of these has been confirmed to date. Cosmic strings nevertheless provide a window into string theory. If cosmic strings are observed, which is a real possibility for a wide range of cosmological string models, this would provide the first experimental evidence of a string theory model underlying the structure of spacetime.

Cosmic string network

There are many attempts to detect the footprint of a cosmic strings network.

Potential applications

In 1986, John G. Cramer proposed that spacecraft equipped with magnet coils could travel along cosmic strings, analogous to how a maglev train travels along a rail line.