Data integration

From Wikipedia, the free encyclopedia

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

Data integration involves combining data residing in different sources and providing users with a unified view of them. This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume (that is, big data) and the need to share existing data explodes. It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. Data integration encourages collaboration between internal as well as external users. The data being integrated must be received from a heterogeneous database system and transformed to a single coherent data store that provides synchronous data across a network of files for clients. A common use of data integration is in data mining when analyzing and extracting information from existing databases that can be useful for Business information.

History

Figure 1: Simple schematic for a data warehouse. The Extract, transform, load (ETL) process extracts information from the source databases, transforms it and then loads it into the data warehouse.

Figure 2: Simple schematic for a data-integration solution. A system designer constructs a mediated schema against which users can run queries. The virtual database interfaces with the source databases via wrapper code if required.

Issues with combining heterogeneous data sources are often referred to as information silos, under a single query interface have existed for some time. In the early 1980s, computer scientists began designing systems for interoperability of heterogeneous databases. The first data integration system driven by structured metadata was designed at the University of Minnesota in 1991, for the Integrated Public Use Microdata Series (IPUMS). IPUMS used a data warehousing approach, which extracts, transforms, and loads data from heterogeneous sources into a unique view schema so data from different sources become compatible. By making thousands of population databases interoperable, IPUMS demonstrated the feasibility of large-scale data integration. The data warehouse approach offers a tightly coupled architecture because the data are already physically reconciled in a single queryable repository, so it usually takes little time to resolve queries.

The data warehouse approach is less feasible for data sets that are frequently updated, requiring the extract, transform, load (ETL) process to be continuously re-executed for synchronization. Difficulties also arise in constructing data warehouses when one has only a query interface to summary data sources and no access to the full data. This problem frequently emerges when integrating several commercial query services like travel or classified advertisement web applications.

As of 2009 the trend in data integration favored the loose coupling of data and providing a unified query-interface to access real time data over a mediated schema (see Figure 2), which allows information to be retrieved directly from original databases. This is consistent with the SOA approach popular in that era. This approach relies on mappings between the mediated schema and the schema of original sources, and translating a query into decomposed queries to match the schema of the original databases. Such mappings can be specified in two ways: as a mapping from entities in the mediated schema to entities in the original sources (the "Global-as-View" (GAV) approach), or as a mapping from entities in the original sources to the mediated schema (the "Local-as-View" (LAV) approach). The latter approach requires more sophisticated inferences to resolve a query on the mediated schema, but makes it easier to add new data sources to a (stable) mediated schema.

As of 2010 some of the work in data integration research concerns the semantic integration problem. This problem addresses not the structuring of the architecture of the integration, but how to resolve semantic conflicts between heterogeneous data sources. For example, if two companies merge their databases, certain concepts and definitions in their respective schemas like "earnings" inevitably have different meanings. In one database it may mean profits in dollars (a floating-point number), while in the other it might represent the number of sales (an integer). A common strategy for the resolution of such problems involves the use of ontologies which explicitly define schema terms and thus help to resolve semantic conflicts. This approach represents ontology-based data integration. On the other hand, the problem of combining research results from different bioinformatics repositories requires bench-marking of the similarities, computed from different data sources, on a single criterion such as positive predictive value. This enables the data sources to be directly comparable and can be integrated even when the natures of experiments are distinct.

As of 2011 it was determined that current data modeling methods were imparting data isolation into every data architecture in the form of islands of disparate data and information silos. This data isolation is an unintended artifact of the data modeling methodology that results in the development of disparate data models. Disparate data models, when instantiated as databases, form disparate databases. Enhanced data model methodologies have been developed to eliminate the data isolation artifact and to promote the development of integrated data models. One enhanced data modeling method recasts data models by augmenting them with structural metadata in the form of standardized data entities. As a result of recasting multiple data models, the set of recast data models will now share one or more commonality relationships that relate the structural metadata now common to these data models. Commonality relationships are a peer-to-peer type of entity relationships that relate the standardized data entities of multiple data models. Multiple data models that contain the same standard data entity may participate in the same commonality relationship. When integrated data models are instantiated as databases and are properly populated from a common set of master data, then these databases are integrated.

Since 2011, data hub approaches have been of greater interest than fully structured (typically relational) Enterprise Data Warehouses. Since 2013, data lake approaches have risen to the level of Data Hubs. (See all three search terms popularity on Google Trends.) These approaches combine unstructured or varied data into one location, but do not necessarily require an (often complex) master relational schema to structure and define all data in the Hub.

Data integration plays a big role in business regarding data collection used for studying the market. Converting the raw data retrieved from consumers into coherent data is something businesses try to do when considering what steps they should take next. Organizations are more frequently using data mining for collecting information and patterns from their databases, and this process helps them develop new business strategies to increase business performance and perform economic analyses more efficiently. Compiling the large amount of data they collect to be stored in their system is a form of data integration adapted for Business intelligence to improve their chances of success.

Example

Consider a web application where a user can query a variety of information about cities (such as crime statistics, weather, hotels, demographics, etc.). Traditionally, the information must be stored in a single database with a single schema. But any single enterprise would find information of this breadth somewhat difficult and expensive to collect. Even if the resources exist to gather the data, it would likely duplicate data in existing crime databases, weather websites, and census data.

A data-integration solution may address this problem by considering these external resources as materialized views over a virtual mediated schema, resulting in "virtual data integration". This means application-developers construct a virtual schema—the mediated schema—to best model the kinds of answers their users want. Next, they design "wrappers" or adapters for each data source, such as the crime database and weather website. These adapters simply transform the local query results (those returned by the respective websites or databases) into an easily processed form for the data integration solution (see figure 2). When an application-user queries the mediated schema, the data-integration solution transforms this query into appropriate queries over the respective data sources. Finally, the virtual database combines the results of these queries into the answer to the user's query.

This solution offers the convenience of adding new sources by simply constructing an adapter or an application software blade for them. It contrasts with ETL systems or with a single database solution, which require manual integration of entire new data set into the system. The virtual ETL solutions leverage virtual mediated schema to implement data harmonization; whereby the data are copied from the designated "master" source to the defined targets, field by field. Advanced data virtualization is also built on the concept of object-oriented modeling in order to construct virtual mediated schema or virtual metadata repository, using hub and spoke architecture.

Each data source is disparate and as such is not designed to support reliable joins between data sources. Therefore, data virtualization as well as data federation depends upon accidental data commonality to support combining data and information from disparate data sets. Because of the lack of data value commonality across data sources, the return set may be inaccurate, incomplete, and impossible to validate.

One solution is to recast disparate databases to integrate these databases without the need for ETL. The recast databases support commonality constraints where referential integrity may be enforced between databases. The recast databases provide designed data access paths with data value commonality across databases.

Theory

The theory of data integration forms a subset of database theory and formalizes the underlying concepts of the problem in first-order logic. Applying the theories gives indications as to the feasibility and difficulty of data integration. While its definitions may appear abstract, they have sufficient generality to accommodate all manner of integration systems, including those that include nested relational / XML databases and those that treat databases as programs. Connections to particular databases systems such as Oracle or DB2 are provided by implementation-level technologies such as JDBC and are not studied at the theoretical level.

Definitions

Data integration systems are formally defined as a tuple $⟨G,S,M⟩$ where $G$ is the global (or mediated) schema, $S$ is the heterogeneous set of source schemas, and $M$ is the mapping that maps queries between the source and the global schemas. Both $G$ and $S$ are expressed in languages over alphabets composed of symbols for each of their respective relations. The mapping $M$ consists of assertions between queries over $G$ and queries over $S$. When users pose queries over the data integration system, they pose queries over $G$ and the mapping then asserts connections between the elements in the global schema and the source schemas.

A database over a schema is defined as a set of sets, one for each relation (in a relational database). The database corresponding to the source schema $S$ would comprise the set of sets of tuples for each of the heterogeneous data sources and is called the source database. Note that this single source database may actually represent a collection of disconnected databases. The database corresponding to the virtual mediated schema $G$ is called the global database. The global database must satisfy the mapping $M$ with respect to the source database. The legality of this mapping depends on the nature of the correspondence between $G$ and $S$. Two popular ways to model this correspondence exist: Global as View or GAV and Local as View or LAV.

Figure 3: Illustration of tuple space of the GAV and LAV mappings. In GAV, the system is constrained to the set of tuples mapped by the mediators while the set of tuples expressible over the sources may be much larger and richer. In LAV, the system is constrained to the set of tuples in the sources while the set of tuples expressible over the global schema can be much larger. Therefore, LAV systems must often deal with incomplete answers.

GAV systems model the global database as a set of views over $S$. In this case $M$ associates to each element of $G$ a query over $S$. Query processing becomes a straightforward operation due to the well-defined associations between $G$ and $S$. The burden of complexity falls on implementing mediator code instructing the data integration system exactly how to retrieve elements from the source databases. If any new sources join the system, considerable effort may be necessary to update the mediator, thus the GAV approach appears preferable when the sources seem unlikely to change.

In a GAV approach to the example data integration system above, the system designer would first develop mediators for each of the city information sources and then design the global schema around these mediators. For example, consider if one of the sources served a weather website. The designer would likely then add a corresponding element for weather to the global schema. Then the bulk of effort concentrates on writing the proper mediator code that will transform predicates on weather into a query over the weather website. This effort can become complex if some other source also relates to weather, because the designer may need to write code to properly combine the results from the two sources.

On the other hand, in LAV, the source database is modeled as a set of views over $G$. In this case $M$ associates to each element of $S$ a query over $G$. Here the exact associations between $G$ and $S$ are no longer well-defined. As is illustrated in the next section, the burden of determining how to retrieve elements from the sources is placed on the query processor. The benefit of an LAV modeling is that new sources can be added with far less work than in a GAV system, thus the LAV approach should be favored in cases where the mediated schema is less stable or likely to change.

In an LAV approach to the example data integration system above, the system designer designs the global schema first and then simply inputs the schemas of the respective city information sources. Consider again if one of the sources serves a weather website. The designer would add corresponding elements for weather to the global schema only if none existed already. Then programmers write an adapter or wrapper for the website and add a schema description of the website's results to the source schemas. The complexity of adding the new source moves from the designer to the query processor.

Query processing

The theory of query processing in data integration systems is commonly expressed using conjunctive queries and Datalog, a purely declarative logic programming language. One can loosely think of a conjunctive query as a logical function applied to the relations of a database such as "$f(A,B)$ where $A<B$". If a tuple or set of tuples is substituted into the rule and satisfies it (makes it true), then we consider that tuple as part of the set of answers in the query. While formal languages like Datalog express these queries concisely and without ambiguity, common SQL queries count as conjunctive queries as well.

In terms of data integration, "query containment" represents an important property of conjunctive queries. A query $A$ contains another query $B$ (denoted $A\supset B$) if the results of applying $B$ are a subset of the results of applying $A$ for any database. The two queries are said to be equivalent if the resulting sets are equal for any database. This is important because in both GAV and LAV systems, a user poses conjunctive queries over a virtual schema represented by a set of views, or "materialized" conjunctive queries. Integration seeks to rewrite the queries represented by the views to make their results equivalent or maximally contained by our user's query. This corresponds to the problem of answering queries using views (AQUV).

In GAV systems, a system designer writes mediator code to define the query-rewriting. Each element in the user's query corresponds to a substitution rule just as each element in the global schema corresponds to a query over the source. Query processing simply expands the subgoals of the user's query according to the rule specified in the mediator and thus the resulting query is likely to be equivalent. While the designer does the majority of the work beforehand, some GAV systems such as Tsimmis involve simplifying the mediator description process.

In LAV systems, queries undergo a more radical process of rewriting because no mediator exists to align the user's query with a simple expansion strategy. The integration system must execute a search over the space of possible queries in order to find the best rewrite. The resulting rewrite may not be an equivalent query but maximally contained, and the resulting tuples may be incomplete. As of 2011 the GQR algorithm is the leading query rewriting algorithm for LAV data integration systems.

In general, the complexity of query rewriting is NP-complete. If the space of rewrites is relatively small, this does not pose a problem — even for integration systems with hundreds of sources.

Medicine and Life Sciences

Large-scale questions in science, such as real world evidence, global warming, invasive species spread, and resource depletion, are increasingly requiring the collection of disparate data sets for meta-analysis. This type of data integration is especially challenging for ecological and environmental data because metadata standards are not agreed upon and there are many different data types produced in these fields. National Science Foundation initiatives such as Datanet are intended to make data integration easier for scientists by providing cyberinfrastructure and setting standards. The five funded Datanet initiatives are DataONE, led by William Michener at the University of New Mexico; The Data Conservancy, led by Sayeed Choudhury of Johns Hopkins University; SEAD: Sustainable Environment through Actionable Data, led by Margaret Hedstrom of the University of Michigan; the DataNet Federation Consortium, led by Reagan Moore of the University of North Carolina; and Terra Populus, led by Steven Ruggles of the University of Minnesota. The Research Data Alliance, has more recently explored creating global data integration frameworks. The OpenPHACTS project, funded through the European Union Innovative Medicines Initiative, built a drug discovery platform by linking datasets from providers such as European Bioinformatics Institute, Royal Society of Chemistry, UniProt, WikiPathways and DrugBank.

LGBT reproduction

From Wikipedia, the free encyclopedia

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

Diagram of the proposed method of lesbian egg fusion

LGBT reproduction refers to lesbian, gay, bisexual, and transgender (LGBT) people having biological children by means of assisted reproductive technology. It is distinct from LGBT parenting, which is a broader cultural phenomenon including LGBT adoption. In recent decades, developmental biologists have been researching and developing techniques to facilitate same-sex reproduction.

The obvious approaches, subject to a growing amount of activity, are female sperm and male eggs. In 2004, by altering the function of a few genes involved with imprinting, other Japanese scientists combined two mouse eggs to produce daughter mice and in 2018 Chinese scientists created 29 female mice from two female mice mothers but were unable to produce viable offspring from two father mice. One of the possibilities is obtaining sperm and eggs from skin stem cells.

Lack of access to assisted reproductive technologies has been seen as a form of healthcare inequality that faces LGBT people.

Gay men

Main articles: Male egg and Artificial womb

Some gay couples decide to have a surrogate pregnancy. A surrogate is a woman carrying an egg fertilized by sperm of one of the men. Some women become surrogates for money, others for humanitarian reasons or both. This allows one of the men to be the biological father while the other will be an adopted father.

Gay men who have become fathers using surrogacy have reported similar experiences to those as other couples who have used surrogacy, including their relationships both their child and their surrogate have.

There is theoretical work being done on creating a zygote from two men which would enable both men to be biological fathers, but it is yet to be practically implemented.

Barrie and Tony Drewitt-Barlow from the United Kingdom became the first gay men in the country to father twins born through surrogacy in 1999.

Lesbians

Main article: Female sperm

Partner-assisted reproduction, or co-IVF is a method of family building that is used by couples who both possess female reproductive organs. The method uses in vitro fertilization (IVF), a method that means eggs are removed from the ovaries, fertilized in a laboratory, and then one or more of the resulting embryos are placed in the uterus to hopefully create a pregnancy. Reciprocal IVF differs from standard IVF in that two women are involved: the eggs are taken from one partner, and the other partner carries the pregnancy. In this way, the process is mechanically identical to IVF with egg donation. Using this process ensures that each partner is a biological mother of the child according to advocates, but in the strictest sense only one mother is the biological mother from a genetic standpoint and the other is a surrogate mother. However the practice has a symbolic weight greater than LGBT adoption, and may create a stronger bond between mother and child than adoption.

In a 2019 study, quality of infant-parent relationships was examined among egg donor families in comparison to in vitro fertilization families. Infants were between the ages of 6–18 months. Through use of the Parent Development Interview (PDI) and observational assessment, the study found few differences between family types on the representational level, yet significant differences between family types on the observational level. Egg donation mothers were less sensitive and structuring than IVF mothers, and egg donation infants were less emotionally responsive, and involving than IVF infants.

There is theoretical work being done on creating a zygote from two women which would enable both women to be biological mothers, but it is yet to be practically implemented. Creating a sperm from an egg and using it to fertilize another egg may offer a solution to this issue, as could a process analogous to somatic cell nuclear transfer involving two eggs being fused together.

In 2004 and 2018 scientists were able to create mice with two mothers via egg fusion. Modification of genomic imprinting was necessary to create healthy bimaternal mice, while live bipaternal mice were created but were unhealthy likely due to genomic imprinting.

If created, a "female sperm" cell could fertilize an egg cell, a procedure that, among other potential applications, might enable female same-sex couples to produce a child who would be the biological offspring of their two mothers. It is also claimed that production of female sperm may stimulate a woman to be both the mother and father (similar to asexual reproduction) of an offspring produced by her own sperm. Many queries, both ethical and moral, arise over these arguments.

Transgender women

Main article: Transgender pregnancy

Many trans women want to have children. Some may seek to have children by using their own sperm and an egg donor or biological female partner. Fertility can be impeded in a variety of ways due to feminizing hormone therapy.

Trans women may have lower sperm quality before HRT, which may pose an issue for creating viable sperm samples to freeze.

Estrogens suppress testosterone levels and at high doses can markedly disrupt sex drive and function and fertility on their own. Moreover, disruption of gonadal function and fertility by estrogens may be permanent after extended exposure.

Nonsteroidal antiandrogens like bicalutamide may be an option for transgender women who wish to preserve sex drive, sexual function, and/or fertility, relative to antiandrogens that suppress testosterone levels and can greatly disrupt these functions such as cyproterone acetate and GnRH modulators. However, estrogens suppress testosterone levels and at high doses can markedly disrupt sex drive and function and fertility on their own. Moreover, disruption of gonadal function and fertility by estrogens may be permanent after extended exposure.

Some trans women want to carry their own children through transgender pregnancy, which has its own set of issues to be overcome, because transgender women do not naturally have the anatomy needed for embryonic and fetal development. As of 2008, there were no successful cases of uterus transplantation concerning a transgender woman.

Uterine transplantation, or UTx, is currently in its infancy and is not yet publicly available. As of 2019, in cisgender women, more than 42 UTx procedures had been performed, with 12 live births resulting from the transplanted uteruses as of publication. The International Society of Uterine Transplantation (ISUTx) was established internationally in 2016, with 70 clinical doctors and scientists, and currently has 140 intercontinental delegates. Its goal is to, "through scientific innovations, advance medical care in the field of uterus transplantation."

In 2012, McGill University published the "Montreal Criteria for the Ethical Feasibility of Uterine Transplantation", a proposed set of criteria for carrying out uterine transplants, in Transplant International. Under these criteria, only a cisgender woman could ethically be considered a transplant recipient. The exclusion of trans women from candidacy may lack justification.

In addition, if trans women wish to conceive with a biological male partner, they face the same issues that cisgender gay couples have in creating a zygote.

Only 3% of transgender people take efforts to preserve their fertility in transition 51% of trans women express regrets for not preserving their fertility, and 97% of transgender adults believe it should be discussed before transition.

Transfeminine lactation

See also: Male lactation and Colostrum

Lactation in trans women is an understudied area. A survey of trans healthcare providers found 34% met trans women who expressed interest in inducing lactation. The first documented instance of a trans woman attempting to breastfeed was in 2018 using domperidone to induce lactation. In 2021 lactation was successfully induced in a trans woman.

To induce lactation, domperidone is used at a dosage of 10 to 20 mg 3 or 4 times per day by mouth. Effects may be seen within 24 hours or may not be seen for 3 or 4 days. The maximum effect occurs after 2 or 3 weeks of treatment, and the treatment period generally lasts for 3 to 8 weeks.

Transgender men

Main article: Transgender pregnancy

Transgender men have a unique situation when it comes to LGBT reproduction as they are one of the only groups that has a risk of unintended pregnancy in a same-gender relationship (cisgender lesbians in relationships with fertile trans women being another example). Pregnancy is possible for transgender men who retain a functioning vagina, ovaries, and a uterus.

Testosterone therapy affects fertility, but many trans men who have become pregnant were able to do so within six months of stopping testosterone.  Another study conducted in 2019 found that transgender male patients seeking oocyte retrieval for either oocyte cryopreservation, embryo cryopreservation, or IVF were able to undergo treatment 4 months after stopping testosterone treatment, on average. All patients experienced menses and normal AMH, FSH, and E₂ levels and antral follicle counts after coming off testosterone which allowed for successful oocyte retrieval. Although the long-term effects of androgen treatment on fertility is still widely unknown, oocyte retrieval does not appear to be affected. Future pregnancies can be achieved by oophyte banking, but the process may increase gender dysphoria or may not be accessible due to lack of insurance coverage. Testosterone therapy is not a sufficient method of contraception, and trans men may experience unintended pregnancy, especially if they miss doses.

Many gay transgender men choose to freeze their eggs before transitioning, and choose to have a female surrogate carry their child while when the time comes, using their eggs and their cis male partner's sperm. This allows them to avoid the potentially dysphoria inducing experience of pregnancy, or cessation of HRT for collecting eggs at an older age.

Some studies report a higher incidence of PCOS among transgender men prior to taking testosterone, the disease causes infertility and can make it harder for trans men to freeze eggs, though not all have not found the same association of trans men and PCOS. People with PCOS in general are also reportedly more likely to see themselves as "sexually undifferentiated" or "androgynous" and "less likely to identify with a female gender scheme."

In popular culture

See also: Reproduction and pregnancy in speculative fiction

Male pregnancy is commonly explored in slash (homosexual) fan fiction, usually based upon fantasy series such as Supernatural or Harry Potter.

In the Omegaverse themes of LGBT reproduction are common. Alpha females are able to impregnate both males and females, and Omega males are able to be impregnated by both males and females.

Between Alphas and Betas, only females can carry on a pregnancy, but male Omegas are often envisaged as being able to become pregnant via an uterus connected to the rectum, and Alphas can impregnate regardless of their main gender. To make penetration and impregnation easier, male Omegas often have self-lubricating anuses.

Kleshas (Buddhism)

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Kleshas_(Buddhism) 

Kleshas (Sanskrit: क्लेश, romanized: kleśa; Pali: किलेस kilesa; Standard Tibetan: ཉོན་མོངས། nyon mongs), in Buddhism, are mental states that cloud the mind and manifest in unwholesome actions. Kleshas include states of mind such as anxiety, fear, anger, jealousy, desire, depression, etc. Contemporary translators use a variety of English words to translate the term kleshas, such as: afflictions, defilements, destructive emotions, disturbing emotions, negative emotions, mind poisons, and neuroses.

In the contemporary Mahayana and Theravada Buddhist traditions, the three kleshas of ignorance, attachment, and aversion are identified as the root or source of all other kleshas. These are referred to as the three poisons in the Mahayana tradition, or as the three unwholesome roots in the Theravada tradition.

While the early Buddhist texts of the Pali canon do not specifically enumerate the three root kleshas, over time the three poisons (and the kleshas generally) came to be seen as the very roots of samsaric existence.

Pali literature

In the Pali Canon's discourses (sutta), kilesa is often associated with the various passions that defile bodily and mental states. In the Pali Canon's Abhidhamma and post-canonical Pali literature, ten defilements are identified, the first three of which – greed, hate, delusion – are considered to be the "roots" of suffering.

Sutta Piṭaka: mental hindrances

In the Pali Canon's Sutta Piṭaka, kilesa and its correlate upakkilesa are affective obstacles to the pursuit of direct knowledge (abhijñā) and wisdom (pañña).

For instance, the Samyutta Nikaya includes a collection of ten discourses (SN 27, Kilesa-saṃyutta) that state that any association of "desire-passion" (chanda-rāgo) with the body or mind is a "defilement of mind" (cittasse'so upakkileso):

"Monks, any desire-passion with regard to the eye is a defilement of the mind. Any desire-passion with regard to the ear... the nose... the tongue... the body... the intellect is a defilement of the mind. When, with regard to these six bases, the defilements of awareness are abandoned, then the mind is inclined to renunciation. The mind fostered by renunciation feels malleable for the direct knowing of those qualities worth realizing."

More broadly, the five hindrances – sensual desire (kāmacchanda), anger (byāpāda), sloth-torpor (thīna-middha), restlessness-worry (uddhacca-kukkucca), and doubt (vicikicchā) – are frequently associated with kilesa in the following (or a similar) manner:

[A]ll those Blessed Ones had first abandoned the five hindrances, defilements of the mind that weaken wisdom ...

sabbe te bhagavanto pañcanīvaraṇe pahāya cetaso upakkilese paññāya dubbalīkaraṇe ... .

Additionally, in the Khuddaka Nikaya's Niddesa, kilesa is identified as a component of or synonymous with craving (taṇhā) and lust (rāga).

Abhidhamma: ten defilements and unwholesome roots

While the Sutta Pitaka does not offer a list of kilesa, the Abhidhamma Pitaka's Dhammasangani (Dhs. 1229ff.) and Vibhanga (Vbh. XII) as well as in the post-canonical Visuddhimagga (Vsm. XXII 49, 65) enumerate ten defilements (dasa kilesa-vatthūni) as follows:

1. greed (lobha)
2. hate (dosa)
3. delusion (moha)
4. conceit (māna)
5. wrong views (micchāditthi)
6. doubt (vicikicchā)
7. torpor (thīnaṃ)
8. restlessness (uddhaccaṃ)
9. shamelessness (ahirikaṃ)
10. recklessness (anottappaṃ)^[7]

The Vibhanga also includes an eightfold list (aṭṭha kilesa-vatthūni) composed of the first eight of the above ten.

Throughout Pali literature, the first three kilesa in the above tenfold Abhidhamma list (lobha dosa moha) are known as the "unwholesome roots" (akusala-mūla or the root of akusala); and, their opposites (alobha adosa amoha) are the three "wholesome roots" (kusala-mūla or the root of kusala). The presence of such a wholesome or unwholesome root during a mental, verbal or bodily action conditions future states of consciousness and associated mental factors (see Karma).

Visuddhimagga: round of defilements

12 Factors		3 Rounds
aging-death		aspects of vipāka (results)
↑
birth
↑		↑
becoming		kamma
↑		↑
clinging		kilesa
↑
craving
↑		↑
feeling		vipāka (results)
↑
contact
↑
sense bases
↑
name-form
↑
consciousness
↑		↑
formations		kamma
↑		↑
ignorance		kilesa
Figure: The "three rounds" of Dependent Origination (Vsm. XVII, 298).

In the 5th-century CE commentarial Visuddhimagga, in its discussion of "Dependent Origination" (Pali: paticca-samuppada) (Vsm. XVII), it presents different expository methods for understanding this teaching's twelve factors (nidana). One method (Vsm. XVII, 298) divides the twelve factors into three "rounds" (vaṭṭa):

• the "round of defilements" (kilesa-vaṭṭa)
• the "round of kamma" (kamma-vaṭṭa)
• the "round of results" (vipāka-vaṭṭa).^[12][13]

In this framework (see Figure to the right, starting from the bottom of the Figure), kilesa ("ignorance") conditions kamma ("formations") which conditions results ("consciousness" through "feelings") which in turn condition kilesa ("craving" and "clinging") which condition kamma ("becoming") and so on. Buddhaghosa (Vsm. XVII, 298) concludes:

So this Wheel of Becoming, having a triple round with these three rounds, should be understood to spin, revolving again and again, forever; for the conditions are not cut off as long as the round of defilements is not cut off.

As can be seen, in this framework, the round of defilements consists of:

• ignorance (avijjā)
• craving (taṇhā)
• clinging (ūpādānā).

Elsewhere in the Visuddhimagga (Vsm. XXII, 88), in the context of the four noble persons (ariya-puggala, see Four stages of enlightenment), the text refers to a precursor to the attainment of nibbana as being the complete eradication of "the defilements that are the root of the round" (vaṭṭa-mūla-kilesā).

Sanskrit Sravaka and Mahayana literature

Three poisons

Main article: Three poisons (Buddhism)

The three kleshas of ignorance, attachment and aversion are referred to as the three poisons (Skt. triviṣa) in the Mahayana tradition and as the three unwholesome roots (Pāli, akusala-mūla; Skt. akuśala-mūla ) in the Therevada tradition. These three poisons (or unwholesome roots) are considered to be the root of all the other kleshas.

Five poisons

In the Mahayana tradition, the five main kleshas are referred to as the five poisons (Sanskrit: pañca kleśaviṣa; Tibetan-Wylie: dug lnga).

The five poisons consist of the three poisons with two additional poisons: pride and jealousy. The five poisons are:

Poison/Klesha	Sanskrit	Pali	Tibetan	Description	Alternate translations
Ignorance	moha avidya	moha avijja	gti mug ma rig pa	Lack of discernment; not understanding the way of things	Confusion, bewilderment, delusion
Attachment	rāga	lobha	'dod chags	Attachment or desire for what we like	Desire, passion
Aversion	dvesha	dosa	zhe sdang	Aversion for what we don't like, or for what prevents us from getting what we like	Anger, hatred
Pride	māna	māna	nga rgyal	Having an inflated opinion of oneself and a disrespectful attitude toward others	Arrogance, Conceit
Envy	irshya	issā	phrag dog	Being unable to bear the accomplishments or good fortune of others	Jealousy

Six root kleshas of the Abhidharma

The Abhidharma-kośa identifies six root kleshas (mūlakleśa):

• Attachment (raga)
• Anger (pratigha)
• Ignorance (avidya)
• Pride/Conceit (māna)
• Doubt (vicikitsa)
• Wrong view/False view/Opinionatedness (dṛiṣṭi)

In the context of the Yogācāra school of Buddhism, Muller (2004: p. 207) states that the Six Klesha arise due to the "...reification of an 'imagined self' (Sanskrit: satkāya-dṛṣṭi)".

Mahaparinirvana Sutra

The Mahayana Mahaparinirvana Sutra lists approximately 50 kleshas, including those of attachment, aversion, stupidity, jealousy, pride, heedlessness, haughtiness, ill-will, quarrelsomeness, wrong livelihood, deceit, consorting with immoral friends, attachment to pleasure, to sleep, to eating, and to yawning; delighting in excessive talking and uttering lies, as well as thoughts of harm.

Two obscurations

Mahayana literature often features an enumeration of "two obscurations" (Wylie: sgrib gnyis), the "obscuration of conflicting emotions" (Sanskrit: kleśa-avaraṇa, Wylie: nyon-mongs-pa'i sgrib-ma) and the "obscuration concerning the knowable" (Sanskrit: jñeya-avaraṇa, Wylie: shes-bya'i sgrib-ma).

Contemporary glosses

Contemporary translators have used many different English words to translate the term kleshas, such as: afflictions, passions, destructive emotions, disturbing emotions, etc.

The following table provides brief descriptions of the term kleshas given by various contemporary Buddhist teachers and scholars:

English/Sanskrit term used	Description	Source
Afflictive emotions	... those mind states that cause suffering, such as depression, fear, hatred, anger, jealousy and so on – it's a long list!	Joseph Goldstein. The Emerging Western Buddhism: An Interview with Joseph Goldstein.
Afflictive emotions	In general, any defilement or emotion which obscures the mind. They are often summarized as three: ignorance, attachment and aversion. All other negative predispositions are produced on the basis of these three.	Khenchen Konchog Gyaltshen (2009). A Complete Guide to the Buddhist Path. p. 451 (from the glossary)
Afflictions	Mental factors that produce states of mental torment both immediately and in the long term. The five principal kleshas, which are sometimes called poisons, are attachment, aversion, ignorance, pride, and jealousy.	Longchen Yeshe Dorje (Kangyur Rinpoche) (2010). Treasury of Precious Qualities. p. 492 (from the glossary)
Conditioning Factors or Mental Afflictions	The processes that not only describe what we perceive, but also determine our responses.	Yongey Mingyur Rinpoche (2008). The Joy of Living. p. 115
Mental afflictions	In Tibetan a mental affliction is defined as a mental process that has the function of disrupting the equilibrium of the mind. They all have that in common, whether or not there is a strong emotional component to it.	Goleman, Daniel (2008). Destructive Emotions: A Scientific Dialogue with the Dalai Lama. Kindle Locations 2553–2555.
Destructive emotions	Fundamentally, a destructive emotion—which is also referred to as an ‘obscuring’ or ‘afflictive’ mental factor—is something that prevents the mind from ascertaining reality as it is. With a destructive emotion, there will always be a gap between the way things appear and the ways things are.	Goleman, Daniel (2008). Destructive Emotions: A Scientific Dialogue with the Dalai Lama. Kindle Locations 1779–1781.
Defilements	These are unskilful factors such as greed, hate, delusion, opinionatedness and lack of moral concern. Whereas the term ‘hindrance’ refers to five sticking points, ‘defilement’ is often used without any definite list, but to refer to any function of the mind which is led by unskilful factors.	Ajahn Sucitto (2011). Meditation, A Way of Awakening. Amaravati Publications. p. 263. (from the glossary)
Kleshas	Kleshas are the strong conflicting emotions that spin off and heighten when we get caught by aversion and attraction.	Pema Chodron. Signs of Spiritual Progress. Shambhala Sun.
Kleshas	Kleshas are properties that dull the mind and are the basis for all unwholesome actions. The three main kleshas are passion, aggression, and ignorance.	Chögyam Trungpa. The Truth of Suffering and the Path of Liberation. Edited by Judy L. Lief. Shambhala. p. 134 (from the glossary)
Kleshas	The basic idea is that certain powerful reactions have the capacity to take hold of us and drive our behavior. We believe in these reactions more than we believe in anything else, and they become the means by which we both hide from ourselves and attempt to cope with a world of ceaseless change and unpredictability. The three poisons of greed, hatred, and ignorance are the classic Buddhist examples, but others include conceit, skeptical doubt, and so-called "speculative" views ...	Mark Epstein. Going on Being: Buddhism and the Way of Change, a Positive Psychology for the West. http://www.quietspaces.com/kleshas.html
Kleshas	The emotional obscurations (in contrast to intellectual obscurations), usually translated as "poisons" or "defilements." The three main klesas are ignorance, hatred, and desire. The five klesas include these three along with pride and envy.	Thrangu Rinpoche (1993). The Practice of Tranquility & Insight: A Guide to Tibetan Buddhist Mediation (p. 152). Snow Lion. Kindle Edition. p. 152 (from the glossary)

Overcoming the kleshas

All Buddhist schools teach that through Tranquility (Samatha) meditation the kilesas are pacified, though not eradicated, and through Insight (Vipassana) the true nature of the kilesas and the mind itself is understood. When the empty nature of the Self and the Mind is fully understood, there is no longer a root for the disturbing emotions to be attached to, and the disturbing emotions lose their power to distract the mind.

Alternative translations

The term kleshas has been translated into English as:

• Afflictions
• Mental afflictions
• Mental disturbances
• Afflictive emotions
• Conditioning factors
• Destructive emotions
• Defiled emotions
• Defilements
• Dissonant emotions
• Disturbing emotions
• Disturbing emotions and attitudes
• Negative emotions
• Dissonant mental states
• Kleshas
• Passions
• Poisons
• Mind poisons
• Worldly desires