Mindset

From Wikipedia, the free encyclopedia

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

Mindset is an "established set of attitudes, esp. regarded as typical of a particular group's social or cultural values; the outlook, philosophy, or values of a person; (now also more generally) frame of mind, attitude, [recte: and] disposition." A mindset may also arise from a person's world view or philosophy of life.

A firmly established mindset could create a powerful incentive to adopt or accept prior behaviors, choices, or tools, sometimes referred to as cognitive inertia, or "groupthink." Within these phenomena, it is often difficult to counteract its effects upon analysis and decision making processes.

In cognitive psychology, a mindset represents the cognitive processes activated in response to a given task (French, 2016). According to French and Chang (2016), scholarly conceptualizations of mindset shift "to the varied definitions and conceptualizations" which "demarcates this literature via a novel categorization using the construct of mindset."

History of research

Some of the earliest empirical explorations of mindset originated in the 1900s with the work of psychology professor Peter M. Gollwitzer. These studies are identified as foundational and precursory for the study of cognition (Gollwitzer 1990, 2012). Gollwitzer's most notable contributions include his theory of mindset and the mindset theory of action phases (French, 2016).

In addition to the field of cognitive psychology, the study of mindset is evident within the social sciences and several other fields (e.g., positive psychology). A characteristic of this area of study, in various manifestations, is the fragment use of mindset throughout the academy (e.g. French, 2016).

In politics

A political example is the "Cold War mindset" prevalent in both the U.S. and USSR, which included absolute trust in two-player game theory, in the integrity of command chain, in control of nuclear materials, and in the mutual assured destruction of both in the case of war. This mindset usefully served to prevent an attack by either country; however, the assumptions underlying deterrence theory have made assessments of the efficacy of the Cold War mindset a matter of some controversy.

Most theorists consider that the key responsibility of an embedded power group is to challenge the assumptions that comprise the group's own mindset. According to these commentators, power groups that fail to review or revise their mindsets with sufficient regularity cannot hold power indefinitely, as a single mindset is unlikely to possess the flexibility and adaptability needed to address all future events. For example, the variations in mindset between Democratic Party and Republican Party presidents in the U.S. may have made that country more able to challenge assumptions than the Kremlin with its more static bureaucracy.

Modern military theory attempts to challenge entrenched mindsets in dealing with asymmetric warfare, terrorism, and the proliferation of weapons of mass destruction. In combination, these threats represent "a revolution in military affairs" and require very rapid adaptation to new threats and circumstances.^[7] In this context, the cost of not implementing adaptive mindsets cannot be afforded.

In system thinking

Building on Magoroh Maruyama's concept of Mindscape, Mindset Theory includes cultural and social orientation type values: Hierarchical Individualism (HI), Egalitarian Individualism (EI), Hierarchical Collectivism (HC), Egalitarian Collectivism (EC), Hierarchic Synergism (HS), Egalitarian Synergism (ES), Hierarchical Populism (HP), and Egalitarian Populism (EP).

Collective

Collective mindsets are described in Hutchin's "Cognition in the Wild" (1995) or Senges' "Knowledge entrepreneurship in universities" (2007). Hutchin analyzes a team of naval navigators as the cognitive unit or as a computational system, while Senges discusses the usefulness and functionality entrepreneurship concept by explaining how the concept is identified in the strategy and practice of universities (Senges, 2007).

There are also parallels to the emerging field of "collective intelligence" (e.g. (Zara, 2004)) and exploiting the "Wisdom of the crowds" (Surowiecki, 2005) of stakeholders. Zara notes that since collective reflection is more explicit, discursive, and conversational, it therefore needs a good ¿gestell?—especially when it comes to information and communication technology.

Erik H. Erikson's (1974) analysis of group-identities and what he calls a "life-plan" is relevant to the embodiment of a collective mindset. He recounts the example of American Indians, who were meant to undergo a reeducation process to instill a modern "life-plan" that aimed for housing and wealth. Erikson writes that the Indians' collective historic identity as buffalo hunters was oriented around such fundamentally different motivations that even communication about divergent "life plans" was difficult (Erikson, 1974).

There is a double relation between the institution embodying collective mindset. One example is the entrepreneurial mindset which refers to a person who "values uncertainty in the marketplace and seeks to continuously identify opportunities with the potential to lead to important innovations" (Hitt, 2011, pg. 371). An institution with an entrepreneurial philosophy will set entrepreneurial goals and strategies as a whole, but maybe even more importantly, it will foster an entrepreneurial milieu, allowing each entity to pursue emergent opportunities. A philosophical stance codified in the mind, as mindset, will lead to a climate that in turn causes values that lead to practice. Hitt's novel cites five dimensions of entrepreneurial mindset as "autonomy, innovativeness, risk taking, proactiveness, and competitive aggressiveness" (Hitt, 2011, pg. 354).

Specific theories

Types and theories

Variation within the study of mindsets includes how to define, measure, and conceptualize a mindset as well as the types of mindset identified. Substantial variations exist even among scholars within the same discipline, studying the same mindset (French, 2016). Nevertheless, any discussion of mindset should include recognition concerning the numerous, varied, and growing number of mindsets and mindset theories that receive attention in multiple disciplines throughout academia.

Mindset agency theory

Mindset theory, as based on the cultural values defined by Sagiv and Schwarts, explains the nature, functions, and variables that make up the characteristics of personality. The mindscape theory and cultural values outlined by Sagive and Schwarts combine to make a more comprehensive whole of mindset agency theory.

Mindscape theory

The Myer's–Briggs Type Indicator (MBTI) deals with psychological functions that, paired with values of social attitude, combine in certain ways to generate personality patterns called "types" that may be evaluated by exploring individual preferences (which change with context). Different is Maruyama's mindscape theory which works with epistemological types. Mindscapes seek to measure individuals on a scale of characteristics and place them into four categories of personalities that make up the population of the world. Each category contains differing views toward information, perception, logic, and ethics. Hierarchical Bureaucrats generally view the world as having natural order with competition and consequences much like natural selection. Independent Princes view the world as random, individualistic, and chaotic with a natural decay that is inevitable. Social Reformers view the world as a balance that can be maintained by symbiosis between everything. Generative Revolutionaries view the world as potential for growth through interaction and symbiosis; change is encouraged.

Sagiv-Schwarts cultural values

Sagiv and Schwarts posited three bi-polar dimensions to culture based on values. These dimensions contain opposites in the realms of cognitive, figurative, and operative values:

• Cognitive: embeddedness or autonomy
• Figurative: mastery or harmony
• Operative: hierarchy or egalitarian

Fixed and growth mindset

See also: Self-actualization § Abraham Maslow's concept of self-actualization

According to Carol Dweck, individuals can be placed on a continuum according to their implicit views of "where ability comes from". The two categorical extremes are fixed mindsets and growth mindsets. In particular, an individual's mindset impacts "motivation to practice and learn". A growth mindset is seen as more positive and helpful, with most research focusing on how to develop this mindset.

Fixed Mindset

Those with a fixed mindset believe "intelligence is static" and there is very little to be done to improve ability. Feedback is seen as an "evaluation of their underlying ability" since success is seen only as a result of this innate ability, not the effort put in. Failure is much more intimidating since it "suggests constraints or limits they would not be able to overcome". Those with a fixed mindset tend to avoid challenges, give up easily, and focus on the outcome. They believe that their talents and abilities are a fixed trait that they are either born with or not born with; thus, effort is not valued as worthwhile to the fixed mindset individual.

Growth Mindset

See also: Kaizen

Those with a growth mindset believe "intelligence can be developed" and their abilities can be enhanced through the learning process. With a growth mindset, individuals tend to embrace challenges, persevere in the face of adversity, accept and learn from failure, and focus on the process rather than the outcome. They see talents and abilities as skills that are developed through effort. Feedback and failure are seen as opportunities for increasing ability signaling the "need to pay attention, invest effort, apply time to practice, and master the new learning opportunity".

Grit closely relates to a growth mindset. Grit can be defined as the combination of determination and perseverance. Keown and Bourke discussed the importance for those growing up to have a combination of not only a growth mindset but also grit. Their 2019 study found that those in a lower economic status had a higher chance of success if they had a growth mindset and were willing to work hard through tribulation.

Classroom Implications

A large part of Dweck's research on mindsets was conducted in the field of education. This research was related to how mindsets affect a student's performance in the classroom. In order for students to effectively adopt a growth mindset, a classroom culture needs to be established that nurtures this type of thinking. One of the ways educators can do this is by creating a growth-mindset culture in their classroom that provides the right kind of praise and encouragement. According to Dweck (2010), "praising students for the process they have engaged in—the effort they applied, the strategies they used, the choices they made, the persistence they displayed, and so on—yields more long-term benefits than telling them they are 'smart' when they succeed". As such, it is important for educators to carefully craft and design meaningful learning activities for their students to engage in the classroom. Dweck (2010) states, "the teacher should portray challenges as fun and exciting, while portraying easy tasks as boring and less useful for the brain". Students who are engaged in more challenging learning activities have more opportunities to make mistakes and struggle, allowing the teacher to interject with new strategies to try while praising students for the work they have done so far.

A second strategy to promote a growth-mindset culture in the classroom is to explicitly teach lessons related to what it means to adopt a growth mindset. Possible activities include establishing personal goals and writing letters. Another practice for promoting growth mindset includes having students "write about and share with one another something they used to be poor at and now are very good at." An additional study by Hinda Hussein (2018), examined the effects of reflective journal writing on students' growth mindset. Findings indicated journaling could positively affect a student's learning process by improving their conceptual knowledge, promoting growth mindset, and enhancing understanding of their thoughts through writing.

The way educators evaluate their students' work and communicate their progress can also contribute to the establishment of a growth mindset culture in the classroom. Dweck (2010) identifies the word "yet" as a valuable tool to assess students' learning. If an educator hears students saying they are not good at something or can't do something, it is important for the teacher to interject with the words "not yet" to reinforce the idea that ability and motivation are fluid. Overall, a classroom that includes challenging learning tasks, praising of the process, and explicit growth mindset teaching and assessment, is a classroom where students will have the tools needed to become lifelong learners.

Reshaping mindsets in students and educators

While elements of personality – such as sensitivity to mistakes and setbacks – can make us predisposed toward holding a certain mindset, we are able to develop and reshape our mindset through our interactions. In multiple studies, Dweck and her colleagues noted that alterations in mindset could be achieved through "praising the process through which success was achieved", "having [college aged students] read compelling scientific articles that support one view or the other", or teaching junior high school students "that every time they try hard and learn something new, their brain forms new connections that, over time, make them smarter." These studies demonstrate how framing and discussing students' work and effort plays a considerable role in the type of mindset students develop and students' conceptions of their own ability.

While much of the research in the field of education focuses on a student's ability to adopt a growth mindset, less attention and focus are given to teachers' mindsets and the role they play in influencing their students. Hattie (2012) states, "differing mindsets, or assumptions, that teachers possess about themselves and their students play a significant role in determining their expectations, teaching practices, and how students perceive their own mindset."

A study by Patrick & Joshi (2019) explored the way teachers explain growth and fixed mindsets. Using 150 semi-structured interviews, two major findings were revealed. First, they found that teachers' prior beliefs about learning and learners influenced how they engaged with these mindsets. Secondly, they found that many teachers tended to oversimplify the concepts of a growth and fixed mindset into positive and negative traits. These findings suggest a need for more teacher training and support for teachers to successfully implement growth mindset initiatives in their classrooms.

An additional study conducted by Fiona S. Seaton (2018), looked specifically at the impact of teacher training aimed at influencing their mindsets and the effect on their resulting practice. The teachers in this study underwent six different training sessions. Seaton found that the training sessions had an impact on teachers' mindsets and that this change was sustained three months after the training. The results of this study suggest that adults' mindsets are malleable and can shift if the right supports are in place. This study also reinforces the bond between a teacher's own beliefs and how they can strongly influence the mindset of their students; therefore, further highlighting the need for proper teacher training in order for mindset initiatives within schools to be fully successful.

Fixed and growth mindsets in males vs. females

Carol Dweck and Jo Boaler have done extensive research on the topics of fixed and growth mindset, which indicates an existing disparity in the fixed and growth mindsets of females and males. Boaler's 2013 article "Ability and Mathematics: the Mindset Revolution that is Reshaping Education," argues that fixed mindset beliefs lead to inequalities in education which partially explains low achievement and participation amongst minorities and female students. Boaler builds on Carol Dweck's research to show that "gender differences in mathematics performance only existed among fixed mindset students" (Boaler, 2013).

Boaler and Dweck argue that people with growth mindsets can gain knowledge. Boaler said, "The key growth mindset message was that effort changes the brain by forming new connections, and that students control this process. The growth mindset intervention halted the students' decline in grades and started the students on a new pathway of improvement and high achievement" (Boaler, 2013, pg. 5). Educational systems focusing on creating a growth mindset environment allow girls to feel like their intelligence is malleable rather than constant.

In addition, L.S. Blackwell (2015) delivered research exploring if growth mindsets can be promoted within minority groups. Blackwell also builds on Dweck's research to observe minority groups and found that "students with a growth mindset had stronger learning goals than the fixed mindset students." These students also "had much more positive attitudes toward effort, agreeing that “when something is hard, it just makes me want to work more on it, not less.” Students with a fixed mindset, on the other hand, were more likely to say that “If you’re not good at a subject, working hard won’t make you good at it,” and “When I work hard at something, it makes me feel like I’m not very smart.” (Blackwell, 2015).

Implications for at risk students

Dweck's research on the theory of growth and fixed mindsets is useful in intervention strategies with at risk students, dispelling negative stereotypes in education held by teachers and students, understanding the impacts of self-theories on resilience, and understanding how process praise can foster a growth mindset and positively impact students' motivation levels. Other scholars have conducted further research building on Dweck's findings. In particular, a study by Rhew et al. (2018) suggested that a growth mindset intervention can increase the motivation levels of adolescent special education participants. Another study by Wang et al. (2019) suggested that substance use has adverse effects on adolescent reasoning. Developing a growth mindset in these adolescents was shown to reduce this adverse effect.

These studies further illustrate how educators can use intervention strategies, targeting a growth mindset, by allowing students to see that their behavior can be changed with effort.

Criticism

Further information: Carol Dweck § Criticism

In recent years, criticism has been directed at "growth mindset" as a concept, and related research. Moreau et al. (2019) suggest "that overemphasizing the malleability of abilities and other traits can have negative consequences for individuals, science, and society."

Benefit mindset

In 2015, Ashley Buchanan and Margaret L. Kern proposed a complementary evolution to the fixed and growth mindset called the benefit mindset. The benefit mindset describes society's everyday leaders who promote well-being on both an individual and a collective level. That is, people who discover their strengths to make valuable contributions to causes that are greater than the self. They question why they do what they do, positioning their actions within a purposeful context.

Buchanan and Kern argue "creating cultures of contribution and everyday leadership could be one of the best points of leverage we have for simultaneously bringing out the best in people, organizations and the planet."

Global mindset

Originating from the study of organizational leadership and coinciding with the growth of multinational corporations in the 1980s, organizations observed that the effectiveness of their executives did not necessarily translate cross-culturally. Global mindset emerged as an explanation (Javidan & Walker, 2013). Leaders in cross-cultural contexts were hypothesized to need an additional skill, ability, or proficiency (i.e. a global mindset) to enable effectiveness regardless of the culture or context (Perlmutter, 1969; Rhinesmith, 1992). Cultural agility refers to the changes implied in such need.

One of the defining characteristics of the study of global mindset is the variety in which scholars conceptualize and operationalize the construct. Yet, scholars typically agree that global mindset and its development increase global effectiveness for both individuals and organizations (French & Chang, 2016).

Abundance and scarcity

Main article: Abundance mindset

Those with abundance mindset believe that there are enough resources for everyone, seeing the glass half full. While those with the scarcity mindset believe that there is a limited number of resources, seeing the glass half empty. Mehta and Zhu (2012) found that a "scarcity mindset makes people think beyond established functionalities to explore broadly for solutions, thereby heightening creativity. In contrast, an abundance mindset induces functional fixedness, thereby reducing creativity."

Productive and defensive

According to Chris Argyris (2004), there are two dominant mindsets in organizations: the productive mindset and the defensive mindset. The productive mindset is hinged in logic and focuses on the knowledge and its certifiable results. This is more of a decision-making mindset which is transparent and auditable (Argyris, 2004).

The defensive mindset is a closed mindset like fixed mindset and is self-protective as well as self-deceptive. This mindset does not look at the greater good but centers on saving the skin of the person holding this mindset. It is highly likely that truth, if perceived harmful for the person concerned, would be shut down. This may allow personal growth but no organizational growth or development (Argyris, 2004).

Heritability of autism

From Wikipedia, the free encyclopedia

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

The heritability of autism is the proportion of differences in expression of autism that can be explained by genetic variation; if the heritability of a condition is high, then the condition is considered to be primarily genetic. Autism has a strong genetic basis, although the genetics of autism are complex and it is unclear whether autism spectrum disorder (ASD) is explained more by multigene interactions or by rare mutations with major effects.

Early studies of twins estimated the heritability of autism to be more than 90%; in other words, that 90% of the differences between autistic and non-autistic individuals are due to genetic effects. This however may be an overestimate: new twin data and models with structural genetic variation are needed. When only one identical twin is autistic, the other often has learning or social disabilities. For adult siblings, the likelihood of having one or more features of the broader autism phenotype might be as high as 30%, much higher than the likelihood in controls.

Genetic linkage analysis has been inconclusive; many association analyses have had inadequate power. For each autistic individual, mutations in more than one gene may be implicated. Mutations in different sets of genes may be involved in different autistic individuals. There may be significant interactions among mutations in several genes, or between the environment and mutated genes. By identifying genetic markers inherited with autism in family studies, numerous candidate genes have been located, most of which encode proteins involved in neural development and function. However, for most of the candidate genes, the actual mutations that increase the likelihood for autism have not been identified. Typically, autism cannot be traced to a Mendelian (single-gene) mutation or to single chromosome abnormalities such as fragile X syndrome or 22q13 deletion syndrome.

Deletion (1), duplication (2) and inversion (3) are all chromosome abnormalities that have been implicated in autism.

The large number of autistic individuals with unaffected family members may result from copy number variations (CNVs)—spontaneous alterations in the genetic material during meiosis that delete or duplicate genetic material. Sporadic (non-inherited) cases have been examined to identify candidate genetic loci involved in autism. A substantial fraction of autism may be highly heritable but not inherited: that is, the mutation that causes the autism is not present in the parental genome.

Although the fraction of autism traceable to a genetic cause may grow to 30–40% as the resolution of array CGH improves, several results in this area have been described incautiously, possibly misleading the public into thinking that a large proportion of autism is caused by CNVs and is detectable via array CGH, or that detecting CNVs is tantamount to a genetic diagnosis. The Autism Genome Project database contains genetic linkage and CNV data that connect autism to genetic loci and suggest that every human chromosome may be involved. It may be that using autism-related subphenotypes instead of the diagnosis of autism per se may be more useful in identifying susceptible loci.

Twin studies

Twin studies are a helpful tool in determining the heritability of disorders and human traits in general. They involve determining concordance of characteristics between identical (monozygotic or MZ) twins and between fraternal (dizygotic or DZ) twins. Possible problems of twin studies are: (1) errors in diagnosis of monozygocity, and (2) the assumption that social environment sharing by DZ twins is equivalent to that of MZ twins.

A condition that is environmentally caused without genetic involvement would yield a concordance for MZ twins equal to the concordance found for DZ twins. In contrast, a condition that is completely genetic in origin would theoretically yield a concordance of 100% for MZ pairs and usually much less for DZ pairs depending on factors such as the number of genes involved and assortative mating.

An example of a condition that appears to have very little if any genetic influence is irritable bowel syndrome (IBS), with a concordance of 28% vs. 27% for MZ and DZ pairs respectively. An example of a human characteristics that is extremely heritable is eye color, with a concordance of 98% for MZ pairs and 7–49% for DZ pairs depending on age.

Identical twin studies put autism's heritability in a range between 36% and 95.7%, with concordance for a broader phenotype usually found at the higher end of the range. Autism concordance in siblings and fraternal twins is anywhere between 0 and 23.5%. This is more likely 2–4% for classic autism and 10–20% for a broader spectrum. Assuming a general-population prevalence of 0.1%, the risk of classic autism in siblings is 20- to 40-fold that of the general population.

Notable twin studies have attempted to shed light on the heritability of autism.

A small scale study in 1977 was the first of its kind to look into the heritability of autism. It involved 10 DZ twins and 11 MZ twins in which at least one twin in each pair showed infantile autism. It found a concordance of 36% in MZ twins compared to 0% for DZ twins. Concordance of "cognitive abnormalities" was 82% in MZ pairs and 10% for DZ pairs. In 12 of the 17 pairs discordant for autism, a biological hazard was believed to be associated with the condition.

A 1979 case report discussed a pair of identical twins concordant for autism. The twins developed similarly until the age of 4, when one of them spontaneously improved. The other twin, who had had infrequent seizures, remained autistic. The report noted that genetic factors were not "all important" in the development of twins.

In 1985, a study of twins enrolled with the UCLA Registry for Genetic Studies found a concordance of 95.7% for autism in 23 pairs of MZ twins, and 23.5% for 17 DZ twins.

In a 1989 study, Nordic countries were screened for cases of autism. Eleven pairs of MZ twins and 10 of DZ twins were examined. Concordance of autism was found to be 91% in MZ and 0% in DZ pairs. The concordances for "cognitive disorder" were 91% and 30% respectively. In most of the pairs discordant for autism, the autistic twin had more perinatal stress.

A British twin sample was reexamined in 1995 and a 60% concordance was found for autism in MZ twins vs. 0% concordance for DZ. It also found 92% concordance for a broader spectrum in MZ vs. 10% for DZ. The study concluded that "obstetric hazards usually appear to be consequences of genetically influenced abnormal development, rather than independent aetiological factors."

A 1999 study looked at social cognitive skills in the general-population of children and adolescents. It found "poorer social cognition in males", and a heritability of 0.68 with higher genetic influence in younger twins.

In 2000, a study looked at reciprocal social behavior in general-population identical twins. It found a concordance of 73% for MZ, i.e. "highly heritable", and 37% for DZ pairs.

A 2004 study looked at 16 MZ twins and found a concordance of 43.75% for "strictly defined autism". Neuroanatomical differences (discordant cerebellar white and grey matter volumes) between discordant twins were found. The abstract notes that in previous studies 75% of the non-autistic twins displayed the broader phenotype.

Another 2004 study examined whether the characteristic symptoms of autism (impaired social interaction, communication deficits, and repetitive behaviors) show decreased variance of symptoms among monozygotic twins compared to siblings in a sample of 16 families. The study demonstrated significant aggregation of symptoms in twins. It also concluded that "the levels of clinical features seen in autism may be a result of mainly independent genetic traits."

An English twin study in 2006 found high heritability for autistic traits in a large group of 3,400 pairs of twins.

One critic of the pre-2006 twin studies said that they were too small and their results can be plausibly explained on non-genetic grounds.

Sibling studies

A study of 99 autistic probands which found a 2.9% concordance for autism in siblings, and between 12.4% and 20.4% concordance for a "lesser variant" of autism.

A study of 31 siblings of autistic children, 32 siblings of children with developmental delay, and 32 controls. It found that the siblings of autistic children, as a group, "showed superior spatial and verbal span, but a greater than expected number performed poorly on the set-shifting, planning, and verbal fluency tasks."

A 2005 Danish study looked at "data from the Danish Psychiatric Central Register and the Danish Civil Registration System to study some risk factors of autism, including place of birth, parental place of birth, parental age, family history of psychiatric disorders, and paternal identity." It found an overall prevalence rate of roughly 0.08%. Prevalence of autism in siblings of autistic children was found to be 1.76%. Prevalence of autism among siblings of children with Asperger syndrome or PDD was found to be 1.04%. The risk was twice as high if the mother had been diagnosed with a psychiatric disorder. The study also found that "the risk of autism was associated with increasing degree of urbanisation of the child's place of birth and with increasing paternal, but not maternal, age."

A study in 2007 looked at a database containing pedigrees of 86 families with two or more autistic children and found that 42 of the third-born male children showed autistic symptoms, suggesting that parents had a 50% chance of passing on a mutation to their offspring. The mathematical models suggest that about 50% of autistic cases are caused by spontaneous mutations. The simplest model was to divide parents into two risk classes depending on whether the parent carries a pre-existing mutation that causes autism; it suggested that about a quarter of autistic children have inherited a copy number variation from their parents.

Other family studies

A 1994 study looked at the personalities of parents of autistic children, using parents of children with Down syndrome as controls. Using standardized tests it was found that parents of autistic children were "more aloof, untactful and unresponsive" compared to parents whose children did not have autism.^[34]

A 1997 study found higher rates of social and communication deficits and stereotyped behaviors in families with multiple-incidence autism.

Autism was found to occur more often in families of physicists, engineers and scientists. 12.5% of the fathers and 21.2% of the grandfathers (both paternal and maternal) of children with autism were engineers, compared to 5% of the fathers and 2.5% of the grandfathers of children with other syndromes. Other studies have yielded similar results. Findings of this nature have led to the coinage of the term "geek syndrome".

A 2001 study of brothers and parents of autistic boys looked into the phenotype in terms of one current cognitive theory of autism. The study raised the possibility that the broader autism phenotype may include a "cognitive style" (weak central coherence) that can confer information-processing advantages.

A study in 2005 showed a positive correlation between repetitive behaviors in autistic individuals and obsessive-compulsive behaviors in parents. Another 2005 study focused on sub-threshold autistic traits in the general population. It found that correlation for social impairment or competence between parents and their children and between spouses is about 0.4.

A 2005 report examined the family psychiatric history of 58 subjects with Asperger syndrome (AS) diagnosed according to DSM-IV criteria. Three (5%) had first-degree relatives with AS. Nine (19%) had a family history of schizophrenia. Thirty five (60%) had a family history of depression. Out of 64 siblings, 4 (6.25%) were diagnosed with AS. According to a 2022 study held on 86 mother-child dyads across 18 months, "prior maternal depression didn’t predict child behavior problems later."

Twinning risk

It has been suggested that the twinning process itself is a risk factor in the development of autism, presumably due to perinatal factors. However, three large-scale epidemiological studies have refuted this idea. These studies took place in California, Sweden, and Australia. One study done in Western Australia, utilized the Maternal and Child Health Research Database that houses birth records for all infants born, including infants and later children diagnosed with autism spectrum disorder. During this study, the population analyzed for the incidence of Autism Spectrum Disorder was restricted to those children with birth years between 1980 and 1995. The focus was on the incidence of autism spectrum disorder in the twin population in comparison to the non twin population. The following two studies, explored the risk of Autism spectrum disorder in the twin population. They drew the same conclusion was drawn that the twinning process alone is not a risk factor. In these studies the data exemplified that both MZ twins will have autism spectrum disorder, but only one of the DZ twins will have autism spectrum disorder with an incidence rate of 90% in MZ twins compared to 0% in DZ twins. The high symmetry in MZ twins can explain the high symmetry of autism spectrum disorder in MZ twins outcome compared to DZ twins and non twin siblings.

Proposed models

Twin and family studies show that autism is a highly heritable condition, but they have left many questions for researchers, most notably

• Why is fraternal twin concordance so low considering that identical twin concordance is high?
• Why are parents of autistic children typically non-autistic?
• Which factors could be involved in the failure to find a 100% concordance in identical twins?
• Is profound intellectual disability a characteristic of the genotype or something totally independent?

Clues to the first two questions come from studies that have shown that at least 30% of individuals with autism have spontaneous de novo mutations that occurred in the father's sperm or mother's egg that disrupts important genes for brain development, these spontaneous mutations are likely cause autism in families where there is no family history. The concordance between identical twins isn't quite 100% for two reasons, because these mutations have variable 'expressivity' and their effects manifest differently due to chance effects, epigenetic, and environmental factors. Also spontaneous mutations can potentially occur specifically in one embryo and not the other after conception. The likelihood of developing intellectual disability is dependent on the importance of the gene to brain development and how the mutation changes this function, also playing a role is the genetic and environmental background upon which a mutation occurs. The recurrence of the same mutations in multiple individuals affected by autism has led Brandler and Sebat to suggest that the spectrum of autism is breaking up into quanta of many different genetic disorders.

Single genes

The most parsimonious explanation for cases of autism where a single child is affected and there is no family history or affected siblings is that a single spontaneous mutation that impacts one or multiple genes is a significant contributing factor. Tens of individual genes or mutations have been definitively identified and are cataloged by the Simons Foundation Autism Research Initiative. Examples of autism that has arisen from a rare or de novo mutation in a single-gene or locus include the neurodevelopmental disorders fragile X syndrome, 22q13 deletion syndrome, and 16p11.2 deletion syndrome.

These mutations themselves are characterized by considerable variability in clinical outcome and typically only a subset of mutation carriers meet criteria for autism. For example, carriers of the 16p11.2 deletion have a mean IQ 32 points lower than their first-degree relatives that do not carry the deletion, however only 20% are below the threshold IQ of 70 for intellectual disability, and only 20% have autism. Around 85% have a neurobehavioral diagnosis, including autism, ADHD, anxiety disorders, mood disorders, gross motor delay, and epilepsy, while 15% have no diagnosis. Alongside these neurobehavioral phenotypes, the 16p11.2 deletions / duplications have been associated with macrocephaly / microcephaly, body weight regulation, and the duplication in particular is associated with schizophrenia. Controls that carry mutations associated with autism or schizophrenia typically present with intermediate cognitive phenotypes or fecundity compared to neurodevelopmental cases and population controls. Therefore, a single mutation can have multiple different effects depending on other genetic and environmental factors.

Multigene interactions

In this model, autism often arises from a combination of common, functional variants of genes. Each gene contributes a relatively small effect in increasing the risk of autism. In this model, no single gene directly regulates any core symptom of autism such as social behavior. Instead, each gene encodes a protein that disrupts a cellular process, and the combination of these disruptions, possibly together with environmental influences, affect key developmental processes such as synapse formation. For example, one model is that many mutations affect MET and other receptor tyrosine kinases, which in turn converge on disruption of ERK and PI3K signaling.

Two family types

In this model most families fall into two types: in the majority, sons have a low risk of autism, but in a small minority their risk is near 50%. In the low-risk families, sporadic autism is mainly caused by spontaneous mutation with poor penetrance in daughters and high penetrance in sons. The high-risk families come from (mostly female) children who carry a new causative mutation but are unaffected and transmit the dominant mutation to grandchildren.

Epigenetic

Main article: Epigenetics of autism

Several epigenetic models of autism have been proposed. These are suggested by the occurrence of autism in individuals with fragile X syndrome, which arises from epigenetic mutations, and with Rett syndrome, which involves epigenetic regulation factors. An epigenetic model would help explain why standard genetic screening strategies have so much difficulty with autism.

Genomic imprinting

Genomic imprinting models have been proposed; one of their strengths is explaining the high male-to-female ratio in ASD. One hypothesis is that autism is in some sense diametrically opposite to schizophrenia and other psychotic-spectrum conditions, that alterations of genomic imprinting help to mediate the development of these two sets of conditions, and that ASD involves increased effects of paternally expressed genes, which regulate overgrowth in the brain, whereas schizophrenia involves maternally expressed genes and undergrowth.

Environmental interactions

Though autism's genetic factors explain most of autism risk, they do not explain all of it. A common hypothesis is that autism is caused by the interaction of a genetic predisposition and an early environmental insult. Several theories based on environmental factors have been proposed to address the remaining risk. Some of these theories focus on prenatal environmental factors, such as agents that cause birth defects; others focus on the environment after birth, such as children's diets. All known teratogens (agents that cause birth defects) related to the risk of autism appear to act during the first eight weeks from conception, strong evidence that autism arises very early in development. Although evidence for other environmental causes is anecdotal and has not been confirmed by reliable studies, extensive searches are underway.

Sex bias

Autism spectrum disorder effects all races, ethnicity, and socioeconomic groups. Still, more males than females are affected across all cultures, the ratios of males- to - females is appropriately 3 to 1. A study analyzed the Autism Genetics Resource Exchange (AGRE database), which holds resources, research, and records of autism spectrum disorder diagnosis From this study, it was concluded, that when spontaneous mutation cause autism spectrum disorder (ASD), there is high penetrance in males and low penetrance in females. A study in published 2020 explored the reason behind this idea further. It is known that the main difference between males and females is males have one X and one Y sex chromosome whereas female have two X chromosomes. This leads to the idea that there is a gene on the X chromosome that is not on the Y that is involved with the sex bias of ASD.

In another study, it has been found that the gene called NLGN4, when mutated, can cause ASD. This gene and other NLGNs gene are important for neuron communications. This NLGN4 gene is found on both the X (NLGN4X) and the Y (NLGN4Y) chromosome. The sex chromosomes are 97% identical. When this gene has been studied, most of the mutation that occur are on NLGN4X. Research into the difference between NLGN4X and NLGN4Y found that the NLGN4Y protein has poor surface expectations and poor synapses regulations, leading to poor neuron communication. Researchers concluded that males have a higher- incidences of autism when the mechanism is NLGN4X-associated. This association was concluded since females have two X chromosomes, if there is a mutation in a gene on a X chromosomes the other X chromosome can be used to compensate for mutation. Whereas males only have one X chromosome, meaning that if there is a mutation in a gene on a X chromosome, that that is the only copy of the gene had and it will be used. The genomic difference between males and females is one mechanism that leads to the higher incidence of ASD in males.

Candidate gene loci

Known genetic syndromes, mutations, and metabolic diseases account for up to 20% of autism cases. A number of alleles have been shown to have strong linkage to the autism phenotype. In many cases the findings are inconclusive, with some studies showing no linkage. Alleles linked so far strongly support the assertion that there is a large number of genotypes that are manifested as the autism phenotype. At least some of the alleles associated with autism are fairly prevalent in the general population, which indicates they are not rare pathogenic mutations. This also presents some challenges in identifying all the rare allele combinations involved in the etiology of autism.

A 2008 study compared genes linked with autism to those of other neurological diseases, and found that more than half of known autism genes are implicated in other disorders, suggesting that the other disorders may share molecular mechanisms with autism.

Primary

Gene	OMIM/#	Locus	Description
CDH9, CDH10		5p14.1	A 2009 pair of genome-wide association studies found an association between autism and six single-nucleotide polymorphisms in an intergenic region between CDH10 (cadherin 10) and CDH9 (cadherin 9). These genes encode neuronal cell-adhesion molecules, implicating these molecules in the mechanism of autism.
CDH8		16q21	A family based study identified a deletion of CDH8 that was transmitted to three out of three affected children and zero out of four unaffected siblings. Further evidence for the role of CDH8 comes from a spontaneous 1.52 megabase inversion that disrupts the gene in an affected child.
MAPK3		16p11.2	A 2008 study observed a de novo deletion of 593 kb on this chromosome in about 1% of persons with autism, and similarly for the reciprocal duplication of the region. Another 2008 study also found duplications and deletions associated with ASD at this locus. This gene encodes ERK1, one of the extracellular signal regulated kinase subfamily of mitogen-activated protein kinases which are central elements of an intracellular signaling pathways that transmits signals from cell surfaces to interiors. 1% of autistic children have been found to have either a loss or duplication in a region of chromosome 16 that encompasses the gene for ERK1. A similar disturbance in this pathway is also found in neuro-cardio-facial-cutaneous syndromes (NCFC), which are characterized by cranio-facial development disturbances that also can be found in some cases of autism.
SERT (SLC6A4)		17q11.2	This gene locus has been associated with rigid-compulsive behaviors. Notably, it has also been associated with depression but only as a result of social adversity, although other studies have found no link. Significant linkage in families with only affected males has been shown. Researchers have also suggested that the gene contributes to hyperserotonemia. However, a 2008 meta-analysis of family- and population-based studies found no significant overall association between autism and either the promoter insertion/deletion (5-HTTLPR) or the intron 2 VNTR (STin2 VNTR) polymorphisms.
CACNA1G		17q21.33	Markers within an interval containing this gene are associated with ASD at a locally significant level. The region likely harbors a combination of multiple rare and common alleles that contribute to genetic risk for ASD.
GABRB3, GABRA4		multiple	GABA is the primary inhibitory neurotransmitter of the human brain. Ma et al. (2005) concluded that GABRA4 is involved in the etiology of autism, and that it potentially increases autism risk through interaction with GABRB1. The GABRB3 gene has been associated with savant skills. The GABRB3 gene deficient mouse has been proposed as a model of ASD.
EN2		7q36.2	Engrailed 2 is believed to be associated with cerebellar development. Benayed et al.. (2005) estimate that this gene contributes to as many as 40% of ASD cases, about twice the prevalence of the general population. But at least one study has found no association.
?		3q25-27	A number of studies have shown a significant linkage of autism and Asperger syndrome with this locus. The most prominent markers are in the vicinity of D3S3715 and D3S3037.
RELN		7q21-q36	In adults, Reelin glycoprotein is believed to be involved in memory formation, neurotransmission, and synaptic plasticity. A number of studies have shown an association between the REELIN gene and autism, but a couple of studies were unable to duplicate linkage findings.
SLC25A12		2q31	This gene encodes the mitochondrial aspartate/glutamate carrier (AGC1). It has been found to have a significant linkage to autism in some studies, but linkage was not replicated in others, and a 2007 study found no compelling evidence of an association of any mitochondrial haplogroup in autism.
HOXA1 and HOXB1		multiple	A link has been found between HOX genes and the development of the embryonic brain stem. In particular, two genes, HOXA1 and HOXB1, in transgenic 'knockout' mice, engineered so that these genes were absent from the genomes of the mice in question, exhibited very specific brain stem developmental differences from the norm, which were directly comparable to the brain stem differences discovered in a human brain stem originating from a diagnosed autistic patient. Conciatori et al.. (2004) found an association of HOXA1 with increased head circumference. A number of studies have found no association with autism. The possibility remains that single allelic variants of the HOXA1 gene are insufficient alone to trigger the developmental events in the embryo now associated with autistic spectrum conditions. Tischfield et al.. published a paper which suggests that because HOXA1 is implicated in a wide range of developmental mechanisms, a model involving multiple allelic variants of HOXA1 in particular may provide useful insights into the heritability mechanisms involved. Additionally, Ingram et al.. alighted upon additional possibilities in this arena. Transgenic mouse studies indicate that there is redundancy spread across HOX genes that complicate the issue, and that complex interactions between these genes could play a role in determining whether or not a person inheriting the requisite combinations manifests an autistic spectrum condition—transgenic mice with mutations in both HOXA1 and HOXB1 exhibit far more profound developmental anomalies than those in which only one of the genes differs from the conserved 'norm'. In Rodier's original work, teratogens are considered to play a part in addition, and that the possibility remains open for a range of teratogens to interact with the mechanisms controlled by these genes unfavourably (this has already been demonstrated using valproic acid, a known teratogen, in the mouse model).
PRKCB1		16p11.2	Philippi et al. (2005) found a strong association between this gene and autism. This is a recent finding that needs to be replicated.
TAOK2		16p11.2	Richter et al. (2018) found a strong association between this gene and autism.
MECP2	300496, AUTSX3	Xq28	Mutations in this gene can give rise to autism spectrum disorders and related postnatal neurodevelopmental disorders.
UBE3A		15q11.2–q13	The maternally expressed imprinted gene UBE3A has been associated with Angelman syndrome. MeCP2 deficiency results in reduced expression of UBE3A in some studies.
SHANK3 (ProSAP2)		22q13	The gene called SHANK3 (also designated ProSAP2) regulates the structural organization of neurotransmitter receptors in post-synaptic dendritic spines making it a key element in chemical binding crucial to nerve cell communication. SHANK3 is also a binding partner of chromosome 22q13 (i.e. a specific section of Chromosome 22) and neuroligin proteins; deletions and mutations of SHANK3, 22q13 (i.e. a specific section of Chromosome 22) and genes encoding neuroligins have been found in some people with autism spectrum disorders. Mutations in the SHANK3 gene have been strongly associated with the autism spectrum disorders. If the SHANK3 gene is not adequately passed to a child from the parent (haploinsufficiency) there will possibly be significant neurological changes that are associated with yet another gene, 22q13, which interacts with SHANK3. Alteration or deletion of either will effect changes in the other. A deletion of a single copy of a gene on chromosome 22q13 has been correlated with global developmental delay, severely delayed speech or social communication disorders and moderate to profound delay of cognitive abilities. Behavior is described as "autistic-like" and includes high tolerance to pain and habitual chewing or mouthing (see also 22q13 deletion syndrome). This appears to be connected to the fact that signal transmission between nerve cells is altered with the absence of 22q13. SHANK3 proteins also interact with neuroligins at the synapses of the brain further complicating the widespread effects of changes at the genetic level and beyond.
NLGN3	300425, AUTSX1	Xq13	Neuroligin is a cell surface protein (homologous to acetylcholinesterase and other esterases) that binds to synaptic membranes. Neuroligins organize postsynaptic membranes that function to transmit nerve cell messages (excitatory) and stop those transmissions (inhibitory); In this way, neuroligins help to ensure signal transitions between nerve cells. Neuroligins also regulate the maturation of synapses and ensure there are sufficient receptor proteins on the synaptic membrane. Mice with a neuroligin-3 mutation exhibit poor social skills but increased intelligence. Though not present in all individuals with autism, these mutations hold potential to illustrate some of the genetic components of spectrum disorders. However, a 2008 study found no evidence for involvement of neuroligin-3 and neuroligin-4x with high-functioning ASD.
MET		7q31	The MET gene (MET receptor tyrosine kinase gene) linked to brain development, regulation of the immune system, and repair of the gastrointestinal system, has been linked to autism. This MET gene codes for a protein that relays signals that turn on a cell's internal machinery. Impairing the receptor's signaling interferes with neuron migration and disrupts neuronal growth in the cerebral cortex and similarly shrinks the cerebellum—abnormalities also seen in autism. It is also known to play a key role in both normal and abnormal development, such as cancer metastases. A mutation of the gene, rendering it less active, has been found to be common amongst children with autism. Mutation in the MET gene demonstrably raises risk of autism by 2.27 times.
NRXN1		2q32	In February 2007, researchers in the Autism Genome Project (an international research team composed of 137 scientists in 50 institutions) reported possible implications in aberrations of a brain-development gene called neurexin 1 as a cause of some cases of autism. Linkage analysis was performed on DNA from 1,181 families in what was the largest-scale genome scan conducted in autism research at the time. The objective of the study was to locate specific brain cells involved in autism to find regions in the genome linked to autism susceptibility genes. The focus of the research was copy number variations (CNVs), extra or missing parts of genes. Each person does not actually have just an exact copy of genes from each parent. Each person also has occasional multiple copies of one or more genes or some genes are missing altogether. The research team attempted to locate CNVs when they scanned the DNA. Neurexin 1 is one of the genes that may be involved in communication between nerve cells (neurons). Neurexin 1 and other genes like it are very important in determining how the brain is connected from cell to cell, and in the chemical transmission of information between nerve cells. These genes are particularly active very early in brain development, either in utero or in the first months or couple of years of life. In some families their autistic child had only one copy of the neurexin 1 gene. Besides locating another possible genetic influence (the findings were statistically insignificant), the research also reinforced the theory that autism involves many forms of genetic variations. A 2008 study implicated the neurexin 1 gene in two independent subjects with ASD, and suggested that subtle changes to the gene might contribute to susceptibility to ASD. A Neurexin 1 deletion has been observed occurring spontaneously in an unaffected mother and was passed on to an affected child, suggesting that the mutation has incomplete penetrance.
CNTNAP2		7q35-q36	Multiple 2008 studies have identified a series of functional variants in the CNTNAP2 gene, a member of the neurexin superfamily, that implicate it as contributing to autism.
FOXP2		7q31	The FOXP2 gene is of interest because it is known to be associated with developmental language and speech deficits. A 2008 study found that FOXP2 binds to and down-regulates CNTNAP2, and that the FOXP2-CNTNAP2 pathway links distinct syndromes involving disrupted language.
GSTP1		11q13	A 2007 study suggested that the GSTP1*A haplotype of the glutathione S-transferase P1 gene (GSTP1) acts in the mother during pregnancy and increases the likelihood of autism in the child.
PRL, PRLR, OXTR		multiple	A 2014 meta-analysis found significant associations between autism and several single-nucleotide polymorphisms in the OXTR gene.

Others

There is a large number of other candidate loci which either should be looked at or have been shown to be promising. Several genome-wide scans have been performed identifying markers across many chromosomes.

A few examples of loci that have been studied are the 17q21 region, the 3p24-26 locus, PTEN, 15q11.2–q13 and deletion in the 22q11.2 area.

Homozygosity mapping in pedigrees with shared ancestry and autism incidence has recently implicated the following candidate genes: PCDH10, DIA1 (formerly known as C3ORF58), NHE9, CNTN3, SCN7A, and RNF8. Several of these genes appeared to be targets of MEF2, one of the transcription factors known to be regulated by neuronal activity and that itself has also recently been implicated as an autism-related disorder candidate gene.

Cryptocurrency and crime

From Wikipedia, the free encyclopedia

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

Cryptocurrency and crime describes notable examples of cybercrime related to theft (or the otherwise illegal acquisition) of cryptocurrencies and some of the methods or security vulnerabilities commonly exploited. Cryptojacking is a form of cybercrime specific to cryptocurrencies that has been used on websites to hijack a victim's resources and use them for hashing and mining cryptocurrencies.

According to blockchain analysis company Chainalysis, cryptocurrency transactions that they were certain involved illicit activities like cybercrime, money laundering and terrorism financing made up 0.15% of transactions conducted in 2021, representing a total of $14 billion.

Background

For broader coverage of this topic, see Cryptocurrency wallet.

There are various types of cryptocurrency wallets available, with different layers of security, including devices, software for different operating systems or browsers, and offline wallets.

Novel exploits unique to blockchain transactions exist which aim to create unintended outcomes for those on the other end of a transaction. One of the more well known issues that opens the possibility for exploits on Bitcoin is the transaction malleability problem.

Notable thefts

In 2018, around US$1.7 billion in cryptocurrency was lost to scams, theft and fraud. In the first quarter 2019, the amount of such losses rose to US$1.2 billion.

Exchanges

Notable cryptocurrency exchange compromises resulting in the loss of cryptocurrencies include:

• In 2015, cryptocurrencies worth US$5 million were stolen from Bitstamp.
• Between 2011 and 2014, US$350 million worth of bitcoin were stolen from Mt. Gox.
• In 2016, US$72 million were stolen through exploiting Bitfinex's exchange wallet, users were refunded.
• On December 7, 2017, Slovenian cryptocurrency exchange Nicehash reported that hackers had stolen over $70M using a hijacked company computer.
• On December 19, 2017, Yapian, the owner of South Korean exchange Youbit, filed for bankruptcy after suffering two hacks that year. Customers were still granted access to 75% of their assets.
• In 2018, cryptocurrencies worth US$400 million were stolen from Coincheck.
• In May 2018, Bitcoin Gold had its transactions hijacked and abused by unknown hackers. Exchanges lost an estimated $18m and Bitcoin Gold was delisted from Bittrex after it refused to pay its share of the damages.
• In June 2018, South Korean exchange Coinrail was hacked, losing over $37M worth of cryptos. The hack worsened an already ongoing cryptocurrency selloff by an additional $42 billion.
• On July 9, 2018, the exchange Bancor, whose code and fundraising had been subjects of controversy, had $23.5 million in cryptocurrency stolen.
• Zaif US$60 million in Bitcoin, Bitcoin Cash and Monacoin stolen in September 2018
• Binance In 2019 cryptocurrencies worth US$40 million were stolen.
• Africrypt founders are suspected of absconding in June 2021 with US$3.6 billion worth of Bitcoin
• PolyNetwork (DeFi) suffered the loss of US$611 million in a theft in August 2021.
• Japanese cryptocurrency exchange Liquid was compromised in August 2021 resulting in a loss of US$97 million worth of digital coins
• Cream Finance were subject to a US$29 million theft in August, 2021 and $130 million October 28, 2021.
• On December 2, 2021, users of the BadgerDAO DeFi lost around $118,500,000 worth of bitcoin and $679,000 worth of ethereum tokens in a front-end attack. A compromised API key of the Cloudflare content delivery network account allowed injecting of a malicious script to the web interface. BadgerDAO "paused" all smart contracts due to user complaints.
• On December 6, 2021, the cryptocurrency exchange BitMart lost around $135M worth of Ethereum and an estimate of around $46 million in other cryptocurrencies due to a breach of two of its wallets. Although BitMart stated that it would reimburse its clients, many BitMart clients have not received any money from the exchange as of January 2022.
• On December 12, 2021, users of VulcanForge lost around $135M worth of PYR due to breaches of multiple wallets. Partering centralized exchanges had been notified of the hack and they have pledged to seize any stolen funds upon deposit.
• On January 27, 2022, Qubit Finance (DeFi) lost around $80M worth of Binance Coin due to a flaw in the smart contract that enabled withdrawal of the said amount in exchange for a deposit of 0 ETH.
• On September 20, 2022, Wintermute was hacked $160M due to a technical flaw

Wallets

The Parity Wallet has had two security incidents amounting to 666,773 ETH lost or stolen. In July 2017, due to a bug in the multisignature code, 153,037 ETH (approximately US$32 million at the time) were stolen. In November 2017, a subsequent multisignature flaw in Parity led to a lock-up of 513,774 Ether (about US$150 million at the time) to be unreachable; as of March 2019, the funds were still frozen.

Energy

Notable cases of electricity theft to mine proof-of-work cryptocurrencies include:

• In February 2021 Malaysian police arrested six men involved in a Bitcoin mining operation which had stolen US$2 million in electricity
• Ukraine authorities shutdown an underground gaming and cryptocurrency farm in July, 2021, accused of stealing $259,300 of electricity each month
• In July 2021 Malaysian authorities destroyed 1,069 cryptocurrency mining systems accused of stealing electricity from the grid
• In May, 2021 UK authorities closed a suspected bitcoin mine after Western Power Distribution found an illegal connection to the electricity supply

Blockchains

Bitcoin

There have been many cases of bitcoin theft. As of December 2017, around 980,000 bitcoins—over five percent of all bitcoin in circulation—had been lost on cryptocurrency exchanges.

One type of theft involves a third party accessing the private key to a victim's bitcoin address, or of an online wallet. If the private key is stolen, all the bitcoins from the compromised address can be transferred. In that case, the network does not have any provisions to identify the thief, block further transactions of those stolen bitcoins, or return them to the legitimate owner.

Theft also occurs at sites where bitcoins are used to purchase illicit goods. In late November 2013, an estimated US$100 million in bitcoins were allegedly stolen from the online illicit goods marketplace Sheep Marketplace, which immediately closed. Users tracked the coins as they were processed and converted to cash, but no funds were recovered and no culprits identified. A different black market, Silk Road 2, stated that during a February 2014 hack, bitcoins valued at $2.7 million were taken from escrow accounts.

Sites where users exchange bitcoins for cash or store them in "wallets" are also targets for theft. Inputs.io, an Australian wallet service, was hacked twice in October 2013 and lost more than $1 million in bitcoins. GBL, a Chinese bitcoin trading platform, suddenly shut down on 26 October 2013; subscribers, unable to log in, lost up to $5 million worth of bitcoin. In late February 2014 Mt. Gox, one of the largest virtual currency exchanges, filed for bankruptcy in Tokyo amid reports that bitcoins worth US$350 million had been stolen. Flexcoin, a bitcoin storage specialist based in Alberta, Canada, shut down in March 2014 after saying it discovered a theft of about $650,000 in bitcoins. Poloniex, a digital currency exchange, reported in March 2014 that it lost bitcoins valued at around $50,000. In January 2015 UK-based bitstamp, the third busiest bitcoin exchange globally, was hacked and US$5 million in bitcoins were stolen. February 2015 saw a Chinese exchange named BTER lose bitcoins worth nearly $2 million to hackers.

A major bitcoin exchange, Bitfinex, was hacked and nearly 120,000 bitcoins (around US$60 million) was stolen in 2016. Bitfinex was forced to suspend its trading. The theft was the second largest bitcoin heist ever, dwarfed only by the Mt. Gox theft in 2014. According to Forbes, "All of Bitfinex's customers,... will stand to lose money. The company has announced a cut of 36.067% across the board." Following the hack the company refunded customers. In 2022, the US government recovered 94,636 bitcoin (worth approximately $3.6 billion at time of recovery) from the 2016 thefts of the Bitfinex exchange. By 2022, the amount of bitcoin stolen was worth $4.5 billion. Two people were charged for the thefts.

On May 7, 2019, hackers stole over 7000 Bitcoins from the Binance Cryptocurrency Exchange, at a value of over 40 million US dollars. Binance CEO Zhao Changpeng stated: "The hackers used a variety of techniques, including phishing, viruses and other attacks.... The hackers had the patience to wait, and execute well-orchestrated actions through multiple seemingly independent accounts at the most opportune time."

Thefts have raised safety concerns. Charles Hayter, founder of digital currency comparison website CryptoCompare said, "It's a reminder of the fragility of the infrastructure in such a nascent industry."^[54] According to the hearing of U.S. House of Representatives Committee on Small Business on April 2, 2014, "these vendors lack regulatory oversight, minimum capital standards and don't provide consumer protection against loss or theft."

Ethereum

See also: Ethereum § The DAO event

In 2016, known as the DAO event, an exploit in the original Ethereum smart contracts resulted in multiple transactions, creating additional US$50 million. Subsequently, the currency was forked into Ethereum Classic, and Ethereum, with the latter continuing with the new blockchain without the exploited transactions.

On November 21, 2017, Tether announced that it had been hacked, losing $31 million in USDT from its core treasury wallet. The company has 'tagged' the stolen currency, hoping to 'lock' them in the hacker's wallet (making them unspendable).

In 2022, hackers created a signature account on a blockchain bridge called "Wormhole" and stole more than $300M worth of ether.

Fraud

Josh Garza, who founded the cryptocurrency startups GAW Miners and ZenMiner in 2014, acknowledged in a plea agreement that the companies were part of a pyramid scheme, and pleaded guilty to wire fraud in 2015. The U.S. Securities and Exchange Commission separately brought a civil enforcement action against Garza, who was eventually ordered to pay a judgment of US$9.1 million plus $700,000 in interest. The SEC's complaint stated that Garza, through his companies, had fraudulently sold "investment contracts representing shares in the profits they claimed would be generated" from mining.

Following its shut-down, in 2018 a class action lawsuit for $771,000 was filed against the cryptocurrency platform known as BitConnect, including the platform promoting YouTube channels. Prior fraud warnings in regards to BitConnect, and cease-and-desist orders by the Texas State Securities Board cited the promise of massive monthly returns.

BitConnect founder and promoters diverted $2 billion in investor funds into personally controlled digital wallets between 2017 and 2018, according to the US Securities and Exchange Commission. The scam purported to use a "crypto trading bot" for a guaranteed return on investment. In reality no such mechanism was implemented and a network of promoters were paid a commission to attract new investors. Lead promotor, Glenn Arcaro, pled guilty to criminal charges.

OneCoin was a massive world-wide multi-level marketing Ponzi scheme promoted as (but not involving) a cryptocurrency, causing losses of US$4 billion worldwide. Several people behind the scheme were arrested in 2018 and 2019.

The cryptocurrency community refers to pre-mining, hidden launches, ICO or extreme rewards for the altcoin founders as deceptive practices. This is at times an inherent part of the cryptocurrency's design. Pre-mining refers to the practice of generating the currency before its released to the public.

Malware

Malware attacks

Some malware can steal private keys for bitcoin wallets allowing the bitcoins themselves to be stolen. The most common type searches computers for cryptocurrency wallets to upload to a remote server where they can be cracked and their coins stolen. Many of these also log keystrokes to record passwords, often avoiding the need to crack the keys. A different approach detects when a bitcoin address is copied to a clipboard and quickly replaces it with a different address, tricking people into sending bitcoins to the wrong address. This method is effective because bitcoin transactions are irreversible.

One virus, spread through the Pony botnet, was reported in February 2014 to have stolen up to $220,000 in cryptocurrencies including bitcoins from 85 wallets. Security company Trustwave, which tracked the malware, reports that its latest version was able to steal 30 types of digital currency.

A type of Mac malware active in August 2013, Bitvanity posed as a vanity wallet address generator and stole addresses and private keys from other bitcoin client software. A different trojan for macOS, called CoinThief was reported in February 2014 to be responsible for multiple bitcoin thefts. The software was hidden in versions of some cryptocurrency apps on Download.com and MacUpdate.

Ransomware

Many types of ransomware demand payment in bitcoin. One program called CryptoLocker, typically spread through legitimate-looking email attachments, encrypts the hard drive of an infected computer, then displays a countdown timer and demands a ransom in bitcoin, to decrypt it. Massachusetts police said they paid a 2 bitcoin ransom in November 2013, worth more than $1,300 at the time, to decrypt one of their hard drives. Bitcoin was used as the ransom medium in the WannaCry ransomware. One ransomware variant disables internet access and demands credit card information to restore it, while secretly mining bitcoins.

As of June 2018, most ransomware attackers preferred to use currencies other than bitcoin, with 44% of attacks in the first half of 2018 demanding Monero, which is highly private and difficult to trace, compared to 10% for bitcoin and 11% for Ether.

Unauthorized mining

In June 2011, Symantec warned about the possibility that botnets could mine covertly for bitcoins. Malware used the parallel processing capabilities of GPUs built into many modern video cards. Although the average PC with an integrated graphics processor is virtually useless for bitcoin mining, tens of thousands of PCs laden with mining malware could produce some results.

In mid-August 2011, bitcoin mining botnets were detected, and less than three months later, bitcoin mining trojans had infected Mac OS X.

In April 2013, electronic sports organization E-Sports Entertainment was accused of hijacking 14,000 computers to mine bitcoins; the company later settled the case with the State of New Jersey.

German police arrested two people in December 2013 who customized existing botnet software to perform bitcoin mining, which police said had been used to mine at least $950,000 worth of bitcoins.

For four days in December 2013 and January 2014, Yahoo! Europe hosted an ad containing bitcoin mining malware that infected an estimated two million computers. The software, called Sefnit, was first detected in mid-2013 and has been bundled with many software packages. Microsoft has been removing the malware through its Microsoft Security Essentials and other security software.

Several reports of employees or students using university or research computers to mine bitcoins have been published.

On February 20, 2014, a member of the Harvard community was stripped of his or her access to the University's research computing facilities after setting up a Dogecoin mining operation using a Harvard research network, according to an internal email circulated by Faculty of Arts and Sciences Research Computing officials.

Ars Technica reported in January 2018 that YouTube advertisements contained JavaScript code that mined the cryptocurrency Monero.

Phishing

A phishing website to generate private IOTA wallet seed passphrases, collected wallet keys, with estimates of up to US$4 million worth of MIOTA tokens stolen. The malicious website operated for an unknown amount of time, and was discovered in January 2018.

Other incidents

In 2015, two members of the Silk Road Task Force—a multi-agency federal task force that carried out the U.S. investigation of Silk Road—were convicted over charges pertaining to corruption. Former DEA agent, Carl Mark Force, had attempted to extort Silk Road founder Ross Ulbricht ("Dread Pirate Roberts") by faking the murder of an informant. He pleaded guilty to money laundering, obstruction of justice, and extortion under color of official right, and was sentenced to 6.5 years in a federal prison. Former U.S. Secret Service agent, Shaun Bridges, pleaded guilty to crimes relating to his diversion of $800,000 worth of bitcoins to his personal account during the investigation, and also separately pleaded guilty to money laundering in connection to another cryptocurrency theft. Bridges was sentenced to almost eight years in federal prison.

Gerald Cotten founded QuadrigaCX in 2013, after graduating from the Schulich school of Business in Toronto. Cotten was acting as the sole curator of the exchange. Quadriga had no official bank accounts, since banks at the time had no method of managing cryptocurrency. In late 2018, Canada's largest crypto exchange QuadrigaCX lost US$190 million in cryptocurrency when the owner died; he was the only one with knowledge of the password to a storage wallet. The exchange filed for bankruptcy in 2019.

Michael Terpin, the founder and chief executive officer of Transform Group, a San Juan, Puerto Rico-based company that advises blockchain businesses on public relations and communications, sued Ellis Pinsky in New York on May 7, 2020, for leading a "sophisticated cybercrime spree" that stole US$24 million in cryptocurrency by hacking into Terpin's phone in 2018. Terpin also sued Nicholas Truglia and won a $75.8 million judgment against Truglia in 2019 in California state court.

On July 15, 2020, Twitter accounts of prominent personalities and firms, including Joe Biden, Barack Obama, Bill Gates, Elon Musk, Jeff Bezos, Apple, Kanye West, Michael Bloomberg and Uber were hacked. Twitter confirmed that it was a coordinated social engineering attack on their own employees. Twitter released its statement six hours after the attack took place. Hackers posted the message to transfer the Bitcoin in a Bitcoin wallet, which would double the amount. The wallet's balance was expected to increase to more than $100,000 as the message spread among the Twitter followers.

In 2022, the Federal Trade Commission reported that $139 million in cryptocurrency was stolen by romance scammers in 2020. Some scammers targeted dating apps with fake profiles.

In early 2022, the Beanstalk cryptocurrency was stripped of its reserves, which were valued at more than US$180 million, after attackers had managed to use borrowed US$80 million in cryptocurrency to buy enough voting rights to transfer the reserves to their own accounts outside the system. It was initially unclear, if such an exploit of governance procedures was illegal.