Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The Alcohol Use Disorders Identification Test (AUDIT) has been developed from a six-country WHO collaborative project as a screening instrument for hazardous and harmful alcohol consumption. It is a 10-item questionnaire which covers the domains of alcohol consumption, drinking behaviour, and alcohol-related problems. Questions were selected from a 150-item assessment schedule (which was administered to 1888 persons attending representative primary health care facilities) on the basis of their representativeness for these conceptual domains and their perceived usefulness for intervention. Responses to each question are scored from 0 to 4, giving a maximum possible score of 40. Among those diagnosed as having hazardous or harmful alcohol use, 92% had an AUDIT score of 8 or more, and 94% of those with non-hazardous consumption had a score of less than 8. AUDIT provides a simple method of early detection of hazardous and harmful alcohol use in primary health care settings and is the first instrument of its type to be derived on the basis of a cross-national study.

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Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption-II

So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.

Complex brain networks: graph theoretical analysis of structural and functional systems

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

It is now incontrovertible that heavy use of cannabis increases the risk of psychosis. There is a dose-response relationship and high potency preparations and synthetic cannabinoids carry the greatest risk. It would be wise to await the outcome of the different models of legalisation that are being introduced in North America, before deciding whether or not to follow suit. Declaration of interest None.

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Cannabis and psychosis: what do we know and what should we do?

The alpha7 nicotinic acetylcholine receptor gene (CHRNA7) is located at 15q13-q14 in a region that is strongly linked to the P50 sensory gating deficit, an endophenotype of schizophrenia and bipolar disorder. Part of the gene is a copy number variant, due to a duplication of exons 5-10 and 3' sequence in CHRFAM7A, which is present in many but not all humans. Maps of this region show that the two genes are in opposite orientation in the individual mainly represented in the public access human DNA sequence database (Build 36), suggesting that an inversion had occurred since the duplication. We have used fluorescent in situ hybridization to investigate this putative inversion. Analysis of interphase chromosomes in 12 individuals confirms the occurrence of an inversion and indicates that CHRFAM7A exists in both orientations with similar frequency. We showed that the 2 bp deletion polymorphism in exon 6 of CHRFAM7A is in strong linkage disequilibrium with the inversion polymorphism (r(2)=0.82, CI 0.53-1.00, P=0.00003), which can therefore be used as a surrogate marker. Previous associations of endophenotypes of schizophrenia with the 2 bp deletion might therefore be due to the orientation of the duplicon containing CHRFAM7A.

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The copy number variant involving part of the alpha7 nicotinic receptor gene contains a polymorphic inversion.

Childhood schizotypy and positive symptoms in schizophrenic patients predict schizotypy in relatives.

1. Diagnosing Schizophrenia 2. Pathobiology of Schizophrenia: Implications for Clinical Management and Treatment 3. The Outcome of Psychotic Illness 4. Pharmacological Treatment of Schizophrenia 5. Cognitive-behavioural Therapy 6. Rehabilitative Treatment of Schizophrenia 7. Clinician Interactions with Patients and Families 8. Case Management of Patients with Schizophrenia 9. Childhood- and Adolescent-onset Schizophrenia 10. The Detection and Optimal Management of Early Psychosis 11. Chronic Schizophrenia 12. Treatment-resistant Schizophrenia 13. Affective Symptoms in Schizophrenia 14. Suicidal Behavior in Schizophrenia 15. Violent Patients: Managing Acute Disturbance 16. The Long-term Treatment of Violent Patients 17. Substance Abuse Comorbidity 18. Issues Affecting Women with Schizophrenia 19. Patient Compliance 20. Medical Management of Persons with Schizophrenia 21. Ethnic Influences in Schizophrenia: Pharmacogenetics 22. Cultural Influences in Schizophrenia 23. Systems of Care for Persons with Schizophrenia in Different Countries 24. Economic Perspectives on the Treatment of Schizophrenia 25. First Person Accounts 26. Curing Schizophrenia, Treating Schizophrenia: Translating Research into Practice 27. Index

Comprehensive care of schizophrenia : a textbook of clinical management

Previously we reported evidence for genetic linkage between markers on chromosome 22q12 and schizophrenia in 23 multiply affected pedigrees. As part of further investigation of this region, we have applied the transmission disequilibrium test (TDT) to the genotype data. The TDT is a test for both linkage and linkage disequilibrium, and is based on the unequal probability of transmission of two different marker alleles from parents to affected offspring. We obtained significant results for the marker D22S283 (chi 2 = 35.9, 14 df, p = 0.001), D22S278 (chi 2 = 16.5, 6 df, p = 0.01) and F8VWFP (chi 2 = 13.1, 4 df, p = 0.01). Application of the Bonferonni correction for testing multiple markers renders the results for marker D22S278 and F8VWFP non-significant (from p = 0.01 to p = 0.14), while the result for D22S283 remains modestly significant at p < 0.02. Overall, our findings strengthen the suggestion that the region around D22S283 contains a susceptibility gene for schizophrenia.

Robin M. Murray

Papers

Cannabis and psychosis: what do we know and what should we do?

The copy number variant involving part of the alpha7 nicotinic receptor gene contains a polymorphic inversion.

Childhood schizotypy and positive symptoms in schizophrenic patients predict schizotypy in relatives.

Comprehensive care of schizophrenia : a textbook of clinical management

Chromosome 22 markers demonstrate transmission disequilibrium with schizophrenia.