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

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Autophagy is a process by which components of the cell are degraded to maintain essential activity and viability in response to nutrient limitation. Extensive genetic studies have shown that the yeast ATG1 kinase has an essential role in autophagy induction. Furthermore, autophagy is promoted by AMP activated protein kinase (AMPK), which is a key energy sensor and regulates cellular metabolism to maintain energy homeostasis. Conversely, autophagy is inhibited by the mammalian target of rapamycin (mTOR), a central cell-growth regulator that integrates growth factor and nutrient signals. Here we demonstrate a molecular mechanism for regulation of the mammalian autophagy-initiating kinase Ulk1, a homologue of yeast ATG1. Under glucose starvation, AMPK promotes autophagy by directly activating Ulk1 through phosphorylation of Ser 317 and Ser 777. Under nutrient sufficiency, high mTOR activity prevents Ulk1 activation by phosphorylating Ulk1 Ser 757 and disrupting the interaction between Ulk1 and AMPK. This coordinated phosphorylation is important for Ulk1 in autophagy induction. Our study has revealed a signalling mechanism for Ulk1 regulation and autophagy induction in response to nutrient signalling.

/pdf/ampk-and-mtor-regulate-autophagy-through-direct-14wqsz16ke.pdf

AMPK and mTOR regulate autophagy through direct phosphorylation of Ulk1

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes.

For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure flux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy.

Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation, it is imperative to target by gene knockout or RNA interference more than one autophagy-related protein. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways implying that not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular assays, we hope to encourage technical innovation in the field.

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Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

History & Exam A healthy 6-month old boy was sent to a pediatric neurologist because his parents had noted “irregular eye movements” for 1 month. The neurologist found horizontal nystagmus with a rare vertical component and ordered a brain MRI that demonstrated a suprasellar mass, prompting hospital admission. We found him to be alert and behaving normally. Vital signs and general physical examination were normal. He could fix and follow objects. Pupils reacted normally to bright light without afferent pupillary defect. He had a multivector nystagmus with rare intermittent convergence movements. Fundus and neurologic examinations were normal. Review of the outside brain MRI showed a mass perhaps arising from the diencephalon with high T2 signal and low T1 signal and intense enhancement. It compressed the third ventricle superiorly, extended anteriorly along the planum sphenoidale, and postero-inferiorly into the pre-pontine cistern. The imaging features, apparent site of origin, and the patient’s young age strongly suggested a diagnosis of pilocytic astrocytoma. In view of the considerable risks in operating on such a young patient with such a large tumor, chemotherapy (carboplatin and vincristine) began without a tissue diagnosis. Four days later, the patient was readmitted for poor feeding and lassitude. Features of a dorsal midbrain syndrome were present. Brain CT demonstrated interval increase in the size of the lateral and third ventricles. A procedure was performed.

A Case of Mistaken Identity

Huntington ’ s disease, a rare dominant degenerative disorder, manifests with neuropsychiatric changes and progres-sive cognitive and motor impairments. These diverse clinical manifestations re ﬂ ect neurodegeneration initially in striatal medium spiny neurons and progressing to involve the entire brain. Disease-causing expansions of a CAG/polyglutamine tract in the gene encoding huntingtin (HTT) protein trigger protein misfolding underlying the occurrence of histopatho-logically visible intranuclear and cytoplasmic inclusion bod-ies. As is likely for the eight other polyglutamine expansion disorders, proteotoxicity, as well as possibly RNA toxicity, underlies signi ﬁ cant aspects of the pathogenic cascade, pro-viding the rationale for HTT-lowering therapeutics. Although HTT levels measured in cerebrospinal ﬂ uid corre-late well with brain expression, visualization of mutant HTT in patients, particularly those entering trials of HTT-lowering therapeutics, would provide insight into the regional distribu-tion of pathology, assess target engagement, and permit in vivo evaluation of regional durability of therapeutic effects. detected pharmacological effects of two intervention para-digms: direct striatal delivery of adeno-associated virus (AAV)-expressing zinc- ﬁ nger protein repressors of mutant HTT and genetic suppression of mutant HTT in a regulatable mouse model. Lowering of HTT in these models was associ-ated with positive effects on imaging markers of striatal neu-ron dysfunction.

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Andrew P. Lieberman

Papers

A Case of Mistaken Identity

A Positron Emission Tomography Ligand for Mutant Huntingtin Sheds Light on Disease

Niemann-Pick C1 protein regulates platelet membrane-associated Ca2+ signalling in thrombo-occlusive diseases in mice.