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

Tumor necrosis factor-alpha (TNF-alpha) has been shown to have certain catabolic effects on fat cells and whole animals. An induction of TNF-alpha messenger RNA expression was observed in adipose tissue from four different rodent models of obesity and diabetes. TNF-alpha protein was also elevated locally and systemically. Neutralization of TNF-alpha in obese fa/fa rats caused a significant increase in the peripheral uptake of glucose in response to insulin. These results indicate a role for TNF-alpha in obesity and particularly in the insulin resistance and diabetes that often accompany obesity.

http://science.sciencemag.org/content/sci/259/5091/87.full.pdf

Adipose expression of tumor necrosis factor-alpha: direct role in obesity-linked insulin resistance

The last decade has seen a sharp increase in the number of scientific publications describing physiological and pathological functions of extracellular vesicles (EVs), a collective term covering various subtypes of cell-released, membranous structures, called exosomes, microvesicles, microparticles, ectosomes, oncosomes, apoptotic bodies, and many other names. However, specific issues arise when working with these entities, whose size and amount often make them difficult to obtain as relatively pure preparations, and to characterize properly. The International Society for Extracellular Vesicles (ISEV) proposed Minimal Information for Studies of Extracellular Vesicles (“MISEV”) guidelines for the field in 2014. We now update these “MISEV2014” guidelines based on evolution of the collective knowledge in the last four years. An important point to consider is that ascribing a specific function to EVs in general, or to subtypes of EVs, requires reporting of specific information beyond mere description of function in a crude, potentially contaminated, and heterogeneous preparation. For example, claims that exosomes are endowed with exquisite and specific activities remain difficult to support experimentally, given our still limited knowledge of their specific molecular machineries of biogenesis and release, as compared with other biophysically similar EVs. The MISEV2018 guidelines include tables and outlines of suggested protocols and steps to follow to document specific EV-associated functional activities. Finally, a checklist is provided with summaries of key points.

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Minimal information for studies of extracellular vesicles 2018 (MISEV2018) : a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines

The transcription factor NF-kappaB has been the focus of intense investigation for nearly two decades. Over this period, considerable progress has been made in determining the function and regulation of NF-kappaB, although there are nuances in this important signaling pathway that still remain to be understood. The challenge now is to reconcile the regulatory complexity in this pathway with the complexity of responses in which NF-kappaB family members play important roles. In this review, we provide an overview of established NF-kappaB signaling pathways with focus on the current state of research into the mechanisms that regulate IKK activation and NF-kappaB transcriptional activity.

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Signaling to NF-kappaB.

Sixteen years have passed since the description of the nuclear factor-кB (NF-кB) as a regulator of к light-chain gene expression in murine B lymphocytes (Sen & Baltimore, 1986a) During that time, over 4,000 publications have appeared, characterizing the family of Rel/NF-кB transcription factors involved in the control of a large number of normal and pathological cellular processes The physiological functions of NF-кB proteins include immunological and inflammatory responses, developmental processes, cellular growth and modulating effects on apoptosis In addition, these factors are activated in a number of diseases, including cancer, arthritis, acute and chronic inflammatory states, asthma, as well as neurodegenerative and heart diseases

Activators and target genes of Rel/NF-kappaB transcription factors.

The enzymatic activity of firefly luciferase provides a sensitive, rapid means to assay transcriptional activity of regulated activation sequences of DNA when fused to the protein coding sequence of the luciferase gene. We have developed several modifications of the luciferase assay and have characterized certain parameters of the assay, resulting in optimization of conditions for the preparation and storage of cell lysates and establishment of substrate concentrations. These findings should be useful to investigators interested in applying the luciferase assay to studies of the transcriptional activity of test DNAs incorporating the luciferase reporter.

Optimized use of the firefly luciferase assay as a reporter gene in mammalian cell lines.

It is now well established that vascular inflammation is an independent risk factor for the development of atherosclerosis. In otherwise healthy patients, chronic elevations of circulating interleukin-6 or its biomarkers are predictors for increased risk in the development and progression of ischemic heart disease. Although multifactorial in etiology, vascular inflammation produces atherosclerosis by the continuous recruitment of circulating monocytes into the vessel wall and by contributing to an oxidant-rich inflammatory milieu that induces phenotypic changes in resident (noninflammatory) cells. In addition, the renin-angiotensin system (RAS) has important modulatory activities in the atherogenic process. Recent work has shown that angiotensin II (Ang II) has significant proinflammatory actions in the vascular wall, inducing the production of reactive oxygen species, inflammatory cytokines, and adhesion molecules. These latter effects on gene expression are mediated, at least in part, through the cytoplasmic nuclear factor-kappaB transcription factor. Through these actions, Ang II augments vascular inflammation, induces endothelial dysfunction, and, in so doing, enhances the atherogenic process. Our recent studies have defined a molecular mechanism for a biological positive-feedback loop that explains how vascular inflammation can be self-sustaining through upregulation of the vessel wall Ang II tone. Ang II produced locally by the inflamed vessel induces the synthesis and secretion of interleukin-6, a cytokine that induces synthesis of angiotensinogen in the liver through a janus kinase (JAK)/signal transducer and activator of transcription (STAT)-3 pathway. Enhanced angiotensinogen production, in turn, supplies more substrate to the activated vascular RAS, where locally produced Ang II synergizes with oxidized lipid to perpetuate atherosclerotic vascular inflammation. These observations suggest that one mechanism by which RAS antagonists prevent atherosclerosis is by reducing vascular inflammation. Moreover, antagonizing the vascular nuclear factor-kappaB and/or hepatic JAK/STAT pathways may modulate the atherosclerotic process.

Vascular Inflammation and the Renin-Angiotensin System

Nuclear factor (NF)-κB is a family of seven structurally related transcription factors that play a central role in cardiovascular growth, stress response, and inflammation by controlling gene network expression. Although the NF-κB subunits are ubiquitously expressed, their actions are regulated in a cell-type and stimulus-specific manner, allowing for a diverse spectrum of effects. For example, NF-κB is activated by cytokines, reactive oxygen species, bacterial cell wall products, vasopressors, viral infection, and DNA damage. Recent molecular dissection of its mechanisms for activation has shown that NF-κB can be induced by the so-called “canonical” and “noncanonical” pathways, leading to distinct patterns in the individual subunits activated and down-stream genetic responses produced. The canonical pathway involves activating the IκB kinase (IKK) with subsequent phosphorylation-induced proteolysis of the IκBα inhibitors and consequent nuclear translocation of the Rel A transcriptional activator. Recent work using high-density oligonucleotide arrays have begun to systematically dissect the scope of the gene network under canonical NF-κB control and have yielded important insights into biological pathways controlled by it. This pathway controls expression of noncontiguous, functionally discrete groups of genes (“regulons”), whose temporal expression occurs in waves. Moreover, its mode of activation (oscillatory or monophasic) plays an important role in determining the spectrum of target genes expressed. By contrast, the noncanonical NF-κB activation pathway involves activating the NF-κB inducing kinase (NIK) to stimulate IKKα-induced phosphorylation and proteolytic processing of the 100-kDa cytoplasmic NF-κB2 precursor. Activated NF-κB2 then forms a complex with Rel B and NIK to translocate into the nucleus thereby activating a distinct set of genes. Although the noncanonical pathway has been most clearly linked to control of adaptive immunity, recent intriguing studies have implicated this pathway in viral induced stress response and in the metabolic syndrome. In this way, a single family of transcription factors can respond to diverse stimuli to regulate cardiovascular homeostasis.

The NF-kappaB regulatory network.

Vascular inflammation is a common pathophysiological response to diverse cardiovascular disease processes, including atherosclerosis, myocardial infarction, congestive heart failure, and aortic aneurysms/dissection. Inflammation is an ordered process initiated by vascular injury that produces enhanced leucocyte adherence, chemotaxis, and finally activation in situ. This process is coordinated by local secretion of adhesion molecules, chemotactic factors, and cytokines whose expression is the result of vascular injury-induced signal transduction networks. A wide variety of mediators of the vascular injury response have been identified; these factors include vasoactive peptides (angiotensin II, Ang II), CD40 ligands, oxidized cholesterol, and advanced glycation end-products. Downstream, the nuclear factor-kappaB (NF-kappaB) transcription factor performs an important signal integration step, responding to mediators of vascular injury in a stimulus-dependent and cell type-specific manner. The ultimate consequence of NF-kappaB signalling is the activation of inflammatory genes including adhesion molecules and chemotaxins. However, clinically, the hallmark of vascular NF-kappaB activation is the production of interleukin-6 (IL-6), whose local role in vascular inflammation is relatively unknown. The recent elucidation for the role of the IL-6 signalling pathway in Ang II-induced vascular inflammation as one that controls monocyte activation as well as its diverse signalling mechanism will be reviewed. These new discoveries further our understanding for the important role of the NF-kappaB-IL-6 signalling pathway in the process of vascular inflammation.

The nuclear factor-κB–interleukin-6 signalling pathway mediating vascular inflammation

Vascular inflammation contributes to cardiovascular diseases such as aortic aneurysm and dissection. However, the precise inflammatory pathways involved have not been clearly defined. We have shown here that subcutaneous infusion of Ang II, a vasopressor known to promote vascular inflammation, into older C57BL/6J mice induced aortic production of the proinflammatory cytokine IL-6 and the monocyte chemoattractant MCP-1. Production of these factors occurred predominantly in the tunica adventitia, along with macrophage recruitment, adventitial expansion, and development of thoracic and suprarenal aortic dissections. In contrast, a reduced incidence of dissections was observed after Ang II infusion into mice lacking either IL-6 or the MCP-1 receptor CCR2. Further analysis revealed that Ang II induced CCR2+CD14hiCD11bhiF4/80- macrophage accumulation selectively in aortic dissections and not in aortas from Il6-/- mice. Adoptive transfer of Ccr2+/+ monocytes into Ccr2-/- mice resulted in selective monocyte uptake into the ascending and suprarenal aorta in regions of enhanced ROS stress, with restoration of IL-6 secretion and increased incidence of dissection. In vitro, coculture of monocytes and aortic adventitial fibroblasts produced MCP-1- and IL-6-enriched conditioned medium that promoted differentiation of monocytes into macrophages, induced CD14 and CD11b upregulation, and induced MCP-1 and MMP-9 expression. These results suggest that leukocyte-fibroblast interactions in the aortic adventitia potentiate IL-6 production, inducing local monocyte recruitment and activation, thereby promoting MCP-1 secretion, vascular inflammation, ECM remodeling, and aortic destabilization.

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Allan R. Brasier

Papers

Optimized use of the firefly luciferase assay as a reporter gene in mammalian cell lines.

Vascular Inflammation and the Renin-Angiotensin System

The NF-kappaB regulatory network.

The nuclear factor-κB–interleukin-6 signalling pathway mediating vascular inflammation

An adventitial IL-6/MCP1 amplification loop accelerates macrophage-mediated vascular inflammation leading to aortic dissection in mice