Clinical significance of monocyte heterogeneity

Monocytes are primitive hematopoietic cells that primarily arise from the bone marrow, circulate in the peripheral blood and give rise to differentiated macrophages. Over the past two decades, considerable attention to monocyte diversity and macrophage polarization has provided contextual clues into the role of myelomonocytic derivatives in human disease. Until recently, human monocytes were subdivided based on expression of the surface marker CD16. "Classical" monocytes express surface markers denoted as CD14(++)CD16(-) and account for greater than 70% of total monocyte count, while "non-classical" monocytes express the CD16 antigen with low CD14 expression (CD14(+)CD16(++)). However, recognition of an intermediate population identified as CD14(++)CD16(+) supports the new paradigm that monocytes are a true heterogeneous population and careful identification of specific subpopulations is necessary for understanding monocyte function in human disease. Comparative studies of monocytes in mice have yielded more dichotomous results based on expression of the Ly6C antigen. In this review, we will discuss the use of monocyte subpopulations as biomarkers of human disease and summarize correlative studies in mice that may yield significant insight into the contribution of each subset to disease pathogenesis

Assessing identity, phenotype, and fate of endothelial progenitor cells

From the paradigm shifting observations of Harvey, Malpighi, and van Leeuwenhoek, blood vessels have become recognized as distinct and dynamic tissue entities that merge with the heart to form a closed circulatory system.1 Vessel structures are comprised predominantly of a luminal layer of endothelial cells that is surrounded by some form of basement membrane, and mural cells (pericytes or vascular smooth muscle cells) that make up the vessel wall. In larger more complex vessel structures the vessel wall is composed of a complex interwoven matrix with nerve components. Understanding the cellular and molecular basis for the formation, remodeling, repair, and regeneration of the vasculature have been and continue to be popular areas for investigation. The endothelium has become a particularly scrutinized cell population with the recognition that these cells may play important roles in maintaining vascular homeostasis and in the pathogenesis of a variety of diseases.2 Although it has been known for several decades that some shed or extruded endothelial cells enter the circulation as apparent contaminants in the human blood stream,3 only more recent technologies have permitted the identification of not only senescent sloughed endothelial cells,4 but also endothelial progenitor cells (EPCs), which have been purported to represent a normal component of the formed elements of circulating blood5 and play roles in disease pathogenesis.6–9 Most citations refer to an article published in 1997 in which Asahara and colleagues isolated, characterized, and examined the in vivo function of putative EPCs from human peripheral blood as a major impetus for generating interest in the field.10 This seminal article presented some evidence to consider emergence of a new paradigm for the process of neovascularization in the form of postnatal vasculogenesis. Since publication of that article, interest in circulating endothelial cells, and particularly EPCs, has soared, and one merely has to type the keyword search terms, endothelial progenitor cell, to recover more than 8984 articles including 1347 review articles in PubMed (as of June 2008). What can we possibly add in the form of another EPC review that will be considered of significant value for the reader? We will attempt to review some of the early article in the field and reflect on how information in those articles was gradually derivatized into perhaps more conflicting rather than unifying concepts. We will also attempt to concisely address some of the important determinants and principles that are now leading to a new understanding of what functionally constitutes an EPC and outline some of the current measures used to identify, enumerate, and quantify these cells. Finally, we give our opinion of the best definition for an EPC based on some comparative analyses performed primarily in human subjects

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US

A hierarchical model of transcriptional dynamics allows robust estimation of transcription rates in populations of single cells with variable gene copy number

Motivation: cis-regulatory DNA sequence elements, such as enhancers and silencers, function to control the spatial and temporal expression of their target genes. Although the overall levels of gene expression in large cell populations seem to be precisely controlled, transcription of individual genes in single cells is extremely variable in real time. It is, therefore, important to understand how these cis-regulatory elements function to dynamically control transcription at single-cell resolution. Recently, statistical methods have been proposed to back calculate the rates involved in mRNA transcription using parameter estimation of a mathematical model of transcription and translation. However, a major complication in these approaches is that some of the parameters, particularly those corresponding to the gene copy number and transcription rate, cannot be distinguished; therefore, these methods cannot be used when the copy number is unknown. Results: Here, we develop a hierarchical Bayesian model to estimate biokinetic parameters from live cell enhancer–promoter reporter measurements performed on a population of single cells. This allows us to investigate transcriptional dynamics when the copy number is variable across the population. We validate our method using synthetic data and then apply it to quantify the function of two known developmental enhancers in real time and in single cells

Thin-film hermeticity: A quantitative analysis of diamond-like carbon using variable angle spectroscopic ellipsometry

The purpose of this paper is twofold. First, we report on the successful application of variable angle spectroscopic ellipsometry to quantitative thin-film hermeticity evaluation. Secondly, it is shown that under a variety of film preparations and moisture introduction conditions water penetrates only a very thin diamondlike carbon (DLC) top surface-roughness region. Thus DLC is an excellent candidate for use as protective coatings in adverse chemical and aqueous environments

Mimesis stories: composing new nature music for the shakuhachi

Nature is a widespread theme in much new music for the shakuhachi (Japanese bamboo flute). This article explores the significance of such music within the contemporary shakuhachi scene, as the instrument travels internationally and so becomes rooted in landscapes outside Japan, taking on the voices of new creatures and natural phenomena. The article tells the stories of five compositions and one arrangement by non-Japanese composers, first to credit composers’ varied and personal responses to this common concern and, second, to discern broad, culturally syncretic traditions of nature mimesis and other, more abstract, ideas about the naturalness of sounds and creative processes (which I call musical naturalism). Setting these personal stories and longer histories side by side reveals that composition creates composers (as much as the other way around). Thus it hints at much broader terrain: the refashioning of human nature at the confluence between cosmopolitan cultural circulations and contemporary encounters with the more-than-human world

On global-local artificial neural networks for function approximation

We present a hybrid radial basis function (RBF) sigmoid neural network with a three-step training algorithm that utilizes both global search and gradient descent training. The algorithm used is intended to identify global features of an input-output relationship before adding local detail to the approximating function. It aims to achieve efficient function approximation through the separate identification of aspects of a relationship that are expressed universally from those that vary only within particular regions of the input space. We test the effectiveness of our method using five regression tasks; four use synthetic datasets while the last problem uses real-world data on the wave overtopping of seawalls. It is shown that the hybrid architecture is often superior to architectures containing neurons of a single type in several ways: lower mean square errors are often achievable using fewer hidden neurons and with less need for regularization. Our global-local artificial neural network (GL-ANN) is also seen to compare favorably with both perceptron radial basis net and regression tree derived RBFs. A number of issues concerning the training of GL-ANNs are discussed: the use of regularization, the inclusion of a gradient descent optimization step, the choice of RBF spreads, model selection, and the development of appropriate stopping criteria