Comment: Monitoring Networked Applications With Incremental Quantile Estimation

Comment: Monitoring Networked Applications With Incremental Quantile Estimation [arXiv:0708.0302]Comment: Published at http://dx.doi.org/10.1214/088342306000000619 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Using data network metrics, graphics, and topology to explore network characteristics

Yehuda Vardi introduced the term network tomography and was the first to propose and study how statistical inverse methods could be adapted to attack important network problems (Vardi, 1996). More recently, in one of his final papers, Vardi proposed notions of metrics on networks to define and measure distances between a network's links, its paths, and also between different networks (Vardi, 2004). In this paper, we apply Vardi's general approach for network metrics to a real data network by using data obtained from special data network tools and testing procedures presented here. We illustrate how the metrics help explicate interesting features of the traffic characteristics on the network. We also adapt the metrics in order to condition on traffic passing through a portion of the network, such as a router or pair of routers, and show further how this approach helps to discover and explain interesting network characteristics.Comment: Published at http://dx.doi.org/10.1214/074921707000000058 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Gene Therapy for Cardiovascular Disease

The last decade has seen substantial advances in the development of gene therapy strategies and vector technology for the treatment of a diverse number of diseases, with a view to translating the successes observed in animal models into the clinic. Perhaps the overwhelming drive for the increase in vascular gene transfer studies is the current lack of successful long-term pharmacological treatments for complex cardiovascular diseases. The increase in cardiovascular disease to epidemic proportions has also led many to conclude that drug therapy may have reached a plateau in its efficacy and that gene therapy may represent a realistic solution to a long-term problem. Here, we discuss gene delivery approaches and target diseases

Myeloid heterogeneity in kidney disease as revealed through single cell RNA sequencing

Kidney disease represents a global health burden of increasing prevalence and is an independent risk factor for cardiovascular disease. Myeloid cells are a major cellular compartment of the immune system; they are found in the healthy kidney and in increased numbers in the damaged and/or diseased kidney, where they act as key players in the progression of injury, inflammation, and fibrosis. They possess enormous plasticity and heterogeneity, adopting different phenotypic and functional characteristics in response to stimuli in the local milieu. Although this inherent complexity remains to be fully understood in the kidney, advances in single-cell genomics promise to change this. Specifically, single-cell RNA sequencing (scRNA-seq) has had a transformative effect on kidney research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput, and subsequent generation of cell atlases. Moving forward, combining scRNA- and single-nuclear RNA-seq with greater-resolution spatial transcriptomics will allow spatial mapping of kidney disease of varying etiology to further reveal the patterning of immune cells and nonimmune renal cells. This review summarizes the roles of myeloid cells in kidney health and disease, the experimental workflow in currently available scRNA-seq technologies, and published findings using scRNA-seq in the context of myeloid cells and the kidney

Targeting Special Populations for Family Preservation: The Influence of Worker Competence and Organizational Culture

Today there are approximately 581,000 children in the United States foster care system. Children of color, one special population group, are disproportionately represented in the foster care system. Family preservation, a program that aims to improve family functioning and thus decrease the need for foster care, has been examined closely. Some researchers believe that family preservation programs have failed partly due to practitioners\u27 inability to target appropriate families (Feldman, 1990; Schuerman, Rzepnicki & Littell, 1994). Additionally, research confirms that children of color are not the target of family preservation services (Denby, Curtis, & Alford, 1998). Improvements in the effectiveness of family preservation will require many types of reform both internal and external to the program. Among the types of internal reform needed is accurate targeting of services. Given the overrepresentation of children of color in the foster care system, this group must be among those who are targeted for services. The results of a national survey of 254 family preservation workers reveal a profile of the worker who is likely to target special populations, including children of color, for family preservation services. A case is made for service improvements and training to facilitate the profiled workers\u27 competencies

Can manipulation of differentiation conditions eliminate proliferative cells from a population of ES cell-derived forebrain cells?

There is preliminary evidence that implantation of primary fetal striatal cells provides functional benefit in patients with Huntington’s disease, a neurodegenerative condition resulting in loss of medium-sized spiny neurons (MSN) of the striatum. Scarcity of primary fetal tissue means it is important to identify a renewable source of cells from which to derive donor MSNs. Embryonic stem (ES) cells, which predominantly default to telencephalic-like precursors in chemically defined medium (CDM), offer a potentially inexhaustible supply of cells capable of generating the desired neurons. Using an ES cell line, with the forebrain marker FoxG1 tagged to the LacZ reporter, we assessed effects of known developmental factors on the yield of forebrain-like precursor cells in CDM suspension culture. Addition of FGF2, but not DKK1, increased the proportion of FoxG1- expressing cells at day 8 of neural induction. Oct4 was expressed at day 8, but was undetectable by day 16. Differentiation of day 16 precursors generated GABA-expressing neurons, with few DARPP32 positive MSNs. Transplantation of day 8 precursor cells into quinolinic acid-lesioned striata resulted in generation of teratomas. However, transplantation of day 16 precursors yielded grafts expressing neuronal markers including NeuN, calbindin and parvalbumin, but no DARPP32 6 weeks post-transplantation. Manipulation of fate of ES cells requires optimization of both concentration and timing of addition of factors to culture systems to generate the desired phenotypes. Furthermore, we highlight the value of increasing the precursor phase of ES cell suspension culture when directing differentiation toward forebrain fate, so as to dramatically reduce the risk of teratoma formation

Inter-comparison of three-dimensional models of volcanic plumes

We performed an inter-comparison study of three-dimensional models of volcanic plumes. A set of common volcanological input parameters and meteorological conditions were provided for two kinds of eruptions, representing a weak and a strong eruption column. From the different models, we compared the maximum plume height, neutral buoyancy level (where plume density equals that of the atmosphere), and level of maximum radial spreading of the umbrella cloud. We also compared the vertical profiles of eruption column properties, integrated across cross-sections of the plume (integral variables). Although the models use different numerical procedures and treatments of subgrid turbulence and particle dynamics, the inter-comparison shows qualitatively consistent results. In the weak plume case (mass eruption rate 1.5 × 106 kg s− 1), the vertical profiles of plume properties (e.g., vertical velocity, temperature) are similar among models, especially in the buoyant plume region. Variability among the simulated maximum heights is ~ 20%, whereas neutral buoyancy level and level of maximum radial spreading vary by ~ 10%. Time-averaging of the three-dimensional (3D) flow fields indicates an effective entrainment coefficient around 0.1 in the buoyant plume region, with much lower values in the jet region, which is consistent with findings of small-scale laboratory experiments. On the other hand, the strong plume case (mass eruption rate 1.5 × 109 kg s− 1) shows greater variability in the vertical plume profiles predicted by the different models. Our analysis suggests that the unstable flow dynamics in the strong plume enhances differences in the formulation and numerical solution of the models. This is especially evident in the overshooting top of the plume, which extends a significant portion (~ 1/8) of the maximum plume height. Nonetheless, overall variability in the spreading level and neutral buoyancy level is ~ 20%, whereas that of maximum height is ~ 10%. This inter-comparison study has highlighted the different capabilities of 3D volcanic plume models, and identified key features of weak and strong plumes, including the roles of jet stability, entrainment efficiency, and particle non-equilibrium, which deserve future investigation in field, laboratory, and numerical studies.YJS was partially supported by the ERI Cooperative Research Program and KAKENHI (25750142). The computations of SK-3D were carried out in part on the Earth Simulator at the JAMSTEC and also on the Primergy RX200S6 at the Research Computer System, Kyushu University. AC was partially supported by a grant of the International Research Promotion Office Earthquake Research Institute, the University of Tokyo. AC, TEO and MC were partially supported by the EU-funded project MEDiterranean Supersite Volcanoes (MEDSUV; grant no. 308665). MC acknowledges CINECA award N. HP10BKFD9F (2013) for high performance computing resources and support. AVE acknowledges NSF Postdoctoral Fellowship EAR1250029, a U.S. Geological Survey Mendenhall fellowship, and grant GID 61233 from NASA Ames Supercomputing Center

Neural Filters for Jet Analysis

We study the efficiency of a neural-net filter and deconvolution method for estimating jet energies and spectra in high-background reactions such as nuclear collisions at the relativistic heavy-ion collider and the large hadron collider. The optimal network is shown to be surprisingly close but not identical to a linear high-pass filter. A suitably constrained deconvolution method is shown to uncover accurately the underlying jet distribution in spite of the broad network response. Finally, we show that possible changes of the jet spectrum in nuclear collisions can be analyzed quantitatively, in terms of an effective energy loss with the proposed method. {} {Dong D W and Gyulassy M 1993}{Neural filters for jet analysis} {(LBL-31560) Physical Review E Vol~47(4) pp~2913-2922}Comment: 21 pages of Postscript, (LBL-31560