Housing and Urbanization: A Socio-Spatial Analysis of Resettlement Projects in Hồ Chí Minh City

As Hồ Chí Minh City continues to undergo rapid urbanization, especially with the creation of a multitude of new urban zone developments on the periphery of the inner districts, the resettling of people has become common. Families who live within areas that are selected for urban upgrading or, as in other cases for the construction of new miniature cities, must face the realities of relocation. Many issues arise in the complicated process of resettling the displaced, due to complex land-use laws, bureaucratic dissonance, and lack of investment in actual resettlement housing. The authorities of Hồ Chí Minh City have faced palpable challenges in facilitating the many processes of resettlement, from persuading developers to invest in resettlement housing to establishing suitable compensation packages. Confusing legal labyrinths, delays in plan approval, and miscommunications between agencies, results in tangible affects on the highly vulnerable displaced families. Additionally, a serious disconnect arises between planners’ envisioned solution for resettlement housing and the real needs of the resettled, who are usually low-income workers. When the precise needs of displaced families and their prior sources of economic livelihood are disregarded, the general result is unsuitable design and the disordering of previously established socio-spatial networks. Additionally the displaced tend to be sent to occupy less advantageous space, as a result of gentrification, and are spatially repositioned in more excluded, disconnected marginal zones. Past and present resettlement procedures have faltered due especially to a lack of socio-spatial planning, which has resulted in undesirable threats to equitable metropolisation and rising potentials for urban fragmentation

Network motifs: structure does not determine function

BACKGROUND: A number of publications have recently examined the occurrence and properties of the feed-forward motif in a variety of networks, including those that are of interest in genome biology, such as gene networks. The present work looks in some detail at the dynamics of the bi-fan motif, using systems of ordinary differential equations to model the populations of transcription factors, mRNA and protein, with the aim of extending our understanding of what appear to be important building blocks of gene network structure. RESULTS: We develop an ordinary differential equation model of the bi-fan motif and analyse variants of the motif corresponding to its behaviour under various conditions. In particular, we examine the effects of different steady and pulsed inputs to five variants of the bifan motif, based on evidence in the literature of bifan motifs found in Saccharomyces cerevisiae (commonly known as baker's yeast). Using this model, we characterize the dynamical behaviour of the bi-fan motif for a wide range of biologically plausible parameters and configurations. We find that there is no characteristic behaviour for the motif, and with the correct choice of parameters and of internal structure, very different, indeed even opposite behaviours may be obtained. CONCLUSION: Even with this relatively simple model, the bi-fan motif can exhibit a wide range of dynamical responses. This suggests that it is difficult to gain significant insights into biological function simply by considering the connection architecture of a gene network, or its decomposition into simple structural motifs. It is necessary to supplement such structural information by kinetic parameters, or dynamic time series experimental data, both of which are currently difficult to obtain

Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

Approximate Bayesian computation methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper we discuss and apply an approximate Bayesian computation (ABC) method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC gives information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus.Comment: 26 pages, 9 figure

Induction and function of the phage shock protein extracytoplasmic stress response in Escherichia coli

The phage shock protein (Psp) F regulon response in Escherichia coli is thought to be induced by impaired inner membrane integrity and an associated decrease in proton motive force (pmf). Mechanisms by which the Psp system detects the stress signal and responds have so far remained undetermined. Here we demonstrate that PspA and PspG directly confront a variety of inducing stimuli by switching the cell to anaerobic respiration and fermentation and by down-regulating motility, thereby subtly adjusting and maintaining energy usage and pmf. Additionally, PspG controls iron usage. We show that the Psp-inducing protein IV secretin stress, in the absence of Psp proteins, decreases the pmf in an ArcB-dependent manner and that ArcB is required for amplifying and transducing the stress signal to the PspF regulon. The requirement of the ArcB signal transduction protein for induction of psp provides clear evidence for a direct link between the physiological redox state of the cell, the electron transport chain, and induction of the Psp response. Under normal growth conditions PspA and PspD control the level of activity of ArcB/ArcA system that senses the redox/metabolic state of the cell, whereas under stress conditions PspA, PspD, and PspG deliver their effector functions at least in part by activating ArcB/ArcA through positive feedback

Surficial geology of Antioch quadrangle, Lake County, Illinois and Kenosha County, Wisconsin

Relief shown by contours and spot heightsIncludes text and 1 location mapIncludes bibliographical references (leaf 4 of pamphlet

Surficial geology of Wauconda Quadrangle, Lake and McHenry counties, Illinois

Relief shown by contours and spot heights"This research was supported in part by the U.S. Geological Survey, National Cooperative Geologic Mapping Program under USGS award number 01HQAG0103."Includes disclaimer, index to adjoining quadrangles, and location mapIncludes bibliographical references in tex

Categorizing the severity of paralytic shellfish poisoning outbreaks in the Gulf of Maine for forecasting and management

Author Posting. © The Author(s), 2013. This is the author's version of the work. It is posted here by permission of Elsevier for personal use, not for redistribution. The definitive version was published in Deep Sea Research Part II: Topical Studies in Oceanography 103 (2014): 277-287, doi:10.1016/j.dsr2.2013.03.027.Development of forecasting systems for harmful algal blooms (HABs) has been a long-standing research and management goal. Significant progress has been made in the Gulf of Maine, where seasonal bloom forecasts are now being issued annually using Alexandrium fundyense cyst abundance maps and a population dynamics model developed for that organism. Thus far these forecasts have used terms such as “significant”, “moderately large” or “moderate” to convey the extent of forecasted paralytic shellfish poisoning (PSP) outbreaks. In this study, historical shellfish harvesting closure data along the coast of the Gulf of Maine were used to derive a series of bloom severity levels that are analogous to those used to define major storms like hurricanes or tornados. Thirty-four years of PSP-related shellfish closure data for Maine, Massachusetts and New Hampshire were collected and mapped to depict the extent of coastline closure in each year. Due to fractal considerations, different methods were explored for measuring length of coastline closed. Ultimately, a simple procedure was developed using arbitrary straight-line segments to represent specific sections of the coastline. This method was consistently applied to each year’s PSP toxicity closure map to calculate the total length of coastline closed. Maps were then clustered together statistically to yield distinct groups of years with similar characteristics. A series of categories or levels was defined (“Level 1: Limited”, “Level 2: Moderate”, and “Level 3: Extensive”) each with an associated range of expected coastline closed, which can now be used instead of vague descriptors in future forecasts. This will provide scientifically consistent and simply defined information to the public as well as resource managers who make decisions on the basis of the forecasts.Research support provided through the Woods Hole Center for Oceans and Human Health, National Science Foundation (NSF) Grants OCE-0430724, and OCE-0911031; and National Institute of Environmental Health Sciences (NIEHS) Grant 1-P50-ES012742-01, the ECOHAB Grant program through NOAA Grant NA06NOS4780245, and the PCM HAB Grant program through NOAA Grant NA11NOS4780023

A large fraction of HLA class I ligands are proteasome-generated spliced peptides

The proteasome generates the epitopes presented on human leukocyte antigen (HLA) class I molecules that elicit CD8(+) T cell responses. Reports of proteasome-generated spliced epitopes exist, but they have been regarded as rare events. Here, however, we show that the proteasome-generated spliced peptide pool accounts for one-third of the entire HLA class I immunopeptidome in terms of diversity and one-fourth in terms of abundance. This pool also represents a unique set of antigens, possessing particular and distinguishing features. We validated this observation using a range of complementary experimental and bioinformatics approaches, as well as multiple cell types. The widespread appearance and abundance of proteasome-catalyzed peptide splicing events has implications for immunobiology and autoimmunity theories and may provide a previously untapped source of epitopes for use in vaccines and cancer immunotherapy

P38 and JNK have opposing effects on persistence of in vivo leukocyte migration in zebrafish

The recruitment and migration of macrophages and neutrophils is an important process during the early stages of the innate immune system in response to acute injury. Transgenic pu.1:EGFP zebrafish permit the acquisition of leukocyte migration trajectories during inflammation. Currently, these high-quality live-imaging data are mainly analysed using general statistics, for example, cell velocity. Here, we present a spatio-temporal analysis of the cell dynamics using transition matrices, which provide information of the type of cell migration. We find evidence that leukocytes exhibit types of migratory behaviour, which differ from previously described random walk processes. Dimethyl sulfoxide treatment decreased the level of persistence at early time points after wounding and ablated temporal dependencies observed in untreated embryos. We then use pharmacological inhibition of p38 and c-Jun N-terminal kinase mitogen-activated protein kinases to determine their effects on in vivo leukocyte migration patterns and discuss how they modify the characteristics of the cell migration process. In particular, we find that their respective inhibition leads to decreased and increased levels of persistent motion in leukocytes following wounding. This example shows the high level of information content, which can be gained from live-imaging data if appropriate statistical tools are used

Generating confidence intervals on biological networks

<p>Abstract</p> <p>Background</p> <p>In the analysis of networks we frequently require the statistical significance of some network statistic, such as measures of similarity for the properties of interacting nodes. The structure of the network may introduce dependencies among the nodes and it will in general be necessary to account for these dependencies in the statistical analysis. To this end we require some form of Null model of the network: generally rewired replicates of the network are generated which preserve only the degree (number of interactions) of each node. We show that this can fail to capture important features of network structure, and may result in unrealistic significance levels, when potentially confounding additional information is available.</p> <p>Methods</p> <p>We present a new network resampling Null model which takes into account the degree sequence as well as available biological annotations. Using gene ontology information as an illustration we show how this information can be accounted for in the resampling approach, and the impact such information has on the assessment of statistical significance of correlations and motif-abundances in the <it>Saccharomyces cerevisiae </it>protein interaction network. An algorithm, GOcardShuffle, is introduced to allow for the efficient construction of an improved Null model for network data.</p> <p>Results</p> <p>We use the protein interaction network of <it>S. cerevisiae</it>; correlations between the evolutionary rates and expression levels of interacting proteins and their statistical significance were assessed for Null models which condition on different aspects of the available data. The novel GOcardShuffle approach results in a Null model for annotated network data which appears better to describe the properties of real biological networks.</p> <p>Conclusion</p> <p>An improved statistical approach for the statistical analysis of biological network data, which conditions on the available biological information, leads to qualitatively different results compared to approaches which ignore such annotations. In particular we demonstrate the effects of the biological organization of the network can be sufficient to explain the observed similarity of interacting proteins.</p