Automatización del sistema de bombeo en el aprovisionamiento de agua potable a la ciudad de Nogoyá, Entre Ríos

En la provincia de Entre Ríos, los proveedores de agua potable y saneamiento a cargo de los municipios, no siempre dan prioridad a la inversión requerida. Este trabajo demuestra que es posible reducir los costos de producción con pequeños aumentos en los presupuestos asignados. Podrían conseguirse ahorros significativos además de preservar los recursos hídricos, a través de una adecuada planificación. La ciudad de Nogoyá tiene una población de alrededor de 25.000 personas. La red de agua potable cubre el 98%, abastecida por 10 pozos equipados con bombas sumergibles, que eran operados por un sistema manual - empírico. El objetivo se basó en alternar la operación de las bombas y la aplicación de la automatización de la operación para minimizar los gastos. Por el momento, se logró reducir los costos operativos entre 8 y 24% y disminuir los conos de depresión de bombeo.In the province of Entre Rios, suppliers of drinking water and sanitation in charge of municipalities, do not always prioritize the investment required.This work shows that it is possible to reduce costs of production with small increases in the assigned budgets. Significant savings could be achieved besides to preserve water resources through an appropriate planning. Nogoyá city has a population of around 25.000 people. The drinking water network covers 98%, supplied by 10 boreholes equipped with submersible pumps, which were operated by a manual - empirical system. The objective was based on alternating the pumps operation, implementing the automation of the operation to minimize expenses. At the moment, we managed to reduce operating costs between 8 and 24% and reduce pumping cones of depression.Universidad Nacional de La Plat

Epigenetic and Transcriptional Dysregulation in T cells of Patients with Atopic Dermatitis

Rationale: Atopic dermatitis (AD) is linked to genetic and environmental risk factors. The effect of these factors on molecular and transcriptional events is not well understood. Immunologically, AD involves skin barrier defects and CD4+ T cells that produce inflammatory cytokines and amplify epidermal dysfunction Our objective was to investigate epigenetic mechanisms that may account for genetic susceptibility in CD4+ T cells. Methods: We measured chromatin accessibility (ATAC-seq), NFKB1 binding (ChIP-seq), and gene expression (RNA-seq) in anti-CD3/CD28 stimulated CD4+ T cells from 6 subjects with active moderate-to-severe AD and 6 age-matched non-allergic controls. Results: AD genetic risk loci were enriched for open chromatin regions in stimulated CD4+ T cells. The majority of ATAC-seq peaks were shared between matched AD-control pairs, consistent with those sections of chromatin being equally available. In contrast, NFKB DNA binding motifs were enriched in AD-dependent open chromatin. NFKB1 ChIP-seq identified genomic regions that were more strongly bound in AD cases, more strongly bound in controls, or shared between cases and controls. Chromatin that was strongly accessible and bound by NFKB1 in AD was enriched for AD genetic risk variants. Using whole genome sequencing data, we identified genotype-dependent accessible chromatin at AD risk loci corresponding to 32 genes with genotype-dependent expression in stimulated CD4+ T cells. Conclusions: The response of CD4+ T cells to stimulation is AD-specific and results in differential chromatin accessibility and transcription factor binding. These differences in transcriptional regulation result in epigenetic and transcriptional dysregulation in CD4+ T cells of patients with AD

Epigenetic and transcriptional dysregulation in CD4+ T cells in patients with atopic dermatitis

Atopic dermatitis (AD) is one of the most common skin disorders among children. Disease etiology involves genetic and environmental factors, with 29 independent AD risk loci enriched for risk allele-dependent gene expression in the skin and CD4+ T cell compartments. We investigated the potential epigenetic mechanisms responsible for the genetic susceptibility of CD4+ T cells. To understand the differences in gene regulatory activity in peripheral blood T cells in AD, we measured chromatin accessibility (an assay based on transposase-accessible chromatin sequencing, ATAC-seq), nuclear factor kappa B subunit 1 (NFKB1) binding (chromatin immunoprecipitation with sequencing, ChIP-seq), and gene expression levels (RNA-seq) in stimulated CD4+ T cells from subjects with active moderate-to-severe AD, as well as in age-matched non-allergic controls. Open chromatin regions in stimulated CD4+ T cells were highly enriched for AD genetic risk variants, with almost half of the AD risk loci overlapping AD-dependent ATAC-seq peaks. AD-specific open chromatin regions were strongly enriched for NF-κB DNA-binding motifs. ChIP-seq identified hundreds of NFKB1-occupied genomic loci that were AD- or control-specific. As expected, the AD-specific ChIP-seq peaks were strongly enriched for NF-κB DNA-binding motifs. Surprisingly, control-specific NFKB1 ChIP-seq peaks were not enriched for NFKB1 motifs, but instead contained motifs for other classes of human transcription factors, suggesting a mechanism involving altered indirect NFKB1 binding. Using DNA sequencing data, we identified 63 instances of altered genotype-dependent chromatin accessibility at 36 AD risk variant loci (30% of AD risk loci) that might lead to genotype-dependent gene expression. Based on these findings, we propose that CD4+ T cells respond to stimulation in an AD-specific manner, resulting in disease- and genotype-dependent chromatin accessibility alterations involving NFKB1 binding

The underlying molecular and network level mechanisms in the evolution of robustness in gene regulatory networks.

Gene regulatory networks show robustness to perturbations. Previous works identified robustness as an emergent property of gene network evolution but the underlying molecular mechanisms are poorly understood. We used a multi-tier modeling approach that integrates molecular sequence and structure information with network architecture and population dynamics. Structural models of transcription factor-DNA complexes are used to estimate relative binding specificities. In this model, mutations in the DNA cause changes on two levels: (a) at the sequence level in individual binding sites (modulating binding specificity), and (b) at the network level (creating and destroying binding sites). We used this model to dissect the underlying mechanisms responsible for the evolution of robustness in gene regulatory networks. Results suggest that in sparse architectures (represented by short promoters), a mixture of local-sequence and network-architecture level changes are exploited. At the local-sequence level, robustness evolves by decreasing the probabilities of both the destruction of existent and generation of new binding sites. Meanwhile, in highly interconnected architectures (represented by long promoters), robustness evolves almost entirely via network level changes, deleting and creating binding sites that modify the network architecture

Schematic representation of the gene-regulatory network model.

<p>(A) Model of development. The expression of each gene is regulated by combinatorial interaction between an explicitly modeled <i>cis</i>-regulatory sequence (black lines) and the gene products (sequence specific transcription factors). Each gene product is represented by a different color. Shapes within the <i>cis</i>-regulatory regions represent sequence determinants of regulatory elements and their colors define the identity of the interacting transcription factor. Within the box, the explicit regulatory sequence representation is illustrated by showing an example of a consensus binding site for a given TF (maximal binding specificity, <i>κ_max</i>) and a mutated site (with a lower <i>κ</i>). The extent of gene regulation is a function of the presence and associated binding specificities of each regulatory element (<i>κ_ix</i>, where <i>i</i> is the input gene and <i>x</i> is a regulatory site on gene <i>j</i>), transcription factor abundances (<i>s_i</i>) and the function of the interacting transcription factors (activator or repressor of transcription, represented as positive and negative <i>s_i</i> values). (B) Population model. Simulations start with a randomly chosen developmentally stable founder. Variation is introduced in two forms: exchange of promoter regions between two randomly chosen parents (without recombination within promoter regions) and single point mutations at the DNA level. Selective pressure is applied to the offspring on two levels: they must develop a stable expression pattern through time (phenotype) and that phenotype must be similar to that of the founder.</p

Network-architecture level mechanisms.

<p>Correlation between the “other contributions” portion of the change in robustness (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002865#pcbi-1002865-g005" target="_blank">Fig. 5B</a>) and the average network rewiring as a function of URR (<i>L</i>) and specificity gap (γ). Rewiring, <i>Φ</i>, was computed between individuals at generation 2000 and their respective founders (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002865#s4" target="_blank">Materials and Methods</a>). These values were corrected for the effects of changing connectivity by calculating <i>Φ</i> between two randomly chosen stable individuals, both with the same average connectivity values observed for the individuals at the end of the simulations. The correlation shows that <i>Φ</i> explains for the most part the “other contributions” component of robustness. The amount of rewiring depends primarily on <i>L</i> and to a lesser extent on <i>γ</i>. Error bars are the standard error of the mean over 100 independent simulations.</p

Decomposition of robustness.

<p>Robustness due to stable individuals is the sum of the products between the frequency and the average phenotypic distance of the mutational events described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002865#pcbi-1002865-g004" target="_blank">Fig. 4A</a>. Therefore they can be used to decompose robustness. (A) Relative composition of the frequencies of each mutation type. They were measured as the differences between final and initial generations in the simulations for each of the classified mutational events (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002865#s4" target="_blank">Materials and Methods</a>). Silent mutations dominate in almost all cases, especially at low specificity gap (<i>γ</i>) and URR lengths (<i>L</i>). Silent mutations are found at the extreme of local-sequence level changes (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002865#pcbi-1002865-g004" target="_blank">Fig. 4B</a>). (B) Fraction of local-sequence and network-architecture level changes. Local changes were calculated as the fraction of the total robustness change assuming constant frequency of silent mutations (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002865#s4" target="_blank">Materials and Methods</a>). The length of the URRs (in base pairs) is indicated on top of each bar in both graphs.</p

Classification of events produced by single point mutations on a <i>cis</i>-regulatory segment.

<p>(A) Decision tree defining all possible events on <i>cis</i>-regulatory regions after the introduction of a point mutation. (B) These events can be thought of as “tools” available to the system, since they summarize all the changes the system can potentially make. For clarity, we also classified them in a continuum, according to their impact on the network architecture. Silent mutations are located at the local-sequence level extreme, since they produce changes that only affect the sequence without modifying either network architecture or gene expression levels. On the other hand, deletion or creation of unique TFBSs is found at the other extreme (network-architecture level) because these events directly impact the network's architecture. A preserved TFBS has the ability to change the relative specificity of a binding site.</p

Evolution of robustness depends on URR length and specificity gap.

<p>(A) Change in robustness, measured as the difference of the mean phenotypic distances between unperturbed and perturbed individuals at generations 2000 and 0. The mutation rate used for this measure was 1 mutation per 100 bp per genome. (B) Change in connectivity (comparing generation 2000 to generation 0), measured by the fraction of unique inputs in the network of a given individual. The numbers at the end of each bar represent the connectivity at the end of the simulations. Error bars are the standard error of the mean over 100 independent simulations.</p