On the bend number of circular-arc graphs as edge intersection graphs of paths on a grid

Golumbic, Lipshteyn and Stern \cite{Golumbic-epg} proved that every graph can be represented as the edge intersection graph of paths on a grid (EPG graph), i.e., one can associate with each vertex of the graph a nontrivial path on a rectangular grid such that two vertices are adjacent if and only if the corresponding paths share at least one edge of the grid. For a nonnegative integer $k$, $B_k$-EPG graphs are defined as EPG graphs admitting a model in which each path has at most $k$ bends. Circular-arc graphs are intersection graphs of open arcs of a circle. It is easy to see that every circular-arc graph is a $B_4$-EPG graph, by embedding the circle into a rectangle of the grid. In this paper, we prove that every circular-arc graph is $B_3$-EPG, and that there exist circular-arc graphs which are not $B_2$-EPG. If we restrict ourselves to rectangular representations (i.e., the union of the paths used in the model is contained in a rectangle of the grid), we obtain EPR (edge intersection of path in a rectangle) representations. We may define $B_k$-EPR graphs, $k\geq 0$, the same way as $B_k$-EPG graphs. Circular-arc graphs are clearly $B_4$-EPR graphs and we will show that there exist circular-arc graphs that are not $B_3$-EPR graphs. We also show that normal circular-arc graphs are $B_2$-EPR graphs and that there exist normal circular-arc graphs that are not $B_1$-EPR graphs. Finally, we characterize $B_1$-EPR graphs by a family of minimal forbidden induced subgraphs, and show that they form a subclass of normal Helly circular-arc graphs

Sensitivity Analysis and Quantification of the Role of Governing Transport Mechanisms and Parameters in a Gas Flow Model for Low-Permeability Porous Media

Recent models represent gas (methane) migration in low-permeability media as a weighted sum of various contributions, each associated with a given flow regime. These models typically embed numerous chemical/physical parameters that cannot be easily and unambiguously evaluated via experimental investigations. In this context, modern sensitivity analysis techniques enable us to diagnose the behavior of a given model through the quantification of the importance and role of model input uncertainties with respect to a target model output. Here, we rely on two global sensitivity analysis approaches and metrics (i.e., variance-based Sobol’ indices and moment-based AMA indices) to assess the behavior of a recent interpretive model that conceptualizes gas migration as the sum of a surface diffusion mechanism and two weighted bulk flow components. We quantitatively investigate the impact of (i) each uncertain model parameter and (ii) the type of their associated probability distribution on the evaluation of methane flow. We then derive the structure of an effective diffusion coefficient embedding all complex mechanisms of the model considered and allowing quantification of the relative contribution of each flow mechanism to the overall gas flow

EXPERIMENTAL STUDY TO DETERMINE THE LOCAL CONDENSATION HEAT TRANSFER COEFFICIENTE FOR R134A FLOWING THROUGH A 4.8 MM INTERNAL DIAMETER SMOOTH HORIZONTAL TUBE

Refrigerant fluid R134a is commonly one of the most utilised invapour compression cycles wordlide, wheter in dommestic HVAC orautomotive regrigeration systems. This paper’s goal is toexperimetnally determine the fluid local condensation Heat TransferCoefficient (HTC), in several flor regimes. In this work, the mass fluxwas equal to 200, 250 and 300 kg/(m2s) and the fluid flowedthrough a smooth, horizontal 4.8 mm internal diameter aluminiumpipe, during which its vapour quality varied along the entire qualityrange. A purpose built test rig was developed, in which fluidconditions were constantly monitored and controlled. Throughmeasurements in temperature and pressure, an energy balance wasused to calculate experimentally the local heat transfer coeeficient.Average results for the unit quality range equalled to 3781 , 3459 and3944 W/m2K for saturation temperature equal to 30 C and theaforementioned mass velocities. Likewise, at 35C the averages HTCfound were 2903, 3141 and 3898 W/m2K at the same mass fluxrates. Later on, the experimental results were compared to tencommonly used HTC correlation found in relevant references,with Chato’s correlation returning the best fitting

EXPERIMENTAL STUDY OF THE PRESSURE DROP OF THE ECOFLUID R1234YF COMPARED TO THE FLUID R134A IN SMOOTH TUBES WITH 4.8MM INTERNAL DIAMMETER

The refrigerant fluid R1234yf is a hydrofluorefine with zero potential for degradation of the ozone layer and low potential for global warming. It is one of the potential substitutes for the currently used R134a in automotive systems. In this work, the pressure drop suffered by the fluids R134a and R1234yf when flowing in a test section through a pipe with a 4.8 mm internal diameter was measured. The pressure drop was plotted as a function of the void fraction at the exit of the test section and the values were compared concerning the change in mass flux, change in saturation temperature, and comparatively between R1234yf and R134a. A significant increase in pressure drop was observed by the increases of the mass flux, showing an increment of 155.46% of the pressure loss from 200 to 300 kg·m-2·s-1 for R1234yf at 35ºC and 161.07% for R134a in the same conditions. Despite being high, those values are expected since increasing mass flux also increases the friction between both phases. On the other hand, by increasing the saturation temperature, the pressure drop is slightly lower once the differences between the densities of the liquid and vapor phases are reduced. Compared with R134a, the R1234yf ecofluid presents less pressure drop, showing a reduction of 24% for 300 kg·m-2·s-1

METHODOLOGY FOR AUTOMOTIVE AIR-CONDITIONING CONTROL OPTIMIZATION USING ARTIFICIAL NEURAL NETWORKS

The transient nature of automotive air conditioning systems control is generally achieved through proportional–integral–derivative controllers (PID’s) parameters tunning. Due to the vast database available from decades of automotive manufacturers design and expertise, Artificial Neural Networks (ANN) might be able to identify underlying patterns to predict and properly tune the air-conditioning PID control systems under different thermal requirements. Recently, advances in computational capability have enabled compact embarked systems to rapidly solve complex, multi-variable sets of equations, thus allowing for ANN to promptly calculate tunning parameters and act upon PID controllers. As any new application, technical literature is still scarce. On this research, a coupled PID and 6-layers perceptron ANN system was devised, programmed and used to simulate how an air-conditioning system performance can be optimized through proportional–integral–derivative controllers tuning. This proposed setup response was then compared to a conventional heuristic PID tunning method (Ziegler Nichols) to demonstrate how ANN’s can more rapidly stabilize the system output

An original deconvolution approach for oil production allocation based on geochemical fingerprinting

We tackle oil commingling scenarios and develop an original deconvolution approach for geochemical production allocation. This yields robust assessment of the proportions of oils forming a mixture originating from commingling oils associated with diverse reservoirs or, wells. Our study starts from considering that production allocation performed by means of geochemical fingerprinting is relevant in the context of modern and sustainable use of georesources, with the added benefit of favoring shared facilities and production equipment. A geochemical production allocation workflow is typically structured according to two steps: (i) determination of the chromatograms associated with the mixture (and eventually with each of the End Members, EMs, constituting the fluids in the mixture), and (ii) the use of a deconvolution algorithm to estimate the mass fraction of each EM. Concerning the latter step, we introduce an original approach and the ensuing deconvolution algorithm (hereafter termed PGM) that does not require additional laboratory efforts in comparison with traditional approaches. We also present extensions of widely used deconvolution algorithms, which we frame in a (stochastic) Monte Carlo context to improve their robustness and reliability. The new PGM approach is assessed jointly with a suite of typically used approaches and algorithms against new laboratory-based commingling scenarios. The latter are based on the design and introduction of a novel and low-cost experimental method. The results of the study (i) constitute a unique and rigorous comparison of the traditionally employed production allocation deconvolution algorithms, (ii) document the critical importance of the number of features of the chromatograms used during a quantitative deconvolution, and (iii) suggest that our new PGM approach is very robust and accurate compared to existing approaches

Effects of supplemental fish oil on resting metabolic rate, body composition, and salivary cortisol in healthy adults

<p>Abstract</p> <p>Background</p> <p>To determine the effects of supplemental fish oil (FO) on resting metabolic rate (RMR), body composition, and cortisol production in healthy adults.</p> <p>Methods</p> <p>A total of 44 men and women (34 ± 13y, mean+SD) participated in the study. All testing was performed first thing in the morning following an overnight fast. Baseline measurements of RMR were measured using indirect calorimetry using a facemask, and body composition was measured using air displacement plethysmography. Saliva was collected via passive drool and analyzed for cortisol concentration using ELISA. Following baseline testing, subjects were randomly assigned in a double blind manner to one of two groups: 4 g/d of Safflower Oil (SO); or 4 g/d of FO supplying 1,600 mg/d eicosapentaenoic acid (EPA) and 800 mg/d docosahexaenoic acid (DHA). All tests were repeated following 6 wk of treatment. Pre to post differences were analyzed using a treatment X time repeated measures ANOVA, and correlations were analyzed using Pearson's r.</p> <p>Results</p> <p>Compared to the SO group, there was a significant increase in fat free mass following treatment with FO (FO = +0.5 ± 0.5 kg, SO = -0.1 ± 1.2 kg, p = 0.03), a significant reduction in fat mass (FO = -0.5 ± 1.3 kg, SO = +0.2 ± 1.2 kg, p = 0.04), and a tendency for a decrease in body fat percentage (FO = -0.4 ± 1.3% body fat, SO = +0. 3 ± 1.5% body fat, p = 0.08). No significant differences were observed for body mass (FO = 0.0 ± 0.9 kg, SO = +0.2 ± 0.8 kg), RMR (FO = +17 ± 260 kcal, SO = -62 ± 184 kcal) or respiratory exchange ratio (FO = -0.02 ± 0.09, SO = +0.02 ± 0.05). There was a tendency for salivary cortisol to decrease in the FO group (FO = -0.064 ± 0.142 μg/dL, SO = +0.016 ± 0.272 μg/dL, p = 0.11). There was a significant correlation in the FO group between change in cortisol and change in fat free mass (r = -0.504, p = 0.02) and fat mass (r = 0.661, p = 0.001).</p> <p>Conclusion</p> <p>6 wk of supplementation with FO significantly increased lean mass and decreased fat mass. These changes were significantly correlated with a reduction in salivary cortisol following FO treatment.</p

Transmembrane protein 88: A Wnt regulatory protein that specifies cardiomyocyte development

Genetic regulation of the cell fate transition from lateral plate mesoderm to the specification of cardiomyocytes requires suppression of Wnt/β-catenin signaling, but the mechanism for this is not well understood. By analyzing gene expression and chromatin dynamics during directed differentiation of human embryonic stem cells (hESCs), we identified a suppressor of Wnt/β-catenin signaling, transmembrane protein 88 (TMEM88), as a potential regulator of cardiovascular progenitor cell (CVP) specification. During the transition from mesoderm to the CVP, TMEM88 has a chromatin signature of genes that mediate cell fate decisions, and its expression is highly upregulated in advance of key cardiac transcription factors in vitro and in vivo. In early zebrafish embryos, tmem88a is expressed broadly in the lateral plate mesoderm, including the bilateral heart fields. Short hairpin RNA targeting of TMEM88 during hESC cardiac differentiation increases Wnt/β-catenin signaling, confirming its role as a suppressor of this pathway. TMEM88 knockdown has no effect on NKX2.5 or GATA4 expression, but 80% of genes most highly induced during CVP development have reduced expression, suggesting adoption of a new cell fate. In support of this, analysis of later stage cell differentiation showed that TMEM88 knockdown inhibits cardiomyocyte differentiation and promotes endothelial differentiation. Taken together, TMEM88 is crucial for heart development and acts downstream of GATA factors in the pre-cardiac mesoderm to specify lineage commitment of cardiomyocyte development through inhibition of Wnt/β-catenin signaling

Predicting protein targets for drug-like compounds using transcriptomics

An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.Fil: Pabon, Nicolas. University of Pittsburgh; Estados UnidosFil: Xia, Yan. University of Carnegie Mellon; Estados UnidosFil: Estabrooks, Samuel K.. University of Pittsburgh; Estados UnidosFil: Ye, Zhaofeng. Tsinghua University; ChinaFil: Herbrand, Amanda K.. Goethe Universitat Frankfurt; AlemaniaFil: Süß, Evelyn. Goethe Universitat Frankfurt; AlemaniaFil: Biondi, Ricardo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina. Goethe Universitat Frankfurt; AlemaniaFil: Assimon, Victoria A.. University of California; Estados UnidosFil: Gestwicki, Jason E.. University of California; Estados UnidosFil: Brodsky, Jeffrey L.. University of Pittsburgh; Estados UnidosFil: Camacho, Carlos. University of Pittsburgh; Estados UnidosFil: Bar Joseph, Ziv. University of Carnegie Mellon; Estados Unido