Multistep, sequential control of the trafficking and function of the multiple sulfatase deficiency gene product, SUMF1 by PDI, ERGIC-53 and ERp44.

Sulfatase modifying factor 1 (SUMF1) encodes for the formylglicine generating enzyme, which activates sulfatases by modifying a key cysteine residue within their catalytic domains. SUMF1 is mutated in patients affected by multiple sulfatase deficiency, a rare recessive disorder in which all sulfatase activities are impaired. Despite the absence of canonical retention/retrieval signals, SUMF1 is largely retained in the endoplasmic reticulum (ER), where it exerts its enzymatic activity on nascent sulfatases. Part of SUMF1 is secreted and paracrinally taken up by distant cells. Here we show that SUMF1 interacts with protein disulfide isomerase (PDI) and ERp44, two thioredoxin family members residing in the early secretory pathway, and with ERGIC-53, a lectin that shuttles between the ER and the Golgi. Functional assays reveal that these interactions are crucial for controlling SUMF1 traffic and function. PDI couples SUMF1 retention and activation in the ER. ERGIC-53 and ERp44 act downstream, favoring SUMF1 export from and retrieval to the ER, respectively. Silencing ERGIC-53 causes proteasomal degradation of SUMF1, while down-regulating ERp44 promotes its secretion. When over-expressed, each of three interactors favors intracellular accumulation. Our results reveal a multistep control of SUMF1 trafficking, with sequential interactions dynamically determining ER localization, activity and secretion

Power-law distributions in empirical data

Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.Comment: 43 pages, 11 figures, 7 tables, 4 appendices; code available at http://www.santafe.edu/~aaronc/powerlaws

Methyl orange photo‐degradation by tio2 in a pilot unit under different chemical, physical, and hydraulic conditions

The photo‐catalytic degradation of a textile azo‐dye as Methyl Orange was studied in an innovative unit constituted by a channel over which a layer of titanium dioxide (TiO2) catalyst in anatase form was deposited and activated by UVB irradiation. The degradation kinetics were followed after variation of the chemical, physical, and hydraulic/hydrodynamic parameters of the system. For this purpose, the influence of the TiO2 dosage (g/cm3), dye concentration (mg/L), pH of the solution, flow‐rate (L/s), hydraulic load (cm), and irradiation power (W) were evaluated on the degradation rates. It was observed that the maximum dosage of TiO2 was 0.79 g/cm3 while for higher dosage a reduction of homogeneity of the cement conglomerate occurred. The Langmuir– Hinshelwood (LH) kinetic model was followed up to a dye concentration around 1 mg/L. It was observed that with the increase of the flow rate, an increase of the degradation kinetics was obtained, while the further increase of the flow‐rate associated with the modification of the hydraulic load determined a decrease of the kinetic rates. The results also evidenced an increase of the kinetic rates with the increase of the UVB intensity. A final comparison with other dyes such as Methyl Red and Methylene Blue was carried out in consideration of the pH of the solution, which sensibly affected the removal efficiencies

Paving the way to food grade analytical chemistry: use of a natural deep eutectic solvent to determine total hydroxytyrosol and tyrosol in extra virgin olive oils

Extra virgin olive oil (EVOO) is well known for containing relevant amounts of healthy phenolic compounds. The European Food Safety Authority (EFSA) allowed a health claim for labelling olive oils containing a minimum amount of hydroxytyrosol (OHTyr) and its derivatives, including tyrosol (Tyr). Therefore, harmonized and standardized analytical protocols are required in support of an effective application of the health claim. Acid hydrolysis performed after extraction and before chromatographic analysis has been shown to be a feasible approach. Nevertheless, other fast, green, and easy methods could be useful for on-site screening and monitoring applications. In the present research, a natural deep eutectic solvent (NADES) composed of lactic acid and glucose was used to perform a liquid/liquid extraction on EVOO samples, followed by UV-spectrophotometric analysis. The spectral features of the extracts were related with the content of total OHTyr and Tyr, determined by the acid hydrolysis method. The second derivative of spectra allowed focusing on three single wavelengths (i.e., 299 nm, 290 nm, and 282 nm) significantly related with total OHTyr, total Tyr, and their sum, respectively. In particular, the sum of OHTyr and Tyr could be determined with a root mean square error of prediction of 29.5 mg/kg, while the limits of quantitation and detection were respectively 11.8 and 4.9 mg/kg. The proposed method, therefore, represents an easy screening tool, with the use of a green, food-derived solvent, and could be considered as an attempt to pave the way for food grade analytical chemistry

Homophily and Contagion Are Generically Confounded in Observational Social Network Studies

We consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individual's covariates on their behavior or other measurable responses. We show that, generically, all of these are confounded with each other. Distinguishing them from one another requires strong assumptions on the parametrization of the social process or on the adequacy of the covariates used (or both). In particular we demonstrate, with simple examples, that asymmetries in regression coefficients cannot identify causal effects, and that very simple models of imitation (a form of social contagion) can produce substantial correlations between an individual's enduring traits and their choices, even when there is no intrinsic affinity between them. We also suggest some possible constructive responses to these results.Comment: 27 pages, 9 figures. V2: Revised in response to referees. V3: Ditt

Automatic Filters for the Detection of Coherent Structure in Spatiotemporal Systems

Most current methods for identifying coherent structures in spatially-extended systems rely on prior information about the form which those structures take. Here we present two new approaches to automatically filter the changing configurations of spatial dynamical systems and extract coherent structures. One, local sensitivity filtering, is a modification of the local Lyapunov exponent approach suitable to cellular automata and other discrete spatial systems. The other, local statistical complexity filtering, calculates the amount of information needed for optimal prediction of the system's behavior in the vicinity of a given point. By examining the changing spatiotemporal distributions of these quantities, we can find the coherent structures in a variety of pattern-forming cellular automata, without needing to guess or postulate the form of that structure. We apply both filters to elementary and cyclical cellular automata (ECA and CCA) and find that they readily identify particles, domains and other more complicated structures. We compare the results from ECA with earlier ones based upon the theory of formal languages, and the results from CCA with a more traditional approach based on an order parameter and free energy. While sensitivity and statistical complexity are equally adept at uncovering structure, they are based on different system properties (dynamical and probabilistic, respectively), and provide complementary information.Comment: 16 pages, 21 figures. Figures considerably compressed to fit arxiv requirements; write first author for higher-resolution version