The Dinuclear Copper Site Structure of Agaricus bisporus Tyrosinase in Solution Probed by X-ray Absorption Spectroscopy*

We have measured the x-ray absorption near edge structure (XANES) spectra of the enzyme tyrosinase from the mushroom Agaricus bisporus in solution in the oxy and deoxy forms. The spectra, obtained under the same conditions as the analogous forms of mollusc hemocyanin (Hc), show that the oxidation state of copper changes from Cu(II) (oxy form) to Cu(I) (deoxy form), and the copper active site(s) of A. bisporus tyrosinase in solution undergoes the same main conformational changes as Hc. We have applied the multiple scattering theory to simulate the XANES spectra of various alternative geometries of the copper site, accounting for the residual differences between Hc and tyrosinase. While oxy-Hc is reasonably fitted only by the pseudo-square-pyramidal geometry reported by its crystallographic data, oxytyrosinase can be fitted, starting from the Hc coordinates, either by distortions toward a pseudo-tetrahedral geometry, with inequivalent copper sites, or by an apically distorted square-pyramidal geometry (with an elongation of the apical distance of no more than 0.2 A)

Amphibian transition to the oxidant terrestrial environment affects the expression of glutathione S-transferases isoenzymatic pattern

AbstractIt has been postulated that glutathione S-transferases (GST; EC 2.5.1.18) may play a role in protecting against oxidative stress.In previous studies, we have purified and characterised from Bufo bufo embryos a GST isoenzyme (BbGSTP1-1), which falls at very low level in the adult liver, where a novel isoform (BbGSTP2-2), starts to be highly expressed. During transition to adult life, B. bufo leaves the aquatic environment to live predominantly in the terrestrial environment, characterised by higher oxygen concentration.It has been found that BbGSTP2-2 is more efficient in scavenging from organic hydroperoxides.Therefore, the appearance of BbGSTP2-2 may respond to the necessity of providing the adult toad with a more suitable protection against oxygen toxic by-products. In this work, we performed experiments aimed at verifying if oxidative stress (hyperoxic and H2O2 treatments) could act as a modulator of BbGSTP2-2 expression. Results show that: (a) BbGSTP2 mRNA starts to be expressed in the late embryonic period, while protein appears during metamorphosis; (b) oxidative stress induces anticipation of BbGSTP2 gene expression at both transcriptional and translational levels.These findings seem to indicate that the appearance of BbGSTP2-2 is aimed at endowing the adult toad with more efficient antioxidant defence in the terrestrial atmosphere

N-Glycomic changes in serum proteins in type 2 diabetes mellitus correlate with complications and with metabolic syndrome parameters

Background: Glycosylation, i.e the enzymatic addition of oligosaccharides (or glycans) to proteins and lipids, known as glycosylation, is one of the most common co-/posttranslational modifications of proteins. Many important biological roles of glycoproteins are modulated by N-linked oligosaccharides. As glucose levels can affect the pathways leading to glycosylation of proteins, we investigated whether metabolic syndrome (MS) and type 2 diabetes mellitus (T2DM), pathological conditions characterized by altered glucose levels, are associated with specific modifications in serum N-glycome. Methods: We enrolled in the study 562 patients with Type 2 Diabetes Mellitus (T2DM) (mean age 65.6 +/- 8.2 years) and 599 healthy control subjects (CTRs) (mean age, 58.5 +/- 12.4 years). N-glycome was evaluated in serum glycoproteins. Results: We found significant changes in N-glycan composition in the sera of T2DM patients. In particular, alpha(1,6)-linked arm monogalactosylated, core-fucosylated diantennary N-glycans (NG1(6)A2F) were significantly reduced in T2DM compared with CTR subjects. Importantly, they were equally reduced in diabetic patients with and without complications (P<0.001) compared with CTRs. Macro vascular-complications were found to be related with decreased levels of NG1(6) A2F. In addition, NG1(6) A2F and NG1(3) A2F, identifying, respectively, monogalactosylated N-glycans with alpha(1,6)- and alpha(1,3)-antennary galactosylation, resulted strongly correlated with most MS parameters. The plasmatic levels of these two glycans were lower in T2DM as compared to healthy controls, and even lower in patients with complications and MS, that is the extreme "unhealthy" phenotype (T2DM+ with MS). Conclusions: Imbalance of glycosyltransferases, glycosidases and sugar nucleotide donor levels is able to cause the structural changes evidenced by our findings. Serum N-glycan profiles are thus sensitive to the presence of diabetes and MS. Serum N-glycan levels could therefore provide a non-invasive alternative marker for T2DM and MS

Design and methodology of the screening for CKD among older patients across Europe (SCOPE) study: A multicenter cohort observational study

Background: Decline of renal function is common in older persons and the prevalence of chronic kidney disease (CKD) is rising with ageing. CKD affects different outcomes relevant to older persons, additionally to morbidity and mortality which makes CKD a relevant health burden in this population. Still, accurate laboratory measurement of kidney function is under debate, since current creatinine-based equations have a certain degree of inaccuracy when used in the older population. The aims of the study are as follows: to assess kidney function in a cohort of 75+ older persons using existing methodologies for CKD screening; to investigate existing and innovative biomarkers of CKD in this cohort, and to align

Exploiting the Reactive Power in Deep Neural Models for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is defined as the task of retrieving the active power consumption of two or more appliances from information gathered at a single metering point. In this work, the use of the reactive aggregate power as an additional feature to the commonly used active power for deep neural models is proposed. The NILM problem is formulated as a denoising problem, and denoising autoencoder (dAE) neural architectures are used to estimate the appliances individual active power consumption. The proposed approach is evaluated on two public datasets: the Almanac of Minutely Power dataset (AMPds) and the UK Domestic Appliance-Level Electricity (UK-DALE) dataset. In order to better evaluate the generalization capabilities of the algorithm, different testing conditions are considered for the UK-DALE dataset, namely a seen and an unseen scenario. The results show that introducing the reactive power can indeed bring and overall performance increase in all scenarios, ranging from +4.9% to +8.4% of the energy-based F1 score

A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks

Nowadays, measurement systems strongly rely on the Internet of Things paradigm, and typically involve miniaturized devices on purpose. In these devices, the computational resources and signal acquisition rates are limited in order to preserve battery life. In addition, the amount of streamed data is affected by the network capacity strictly related to the transmission protocol constraints and the environmental conditions. All those limitations are in contrast with the need of exploiting all possible signal details for the task under study. In the specific application of interest, i.e., Non-Intrusive Load Monitoring (NILM), they could lead to low performance in the energy disaggregation process. To overcome these issues, an ad hoc data reduction policy needs to be adopted, in order to reduce the acquisition and elaboration burden of the device, and, at the same time, to ensure compliance with network bandwidth limits while maintaining a reliable signal representation. Moved by these motivations, an extended evaluation study concerning the application of data reduction strategy to the aggregate signal is presented in this work. In particular, a non-uniform subsampling (NUS) scheme is defined together with a uniform subsampling (US) strategy and compared, in terms of disaggregation performance, with the use of data at original sampling (OS) rate. A Deep Learning based technique is used for disaggregation, having the aggregate active power signal sampled according to diverse sampling schema mentioned above as input. The approaches are tested on the UK-DALE and REDD datasets, and the combination of US+NUS configurations allows for achieving a good performance in terms of F 1 -score, even superior than the one obtained with the OS rate, and a remarkable data reduction at the same time

Exploiting temporal features and pressure data for automatic leakage detection in smart water grids

In this paper, the unsupervised approach recently proposed by the authors for automatic leakage detection in smart water grids is extended. First of all, the EPANET tool is adopted in order to simulate more realistic leakages. Also, with respect to the original work, an additional time resolution, of 30 minutes, is included, based on the water dataset of the Almanac of Minutely Power Dataset (AMPds). New experiments are performed, as well, to evaluate the results of the application of both temporal features and pressure data. The pressure data is obtained by means of the EPANEt tool, whereas the leakages are induced at run-time for a more realistic behaviour. Two alternative sets of temporal features are evaluated by combining them with the features extracted from both flow and pressure data. Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), and One-Class Support Vector Machine (OC-SVM) are used to characterize the normal data behaviour, under a comparative perspective. A feature selection strategy is adopted in computer simulations and the resulting performance indices are evaluated in terms of Area Under Curve (AUC). The obtained results show that the introduction of the temporal information produces a slight performance improvement for both flow and pressure data, but, most importantly, the combination of flow and pressure features allows a significant improvement of leakage detection for both GMM and HMM at every resolution, up to 88% of AUC