Augmented Sparse Reconstruction of Protein Signaling Networks

The problem of reconstructing and identifying intracellular protein signaling and biochemical networks is of critical importance in biology today. We sought to develop a mathematical approach to this problem using, as a test case, one of the most well-studied and clinically important signaling networks in biology today, the epidermal growth factor receptor (EGFR) driven signaling cascade. More specifically, we suggest a method, augmented sparse reconstruction, for the identification of links among nodes of ordinary differential equation (ODE) networks from a small set of trajectories with different initial conditions. Our method builds a system of representation by using a collection of integrals of all given trajectories and by attenuating block of terms in the representation itself. The system of representation is then augmented with random vectors, and minimization of the 1-norm is used to find sparse representations for the dynamical interactions of each node. Augmentation by random vectors is crucial, since sparsity alone is not able to handle the large error-in-variables in the representation. Augmented sparse reconstruction allows to consider potentially very large spaces of models and it is able to detect with high accuracy the few relevant links among nodes, even when moderate noise is added to the measured trajectories. After showing the performance of our method on a model of the EGFR protein network, we sketch briefly the potential future therapeutic applications of this approach.Comment: 24 pages, 6 figure

Strontium and iron-doped barium cobaltite prepared by solution combustion synthesis: exploring a mixed-fuel approach for tailored intermediate temperature solid oxide fuel cell cathode materials

Ba0.5Sr0.5Co0.8Fe0.2O3–δ (BSCF) powders were prepared by solution combustion synthesis using single and double fuels. The effect of the fuel mixture on the main properties of this well-known solid oxide fuel cell cathode material with high oxygen ion and electronic conduction was investigated in detail. Results showed that the fuel mixture significantly affected the area-specific resistance of the BSCF cathode materials, by controlling the oxygen deficiency and stabilizing the Co2+ oxidation state. It was demonstrated that high fuel-to-metal cations molar ratios and high reducing power of the combustion fuel mixture are mainly responsible for the decreasing of the area-specific resistance of BSCF cathode materials. Moreover, a new metastable monoclinic phase with Ba0.5Sr0.5CO3 composition was discovered in the as-burned BSCF powders, enlarging the existing information on the BSCF phase formation mechanis

Big IoT data mining for real-time energy disaggregation in buildings (extended abstract)

In the smart grid context, the identification and prediction of building energy flexibility is a challenging open question. In this paper, we propose a hybrid approach to address this problem. It combines sparse smart meters with deep learning methods, e.g. Factored Four-Way Conditional Restricted Boltzmann Machines (FFW-CRBMs), to accurately predict and identify the energy flexibility of buildings unequipped with smart meters, starting from their aggregated energy values. The proposed approach was validated on a real database, namely the Reference Energy Disaggregation Dataset

Online contrastive divergence with generative replay: experience replay without storing data

Conceived in the early 1990s, Experience Replay (ER) has been shown to be a successful mechanism to allow online learning algorithms to reuse past experiences. Traditionally, ER can be applied to all machine learning paradigms (i.e., unsupervised, supervised, and reinforcement learning). Recently, ER has contributed to improving the performance of deep reinforcement learning. Yet, its application to many practical settings is still limited by the memory requirements of ER, necessary to explicitly store previous observations. To remedy this issue, we explore a novel approach, Online Contrastive Divergence with Generative Replay (OCD_GR), which uses the generative capability of Restricted Boltzmann Machines (RBMs) instead of recorded past experiences. The RBM is trained online, and does not require the system to store any of the observed data points. We compare OCD_GR to ER on 9 real-world datasets, considering a worst-case scenario (data points arriving in sorted order) as well as a more realistic one (sequential random-order data points). Our results show that in 64.28% of the cases OCD_GR outperforms ER and in the remaining 35.72% it has an almost equal performance, while having a considerably reduced space complexity (i.e., memory usage) at a comparable time complexity

Effects due to Resonant and Continuum States on the Neutrino-Nucleus Cross Section

Estimates of the neutrino-nucleus cross section, for the charged-current process nu+208Pb-> e+208Bi, are presented. The nuclear structure calculations have been performed by considering bound, resonant, and continuum states in the single-particle basis used to construct correlated proton-particle neutron-hole configurations. The observed features of the spectrum of 208Bi have been reproduced, as accurately as possible, by diagonalizing a phenomenological multipole-multipole interaction. Calculations of the cross section, for values of q 200 $ MeV, were performed, and the dependence of the results upon the choice of the residual proton-neutron interaction was investigated. It is found that the inclusion of resonant states in the calculation of the nuclear wave functions increases the neutrino-nucleus cross section, and that the contribution of the continuum is negligible.Comment: 15 pages, 6 figures, 2 tables, 39 references. submitted to Physical Review

Nero Siciliano pig: analysis of coat colour affecting genes and perspectives for breed traceability

Nero Siciliano is an autochthonous pig breed reared in the internal areas of Sicily region mainly in the Nebrodi mountains. The animals are usually completely black with a dorsal stripe but a few present white portions mainly in the face or in the fore legs. According to the increased requests of the consumers for local and typical products, meat and cured products of Nero Siciliano pigs are sold at a higher price compared to other pig products. Thus there is the need to guarantee both consumers and the whole Nero Siciliano production chain from possible frauds. The identification and/or use of DNA markers that may be breed specific could make it possible to establish breed traceability and authenticity systems for the products obtained with this local pig breed. Mutations in coat colour genes have been already described and utilized for porcine breed traceability. In this trial we analysed mutations identified in two coat colour affecting genes, the melanocortin 1 receptor (MC1R) and the v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene (KIT), with the aim to characterize the Nero Siciliano pig at these loci and provide useful information to establish authenticity systems for the meat products. Fragment analysis of PCR products and PCRRFLP methods were used to identify the polymorphic sites that can distinguish known alleles at these two loci in 104 Nero Siciliano pigs. Four alleles were identified at the MC1R locus: the two dominant black alleles (ED2, frequency of 0.673; ED1, 0.187), allele EP (0.106) and the recessive e allele (0.034). The results showed that different alleles were observed at this locus, polymorphisms at the MC1R gene cannot be used for product traceability and authentication of this breed. As regards the KIT locus, all the animals were negative for the splice site mutation of exon/intron 17. Thus, meat of Nero Siciliano pigs can be distinguished from meat of white pigs that are positive for this polymorphic site. Moreover, at this locus only 4 pigs showed the 3'-5' duplication breakpoint suggesting that they carried the Ip allele. Studies are in progress to evaluate the effect of this allele on coat colour phenotypes in Nero Siciliano pig

Milk composition of "Nero Siciliano" sow. Preliminary results

AbstractQuantitative and qualitative milk production is the basis for determining the nutritional requirements of lactating sow; indeed, the gross nutrient composition of sow's milk is frequently used as a suitable starting point when formulating milk-replacer diets for piglets. Data about the sow's milk can be found from the literature but, in authors knowledge, no data on milk composition of Nero Siciliano sow exist. This study reports the preliminary results concerning some physical and chemical characteristics of the milk of this autochthonous Sicilian pig race during the lactation.The research was carried out on 10 "Nero Siciliano" sows, 4 primiparous (age: 9-12 months) and 6 pluriparous (age: 2-5 years), stabled in single boxes and fed with a concentrate. From the 10th day after farrowing to the weaning (day 58th), every week, in the morning, the sows were injected with 5 IU oxytocin (i.m.) and hand-milked; all functional mammary glands were milked. Piglets were removed and isolated from the dams fo..