SESF-Fuse: an unsupervised deep model for multi-focus image fusion

Neural Computing and Applications - Muti-focus image fusion is the extraction of focused regions from different images to create one all-in-focus fused image. The key point is that only objects...     

Curcumin Prevents Cerebellar Hypoplasia and Restores the Behavior in Hyperbilirubinemic Gunn Rat by a Pleiotropic Effect on the Molecular Effectors of Brain Damage

Bilirubin toxicity to the central nervous system (CNS) is responsible for severe and permanent neurologic damage, resulting in hearing loss, cognitive, and movement impairment. Timely and effective management of severe neonatal hyperbilirubinemia by phototherapy or exchange transfusion is crucial for avoiding permanent neurological consequences, but these therapies are not always possible, particularly in low-income countries. To explore alternative options, we investigated a pharmaceutical approach focused on protecting the CNS from pigment toxicity, independently from serum bilirubin level. To this goal, we tested the ability of curcumin, a nutraceutical already used with relevant results in animal models as well as in clinics in other diseases, in the Gunn rat, the spontaneous model of neonatal hyperbilirubinemia. Curcumin treatment fully abolished the landmark cerebellar hypoplasia of Gunn rat, restoring the histological features, and reverting the behavioral abnormalities present in the hyperbilirubinemic rat. The protection was mediated by a multi-target action on the main bilirubin-induced pathological mechanism ongoing CNS damage (inflammation, redox imbalance, and glutamate neurotoxicity). If confirmed by independent studies, the result suggests the potential of curcumin as an alternative/complementary approach to bilirubin-induced brain damage in the clinical scenario.     

The GAUGAA Motif Is Responsible for the Binding between circSMARCA5 and SRSF1 and Related Downstream Effects on Glioblastoma Multiforme Cell Migration and Angiogenic Potential

Circular RNAs (circRNAs) are a large class of RNAs with regulatory functions within cells. We recently showed that circSMARCA5 is a tumor suppressor in glioblastoma multiforme (GBM) and acts as a decoy for Serine and Arginine Rich Splicing Factor 1 (SRSF1) through six predicted binding sites (BSs). Here we characterized RNA motifs functionally involved in the interaction between circSMARCA5 and SRSF1. Three different circSMARCA5 molecules (Mut1, Mut2, Mut3), each mutated in two predicted SRSF1 BSs at once, were obtained through PCR-based replacement of wild-type (WT) BS sequences and cloned in three independent pcDNA3 vectors. Mut1 significantly decreased its capability to interact with SRSF1 as compared to WT, based on the RNA immunoprecipitation assay. In silico analysis through the “Find Individual Motif Occurrences” (FIMO) algorithm showed GAUGAA as an experimentally validated SRSF1 binding motif significantly overrepresented within both predicted SRSF1 BSs mutated in Mut1 (q-value = 0.0011). U87MG and CAS-1, transfected with Mut1, significantly increased their migration with respect to controls transfected with WT, as revealed by the cell exclusion zone assay. Immortalized human brain microvascular endothelial cells (IM-HBMEC) exposed to conditioned medium (CM) harvested from U87MG and CAS-1 transfected with Mut1 significantly sprouted more than those treated with CM harvested from U87MG and CAS-1 transfected with WT, as shown by the tube formation assay. qRT-PCR showed that the intracellular pro- to anti-angiogenic Vascular Endothelial Growth Factor A (VEGFA) mRNA isoform ratio and the amount of total VEGFA mRNA secreted in CM significantly increased in Mut1-transfected CAS-1 as compared to controls transfected with WT. Our data suggest that GAUGAA is the RNA motif responsible for the interaction between circSMARCA5 and SRSF1 as well as for the circSMARCA5-mediated control of GBM cell migration and angiogenic potential.     

Hepatic damage produced by long-term alcohol consumption in well-nourished rats

A study of the effect of long-term alcohol consumption on the liver of well-nourished rats is described. Rats fed for 16 weeks on a semipurified diet supplemented with high levels of vitamins and lipotropic factors and alcohol corresponding to 35% of the total caloric intake developed marked fatty changes of the liver. Mild fatty changes were observed in pair-fed controls receiving as isoenergetic equivalent of sucrose instead of alcohol. Intracellular hyaline bodies, corresponding ultrastructurally to giant mitochondria were abundantly found in the hepatocytes of alcoholic rats, while in the controls they were not seen. Te findings in this investigation are postulated to provide further evidence that the long-term intake of alcohol exerts a direct causative role in the pathogenesis of liver damage.     

Modification of oxidizing activity of Thiobacillus ferroxidans by some particulate solids

Different particulate solids such as glass powder, various types of activated carbon and bentonite suspended in Thiobacillus ferrooxidans cultures produce very different effects on the oxidizing activity of the microorganism. The atmospheric oxygen uptake by Thiobacillus ferrooxidans in Warburg respirometer flasks containing 9K medium with ferrous sulfate as energy source and suspended bentonite is 2.8 times that in the absence of solids. The dissolution rate of pure pyrite in 250 cm³ shake flasks containing 9K medium and bentonite is considerably enhanced over that where the only solid phase is pyrite. Conversely, the oxidizing ability of the microorganism seems to be inhibited by all the activated carbons tested. The effect of ground glass seems to be related to its particle size distribution. Implications for the bioleaching of sulfide ores and coal pyrite are discussed in the light of current knowledge on the adsorption of Thiobacillus ferrooxidans cells on solid surfaces.     

Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network

Algorithms for numeric data classification have been applied for text classification. Usually the vector space model is used to represent text collections. The characteristics of this representation such as sparsity and high dimensionality sometimes impair the quality of general-purpose classifiers. Networks can be used to represent text collections, avoiding the high sparsity and allowing to model relationships among different objects that compose a text collection. Such network- based representations can improve the quality of the classification results. One of the simplest ways to represent textual collections by a network is through a bipartite heterogeneous network, which is composed of objects that represent the documents connected to objects that represent the terms. Heterogeneous bipartite networks do not require computation of similarities or relations among the objects and can be used to model any type of text collection. Due to the advantages of representing text collections through bipartite heterogeneous networks, in this article we present a text classifier which builds a classification model using the structure of a bipartite heterogeneous network. Such an algorithm, referred to as IMBHN （Inductive Model Based on Bipartite Heterogeneous Network）, induces a classification model assigning weights to objects that represent the terms for each class of the text collection. An empirical evaluation using a large amount of text collections from different domains shows that the proposed IMBHN algorithm produces significantly better results than k-NN, C4.5, SVM, and Naive Bayes algorithms.     

Experimental and theoretical investigation on unburned coal char burnout in a pilot-scale rotary kiln

Oxidation reactivity studies are imperative for improving carbon re-burn technologies and valuing the heat content of unburned carbon within coal combustion ashes. Non-isothermal, thermal gravimetric analysis (TGA) was used to examine the oxidation kinetics of unburned carbon in coal combustion fly ashes having different particle size distributions; TGA results were related to combustion efficiencies as measured in a bench-scale rotary kiln. The activation energy and pre-exponential factor were determined for the chemically-controlled reaction regime; the transition temperatures between chemically-controlled and partially diffusion-controlled combustion regimes were obtained for unburned carbon particles of different sizes. After the oxidation reaction rates were evaluated, the residence time distribution (RTD) of fly ashes in the rotary kiln were experimentally measured and the mean residence times related to process parameters, including the rotating velocity and kiln inclination. By comparing these results with an advective-dispersive model, the axial dispersion coefficient of fly ashes was determined. The reaction rates obtained by thermal analyses and the RTDs were used to predict combustion efficiencies within the kiln and oxidation conditions of unburned carbon using various processing options.     

A tartrate-based alloy bath for brass-plated steel wire production

The plating of brass on steel from a tartrate based alloy bath has been studied using electrochemical methods and surface analysis techniques. The coating composition obtained using both direct current (d.c.) and pulsed current (p.c.) electrolysis was examined for a variety of temperatures, current densities and bath compositions. The dissolution process of a suitable composition of brass anodes was studied using small amounts of nitrate as depolarizing agent. Tests in a pilot plant showed that when electrolysis is carried outin continuo the coating composition remains constant with time (±1%). Tests were performed at cathode current densities of industrial interest (20–30 A dm^–2). The Auger and XPS analyses combined with Ar⁺ etching indicated a homogeneous composition throughout the coating.     

Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks

Anomaly detection in resource constrained wireless networks is an important challenge for tasks such as intrusion detection, quality assurance and event monitoring applications. The challenge is to detect these interesting events or anomalies in a timely manner, while minimising energy consumption in the network. We propose a distributed anomaly detection architecture, which uses multiple hyperellipsoidal clusters to model the data at each sensor node, and identify global and local anomalies in the network. In particular, a novel anomaly scoring method is proposed to provide a score for each hyperellipsoidal model, based on how remote the ellipsoid is relative to their neighbours. We demonstrate using several synthetic and real datasets that our proposed scheme achieves a higher detection performance with a significant reduction in communication overhead in the network compared to centralised and existing schemes.     

Collective decision making: a great opportunity for constraint reasoning

Collective decision making is an area of increasingly growing interest, mainly due to the rise of many IT-enabled environments where people connect and share information with others. We believe that constraint reasoning can have a major impact in this field, by providing general and flexible frameworks to model agents’ preferences over the alternative decisions, efficient algorithms to compute the best individual and collective decisions, and innovative approaches to deal with missing information. However, in order to do this, we claim that constraint reasoning should increase its efforts to open up to other research areas, such as voting and game theory, multi-agent systems, machine learning, and reasoning under uncertainty.