Reusable Polyacrylonitrile-Sulfur Extractor of Heavy Metal Ions from Wastewater

Mercury, lead, and cadmium are among the most toxic and carcinogenic heavy metal ions (HMIs), posing serious threats to the sustainability of aquatic ecosystems and public health. There is an urgent need to remove these ions from water by a cheap but green process. Traditional methods have insufficient removal efficiency and reusability. Structurally robust, large surface-area adsorbents functionalized with high-selectivity affinity to HMIs are attractive filter materials. Here, an adsorbent prepared by vulcanization of polyacrylonitrile (PAN), a nitrogen-rich polymer, is reported, giving rise to PAN-S nanoparticles with cyclic π-conjugated backbone and electronic conductivity. PAN-S can be coated on ultra-robust melamine (ML) foam by simple dipping and drying. In agreement with hard/soft acid/base theory, N- and S-containing soft Lewis bases have strong binding to Hg²⁺, Pb²⁺, Cu²⁺, and Cd²⁺, with extraordinary capture efficiency and performance stability. Furthermore, the used filters, when collected and electrochemically biased in a recycling bath, can release the HMIs into the bath and electrodeposit on the counter-electrode as metallic Hg⁰, Pb⁰, Cu⁰, and Cd⁰, and the PAN-S@ML filter can then be reused at least 6 times as new. The electronically conductive PAN-S@ML filter can be fabricated cheaply and holds promise for scale-up applications.     

Analysis of optical surfaces by means of surface plasmonspectroscopy

The sensitivity to material parameters of surface plasmon oscillations is explored for the design of a prototype system operating at a wavelength of 670 nm, for the simultaneous determination of thickness, complex permittivity and surface roughness of metal films. The apparatus was employed for characterization of gold and silver films with thicknesses within the 10-100 nm range. Thickness data measured with the prototype system were well correlated with those measured on a conventional surface profilometer. The Fourier spectrum of surface irregularities extracted from scattered light, enhanced by surface plasmon oscillations, was used to determine surface roughness parameters for the distinct samples. Those parameters were shown to be representative of typical surface structures observed with the atomic force microscope     

TGF-beta-induced apoptosis of cerebellar granule neurons is prevented by depolarization

The regulation of programmed cell death in the developing nervous system involves target-derived survival factors, afferent synaptic activity, and hormone- and cytokine-dependent signaling. Cultured immature cerebellar granule neurons die by apoptosis within several days in vitro unless maintained in depolarizing (high) concentrations of potassium (25 mM K+). Here we report that transforming growth factors (TGF)-beta1, -beta2, and -beta3 accelerate apoptosis of these neurons when maintained in physiological (low) K+ medium (5mM K+) as assessed by measures of viability, quantitative DNA fragmentation, and nuclear morphology. TGF-beta-induced apoptosis of these neurons is not blocked by CNTF and LIF, cytokines that enhance neuronal survival when applied alone, or by IGF-I, which prevents apoptosis upon potassium withdrawal. In contrast, neurons that differentiate in high K+ medium for several days in vitro acquire resistance to TGF-beta-mediated cell death. Granule neurons maintained in either low or high K+ medium produce latent, but not bioactive, TGF-beta1 and -beta2. Because neutralizing TGF-beta antibodies fail to augment survival of low K+ neurons, the cerebellar neurons are apparently unable to activate latent TGF-beta. Thus, apoptosis of low K+ neurons is not attributable to endogenous production of TGF-beta. Taken together, our data suggest that TGF-beta may limit the expansion of postmitotic neuronal precursor populations by promoting their apoptosis but may support survival of those neurons that have maturated, differentiated, and established supportive synaptic connectivity.     

Defensive allomones in three species ofHypselodoris (gastropoda: Nudibranchia) from the Cantabrian sea

ThreeHypselodoris species,H. villafranca, H. cantabrica, and H. tricolor, have been studied in order to further investigate the defensive strategies of Chromodorididae mollusks from Iberian coasts. All animals possess large amounts of furanosesquiterpenoids, probably derived from their diet of sponges. The products are transferred and stored in specific sites of the mantle (mantle dermal formations, MDFs) to be used as chemical deterrents against predators. Chemical analysis of CantabrianHypselodoris species has led to the characterization of eight sesquiterpenoids. Among these, six have been found previously in both sponges and nudibranchs, while two are new natural products.Hypselodoris species cooccurring in the same area contained comparable mixtures of sesquiterpenoids. However, two populations ofH. cantabrica from different sites of the Cantabrian sea showed different metabolic patterns. All sesquiterpenoids induce feeding deterrence inCarassius auratus and two are also toxic toGambusia affinis.     

An approach to reduce energy consumption and performance losses on heterogeneous servers using power capping

Journal of Scheduling - Rapid growth of demand for remote computational power, along with high energy costs and infrastructure limits, has led to treating power usage as a primary constraint in...     

Comparing and experimenting machine learning techniques for code smell detection

Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.     

Gossip-based aggregation of trust in decentralized reputation systems

Decentralized Reputation Systems have recently emerged as a prominent method of establishing trust among self-interested agents in online environments. A key issue is the efficient aggregation of data in the system; several approaches have been proposed, but they are plagued by major shortcomings. We put forward a novel, decentralized data management scheme grounded in gossip-based algorithms. Rumor mongering is known to possess algorithmic advantages, and indeed, our framework inherits many of their salient features: scalability, robustness, a global perspective, and simplicity. We demonstrate that our scheme motivates agents to maintain a very high reputation, by showing that the higher an agent’s reputation is above the threshold set by its peers, the more transactions it would be able to complete within a certain time unit. We analyze the relation between the amount by which an agent’s average reputation exceeds the threshold and the time required to close a deal. This analysis is carried out both theoretically, and empirically through a simulation system called GossipTrustSim. Finally, we show that our approach is inherently impervious to certain kinds of attacks. A preliminary version of this article appeared in the proceedings of IJCAI 2007.     

A structural linearization principle for processes

In [11], an induction principle for processes was given which allows one to apply model-checking techniques to parameterized families of processes. A limitation of the induction principle is that it does not apply to the case in which one process depends directly upon a parameterized number of processes, which grows without bound. This would seem to preclude its application to families ofN processes interconnected in a star topology. Nonetheless, we show that if the dependency can be computed incrementally, then the direct dependency upon the parameterized number of processes may be re-expressed recursively in terms of a linear cascade of processes, yielding in effect a linearization of the inter-process dependencies and allowing the induction principle to apply.A previous version of this paper appears in the Proceedings of CAV 1993 (LNCS 697).     

Engineered Multifunctional Albumin‐Decorated Porous Silicon Nanoparticles for FcRn Translocation of Insulin

The last decade has seen remarkable advances in the development of drug delivery systems as alternative to parenteral injection‐based delivery of insulin. Neonatal Fc receptor (FcRn)‐mediated transcytosis has been recently proposed as a strategy to increase the transport of drugs across the intestinal epithelium. FcRn‐targeted nanoparticles (NPs) could hijack the FcRn transcytotic pathway and cross the epithelial cell layer. In this study, a novel nanoparticulate system for insulin delivery based on porous silicon NPs is proposed. After surface conjugation with albumin and loading with insulin, the NPs are encapsulated into a pH‐responsive polymeric particle by nanoprecipitation. The developed NP formulation shows controlled size and homogeneous size distribution. Transmission electron microscopy (TEM) images show successful encapsulation of the NPs into pH‐sensitive polymeric particles. No insulin release is detected at acidic conditions, but a controlled release profile is observed at intestinal pH. Toxicity studies show high compatibility of the NPs with intestinal cells. In vitro insulin permeation across the intestinal epithelium shows approximately fivefold increase when insulin is loaded into FcRn‐targeted NPs. Overall, these FcRn‐targeted NPs offer a toolbox in the development of targeted therapies for oral delivery of insulin.