Fibrosis of Peritoneal Membrane as Target of New Therapies in Peritoneal Dialysis

Peritoneal dialysis (PD) is an efficient renal replacement therapy for patients with end-stage renal disease. Even if it ensures an outcome equivalent to hemodialysis and a better quality of life, in the long-term, PD is associated with the development of peritoneal fibrosis and the consequents patient morbidity and PD technique failure. This unfavorable effect is mostly due to the bio-incompatibility of PD solution (mainly based on high glucose concentration). In the present review, we described the mechanisms and the signaling pathway that governs peritoneal fibrosis, epithelial to mesenchymal transition of mesothelial cells, and angiogenesis. Lastly, we summarize the present and future strategies for developing more biocompatible PD solutions.     

Focused Design of Novel Cyclic Peptides Endowed with GABARAP-Inhibiting Activity

(1) Background: Disfunctions in autophagy machinery have been identified in various conditions, including neurodegenerative diseases, cancer, and inflammation. Among mammalian autophagy proteins, the Atg8 family member GABARAP has been shown to be greatly involved in the autophagy process of prostate cancer cells, supporting the idea that GABARAP inhibitors could be valuable tools to fight the progression of tumors. (2) Methods: In this paper, starting from the X-ray crystal structure of GABARAP in a complex with an AnkirinB-LIR domain, we identify two new peptides by applying in silico drug design techniques. The two ligands are synthesized, biophysically assayed, and biologically evaluated to ascertain their potential anticancer profile. (3) Results: Two cyclic peptides (WC8 and WC10) displayed promising biological activity, high conformational stability (due to the presence of disulfide bridges), and K_d values in the low micromolar range. The anticancer assays, performed on PC-3 cells, proved that both peptides exhibit antiproliferative effects comparable to those of peptide K1, a known GABARAP inhibitor. (4) Conclusions: WC8 and WC10 can be considered new GABARAP inhibitors to be employed as pharmacological tools or even templates for the rational design of new small molecules.     

Effects of Long-Term Temozolomide Treatment on Glioblastoma and Astrocytoma WHO Grade 4 Stem-like Cells

Glioblastoma leads to a fatal course within two years in more than two thirds of patients. An essential cornerstone of therapy is chemotherapy with temozolomide (TMZ). The effect of TMZ is counteracted by the cellular repair enzyme O⁶-methylguanine-DNA methyltransferase (MGMT). The MGMT promoter methylation, the main regulator of MGMT expression, can change from primary tumor to recurrence, and TMZ may play a significant role in this process. To identify the potential mechanisms involved, three primary stem-like cell lines (one astrocytoma with the mutation of the isocitrate dehydrogenase (IDH), CNS WHO grade 4 (HGA)), and two glioblastoma (IDH-wildtype, CNS WHO grade 4) were treated with TMZ. The MGMT promoter methylation, migration, proliferation, and TMZ-response of the tumor cells were examined at different time points. The strong effects of TMZ treatment on the MGMT methylated cells were observed. Furthermore, TMZ led to a loss of the MGMT promoter hypermethylation and induced migratory rather than proliferative behavior. Cells with the unmethylated MGMT promoter showed more aggressive behavior after treatment, while HGA cells reacted heterogenously. Our study provides further evidence to consider the potential adverse effects of TMZ chemotherapy and a rationale for investigating potential relationships between TMZ treatment and change in the MGMT promoter methylation during relapse.     

A semantic role labelling-based framework for learning ontologies from Spanish documents

Currently, most of the information available in the Web is adapted primarily for human consumption, but there is so much information that can no longer be processed by a person in a reasonable time, either in digital or physical formats. To solve this problem, the idea of the Semantic Web arose. The Semantic Web deals with adding machine-readable information to Web pages. Ontologies represent a very important element of this web, as they provide a valid and robust structure to represent knowledge based on concepts, relations, axioms, etc. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semiautomatic methods to learn ontologies. In this sense, this paper proposes a new ontology learning methodology based on semantic role labeling from digital Spanish documents. The method makes it possible to represent multiple semantic relations specially taxonomic and partonomic ones in the standardized OWL 2.0. A set of experiments has been performed with the approach implemented in educational domain that show promising results.     

SWTRACK: An intelligent model for cargo tracking based on off-the-shelf mobile devices

Logistics represent the biggest cost for transportation companies. Companies are investing in monitoring and tracking systems aiming at improving services, reducing costs and ensuring the safety in cargos transports. This article presents an intelligent model to obtain the position of vehicles, using off-the-shelf mobile devices named SWTRACK. The proposal allows distributors companies to track loads and to have real-time information about their whereabouts during a travel. SWTRACK presents an automatic travel management to initialize and finish travels without user interaction. Furthermore, it provides a mechanism to monitor detours in planned routes and to send alarms notification through mobile devices. To build that mechanism, we studied the Geofence concept and existing techniques to prepare two solutions based on continuous monitoring. Those solutions were compared and discussed. We also conducted a model evaluation using off-the-shelf mobile devices. Moreover, we provide an impact analysis regarding GPS precision and battery power consumption. In the end, we show that the proposed model generates a reliable system for tracking vehicles.     

A multi-expert system for ranking patents: An approach based on fuzzy pay-off distributions and a TOPSIS–AHP framework

The aim of this paper is to introduce a decision support system that ranks patents based on multiple expert evaluations. The presented approach starts with the creation of three value scenarios for each considered patent by each expert. These are used for the construction of individual fuzzy pay-off distribution functions for the patent value; a consensual fuzzy pay-off distribution is then determined starting from the individual distributions. Possibilistic moments are calculated from the consensus pay-off distribution for each patent and used in ranking them with TOPSIS. It is further showed how the analytic hierarchy process (AHP) can be used to include additional decision variables into the patent selection, thus allowing for a two-tier decision making process. The system is illustrated with a numerical example and the usability of the system and the combination of methods it includes for patent portfolio selection in the real world context is discussed.     

Complex and dynamic population structures: synthesis, open questions, and future directions

The population structure of an evolutionary algorithm influences the dissemination and mixing of advantageous alleles, and therefore affects search performance. Much recent attention has focused on the analysis of complex population structures, characterized by heterogeneous connectivity distributions, non-trivial clustering properties, and degree–degree correlations. Here, we synthesize the results of these recent studies, discuss their limitations, and highlight several open questions regarding (1) unsolved theoretical issues and (2) the practical utility of complex population structures for evolutionary search. In addition, we will discuss an alternative complex population structure that is known to significantly influence dynamical processes, but has yet to be explored for evolutionary optimization. We then shift our attention toward dynamic population structures, which have received markedly less attention than their static counterparts. We will discuss the strengths and limitations of extant techniques and present open theoretical and experimental questions and directions for future research. In particular, we will focus on the prospects of “active linking,” wherein edges are dynamically rewired according to the genotypic or phenotypic properties of individuals, or according to the success of prior inter-individual interactions.     

Extending the information delivery manual approach to identify information requirements for performance analysis of HVAC systems

Heating, Ventilation and Air-Conditioning (HVAC) systems account for more than 15% of the total energy consumption in the US. In order to improve the energy efficiency of HVAC systems, researchers have developed hundreds of algorithms to automatically analyze their performance. However, the complex information, such as configurations of HVAC systems, layouts and materials of building elements and dynamic data from the control systems, required by these algorithms inhibits the process of deploying them in real-world facilities. To address this challenge, we envision a framework that automatically integrates the required information items and provides them to the performance analysis algorithms for HVAC systems. This paper presents an approach to identify and document the information requirements from the publications that describe these algorithms. We extend the Information Delivery Manual (IDM) approach so that the identified information requirements can be mapped to multiple information sources that use various formats and schemas. This paper presents the extensions to the IDM approach and the results of using it to identify information requirements for performance analysis algorithms of HVAC systems.     

Fault tolerant network design inspired by Physarum polycephalum

Physarum polycephalum, a true slime mould, is a primitive, unicellular organism that creates networks to transport nutrients while foraging. The design of these natural networks proved to be advanced, e.g. the slime mould was able to find the shortest path through a maze. The underlying principles of this design have been mathematically modelled in literature. As in real life the slime mould can design fault tolerant networks, its principles can be applied to the design of man-made networks. In this paper, an existing model and algorithm are adapted and extended with stimulation and migration mechanisms which encourage formation of alternative paths, optimize edge positioning and allow for automated design. The extended model can then be used to better design fault tolerant networks. The extended algorithm is applied to several national and international network configurations. Results show that the extensions allow the model to capture the fault tolerance requirements more accurately. The resulting extended algorithm overcomes weaknesses in geometric graph design and can be used to design fault tolerant networks such as telecommunication networks with varying fault tolerance requirements.     

Impact of Ge-Sb-Te compound engineering on the set operation performance in phase-change memories

The phase-change memory (PCM) technology is considered as one of the most attractive non-volatile memory concepts for next generation data storage. It relies on the ability of a chalcogenide material belonging to the Ge-Sb-Te compound system to reversibly change its phase between two stable states, namely the poly-crystalline low-resistive state and the amorphous high-resistive state, allowing the storage of the logical bit. A careful study of the phase-change material properties in terms of the set operation performance, the program window and the electrical switching parameters as a function of composition is very attractive in order to enlarge the possible PCM application spectrum. Concerning the set performance, a crystallization kinetics based interpretation of the observed behavior measured on different Ge-Sb-Te compounds is provided, allowing a physics-based comprehension of the reset-to-set transition.