Some remarks on the homogenization of the dam problem

The homogenization of the dam problem is discussed in the general weak formulation by means of the technique of correctors. In the two special cases of horizontal layers and of the isotropic coefficients with separated variables the problem is solved. However, in general, the unsaturated region is not stable for the homogenization, as a counterexample by Alt shows.     

Neural Networks for Financial Time Series Forecasting

Financial and economic time series forecasting has never been an easy task due to its sensibility to political, economic and social factors. For this reason, people who invest in financial markets and currency exchange are usually looking for robust models that can ensure them to maximize their profile and minimize their losses as much as possible. Fortunately, recently, various studies have speculated that a special type of Artificial Neural Networks (ANNs) called Recurrent Neural Networks (RNNs) could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast: (i) the closing price of eight stock market indexes; and (ii) the closing price of six currency exchange rates related to the USD, using the RNNs model and its variants: the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The results show that the GRU gives the overall best results, especially for the univariate out-of-sample forecasting for the currency exchange rates and multivariate out-of-sample forecasting for the stock market indexes.     

Inclusion Complex between Local Anesthetic/2-hydroxypropyl-β-cyclodextrin in Stealth Liposome

The drugs delivery system in the treatment of diseases has advantages such as reduced toxicity, increased availability of the drug, etc. Therefore, studies of the supramolecular interactions between local anesthetics (LAs) butamben (BTB) or ropivacaine (RVC) complexed with 2-hydroxypropyl-β-cyclodextrin (HP-βCD) and carried in Stealth liposomal (SL) are performed. ¹H-NMR nuclear magnetic resonance (DOSY and STD) were used as the main tools. The displacements observed in the ¹H-NMR presented the complexion between LAs and HP-βCD. The diffusion coefficients of free BTB and RVC were 7.70 × 10⁻¹⁰ m² s⁻¹ and 4.07 × 10⁻¹⁰ m² s⁻¹, and in the complex with HP-βCD were 1.90 × 10⁻¹⁰ m² s⁻¹ and 3.64 × 10⁻¹⁰ m² s⁻¹, respectively, which indicate a strong interaction between the BTB molecule and HP-βCD (98.3% molar fraction and Ka = 72.279 L/mol). With STD-NMR, the encapsulation of the BTB/HP-βCD and RVC/HP-βCD in SL vesicles was proven. Beyond the saturation transfer to the LAs, there is the magnetization transfer to the hydrogens of HP-βCD. BTB and RVC have already been studied in normal liposome systems; however, little is known of their behavior in SL.     

Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20–25% inhibition of RIPK1’s kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.     

Vibrational analysis of d-PCL(530)/siloxane-based hybrid electrolytes doped with two lithium salts

The present study has been focused on environmentally friendly sol–gel-derived electrolytes based on a di-urethane cross-linked d-PCL(530)/siloxane network [where d represents di, PCL identifies the poly(ε-caprolactone) biopolymer, and 530 is the average molecular weight in grams per mole] doped with a wide range of concentration of lithium perchlorate and lithium bis(trifluoromethanesulfonyl)imide. Fourier Transform Infrared and Raman spectroscopies have been applied to evaluate the extent of ionic association. Characteristic bands of the PCL(530) segments, of the urethane cross-links, and of the anions have been examined to gain insight into the cation/biopolymer, cation/anion, and cation/cross-link interactions. In both electrolyte systems, “free” ions and contact ions have been identified. The addition of salt modifies the hydrogen-bonded array of the host matrix, causing the destruction/formation of the urethane/urethane aggregates.     

Fingerprinting pre-historical symbolic artefacts by a non-destructive methodological approach

Journal of Radioanalytical and Nuclear Chemistry - A contribution to the discussion about Vila Nova de São Pedro (VNSP) as a production centre of symbolic lithic artefacts, the origin of raw...     

The influence of hubs in the structure of a neuronal network during an epileptic seizure

In this work, we propose changes in the structure of a neuronal network with the intention to provoke strong synchronization to simulate episodes of epileptic seizure. Starting with a network of Izhikevich neurons we slowly increase the number of connections in selected nodes in a controlled way, to produce (or not) hubs. We study how these structures alter the synchronization on the spike firings interval, on individual neurons as well as on mean values, as a function of the concentration of connections for random and non-random (hubs) distribution. We also analyze how the post-ictal signal varies for the different distributions. We conclude that a network with hubs is more appropriate to represent an epileptic state.