Documenti sui giudici d'Arborea nei protocolli di Bartomeu de Miramat e Pere Martí - Arxiu Històric de Protocols de Barcelona (1336-1362)

El ensayo propone, a través dela lectura de algunos documentos notariales barceloneses, una reflexión sobre los jueces de Arborea en una dimensión privada, lejana de las actividades bélicas que han sido bien estudiadas por la historiografía. El objetivo es restituir una imagen de la familia judicial y de sus miembros en la vida privada, que permita observar su estatuto de nobles, sus hábitos y costumbres similares a los de las otras familias nobles de la época, así como los vínculos establecidos con Cataluña

BSA Fragmentation Specifically Induced by Added Electrolytes: an Electrospray Ionization Mass Spectrometry Investigation

Biointerfaces are significantly affected by electrolytes according to the Hofmeister series. This work reports a systematic investigation on the effect of different metal chlorides, sodium and potassium bromides, iodides and thiocyanates, on the ESI/MS spectra of bovine serum albumin (BSA) in aqueous solution at pH = 2.7. The concentration of each salt was varied to maximize the quality of the ESI/MS spectrum, in terms of peak intensity and bell-shaped profile. The ESI/MS spectra of BSA in the absence and in the presence of salts showed a main protein pattern characterized by the expected mass of 66.5 kDa, except the case of BSA/RbCl (mass 65,3 kDa). In all systems we observed an additional pattern, characterized by at least three peaks with low intensity, whose deconvolution led to suggest the formation of a BSA fragment with a mass of 19.2 kDa. Only NaCl increased the intensity of the peaks of the main BSA pattern, while minimizing that of the fragment. NaCl addition seems to play a crucial role in stabilizing BSA ionized interface against hydrolysis of peptide bonds, through different synergistic mechanisms. To quantify the observed specific electrolyte effects, two “Hofmeister” parameters (Hs and Ps) are proposed. They are obtained using the ratio of (BSA-Salt)/BSA peak intensities for both the BSA main pattern and for its fragment

Accurate and efficient target prediction using a potency-sensitive influence-relevance voter

BackgroundA number of algorithms have been proposed to predict the biological targets of diverse molecules. Some are structure-based, but the most common are ligand-based and use chemical fingerprints and the notion of chemical similarity. These methods tend to be computationally faster than others, making them particularly attractive tools as the amount of available data grows.ResultsUsing a ChEMBL-derived database covering 490,760 molecule-protein interactions and 3236 protein targets, we conduct a large-scale assessment of the performance of several target-prediction algorithms at predicting drug-target activity. We assess algorithm performance using three validation procedures: standard tenfold cross-validation, tenfold cross-validation in a simulated screen that includes random inactive molecules, and validation on an external test set composed of molecules not present in our database.ConclusionsWe present two improvements over current practice. First, using a modified version of the influence-relevance voter (IRV), we show that using molecule potency data can improve target prediction. Second, we demonstrate that random inactive molecules added during training can boost the accuracy of several algorithms in realistic target-prediction experiments. Our potency-sensitive version of the IRV (PS-IRV) obtains the best results on large test sets in most of the experiments. Models and software are publicly accessible through the chemoinformatics portal at http://chemdb.ics.uci.edu/

Writing a novel, chapter one: precedents and decision process in the Brazilian Supreme Court

O artigo, por meio do estudo de casos exemplificativos, procura mostrar que um dos motivos para a falta de uma cultura de respeito aos precedentes judiciais no Supremo Tribunal Federal (STF), ou para a falta de um romance em cadeia (Dworkin), é a dificuldade de formação de uma ratio decidendi comum entre os ministros nos julgamentos da corte, em virtude, por exemplo, do próprio processo decisório do Tribunal. A falta de padrões de decisão implica que cada caso seja decidido sem referência a casos previamente relacionados. Esse contexto pode colaborar para a falta de transparência decisória e para o que pode ser considerado um deficit democrático do STF.Through the analyses of landmark cases, this article argues that one reason for the tendency by the Brazilian Supreme Court (STF) to disregard judicial precedents is the difficulty to create a common ratio decidendi in Court decisions and prevent the emergence od Dworkin's chain of law. This is due, in part, to the court's own decision process. The lack of a decision pattern entails that each case is decided without refecenre to previous cases. This context might foster an atmosphere in which decisions are not transparent, something which risks creating a democratic deficit on the STF

Machine Learning, Quantum Mechanics, and Chemical Compound Space

We review recent studies dealing with the generation of machine learning models of molecular and solid properties. The models are trained and validated using standard quantum chemistry results obtained for organic molecules and materials selected from chemical space at random

Greedy and linear ensembles of machine learning methods outperform single approaches for QSPR regression problems

The application of Machine Learning to cheminformatics is a large and active field of research, but there exist few papers which discuss whether ensembles of different Machine Learning methods can improve upon the performance of their component methodologies. Here we investigated a variety of methods, including kernel-based, tree, linear, neural networks, and both greedy and linear ensemble methods. These were all tested against a standardised methodology for regression with data relevant to the pharmaceutical development process. Thinvestigation focused on QSPR problems within drug-like chemical space. We aimed to investigate which methods perform best, and how the ‘wisdom of crowds’ principle can be applied to ensemble predictors. It was found that no single method performs best for all problems, but that a dynamic, well-structured ensemble predictor would perform very well across the board, usually providing an improvement in performance over the best single method. Its use of weighting factors allows the greedy ensemble to acquire a bigger contribution from the better performing models, and this helps the greedy ensemble generally to outperform the simpler linear ensemble. Choice of data pre-processing methodology was found to be crucial to performance of each method too.PostprintPeer reviewe

Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest -and hope -that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited -in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted. Improving the performance of machine-learning methods calls for the development of fundamentally new chemical descriptors