Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better

A resorcin[4]arene hexameric capsule as a supramolecular catalyst in elimination and isomerization reactions

The hexameric resorcin[4]arene capsule as a self-assembled organocatalyst promotes a series of reactions like the carbonyl–ene cyclization of (S)-citronellal preferentially to isopulegol, the water elimination from 1,1-diphenylethanol, the isomerization of α-pinene and β-pinene preferentially to limonene and minor amounts of camphene. The role of the supramolecular catalyst consists in promoting the protonation of the substrates leading to the formation of cationic intermediates that are stabilized within the cavity with consequent peculiar features in terms of acceleration and product selectivity. In all cases the catalytic activity displayed by the hexameric capsule is remarkable if compared to many other strong Brønsted or Lewis acid

β-Cyclodextrin Supramolecular Recognition of bis-Cationic Dithienylethenes

The supramolecular interactions in water between b-cyclodextrin and the open and closed photochromic forms of two bis-cationic dithienylethenes, characterized by different electronic properties, were investigated aiming at underlying the key aspects of the recognition process. The dithienylethene equipped with the cyclopentenyl unit showed a difference in binding free energies to the b-cyclodextrin between the open and closed photochromic forms of about 1 kJ/mol. Conversely, the dithienylethene equipped with the perfluorinated cyclopentenyl unit not only was a better guest but showed a three times higher difference in the binding of free energies between the open and closed isomers

Uncertainty-aware data-driven predictive control in a stochastic setting

Data-Driven Predictive Control (DDPC) has been recently proposed as an effective alternative to traditional Model Predictive Control (MPC), in that the same constrained optimization problem can be addressed without the need to explicitly identify a full model of the plant. However, DDPC is built upon input/output trajectories. Therefore, the finite sample effect of stochastic data, due to, e.g., measurement noise, may have a detrimental impact on closed-loop performance. Exploiting a formal statistical analysis of the prediction error, in this paper we propose the first systematic approach to deal with uncertainty due to finite sample effects. To this end, we introduce two regularization strategies for which, differently from existing regularization-based DDPC techniques, we propose a tuning rationale allowing us to select the regularization hyper-parameters before closing the loop and without additional experiments. Simulation results confirm the potential of the proposed strategy when closing the loop.Comment: 6 pages, 1 figure, this work has been submitted and accepted for publication at the IFAC World Congress 2023, Yokohama, Japa

Dynamic probabilistic linear discriminant analysis for video classification

Component Analysis (CA) comprises of statistical techniques that decompose signals into appropriate latent components, relevant to a task-at-hand (e.g., clustering, segmentation, classification). Recently, an explosion of research in CA has been witnessed, with several novel probabilistic models proposed (e.g., Probabilistic Principal CA, Probabilistic Linear Discriminant Analysis (PLDA), Probabilistic Canonical Correlation Analysis). PLDA is a popular generative probabilistic CA method, that incorporates knowledge regarding class-labels and furthermore introduces class-specific and sample-specific latent spaces. While PLDA has been shown to outperform several state-of-the-art methods, it is nevertheless a static model; any feature-level temporal dependencies that arise in the data are ignored. As has been repeatedly shown, appropriate modelling of temporal dynamics is crucial for the analysis of temporal data (e.g., videos). In this light, we propose the first, to the best of our knowledge, probabilistic LDA formulation that models dynamics, the so-called Dynamic-PLDA (DPLDA). DPLDA is a generative model suitable for video classification and is able to jointly model the label information (e.g., face identity, consistent over videos of the same subject), as well as dynamic variations of each individual video. Experiments on video classification tasks such as face and facial expression recognition show the efficacy of the proposed metho

Gender Stereotype Reinforcement: Measuring the Gender Bias Conveyed by Ranking Algorithms

Search Engines (SE) have been shown to perpetuate well-known gender stereotypes identified in psychology literature and to influence users accordingly. Similar biases were found encoded in Word Embeddings (WEs) learned from large online corpora. In this context, we propose the Gender Stereotype Reinforcement (GSR) measure, which quantifies the tendency of a SE to support gender stereotypes, leveraging gender-related information encoded in WEs. Through the critical lens of construct validity, we validate the proposed measure on synthetic and real collections. Subsequently, we use GSR to compare widely-used Information Retrieval ranking algorithms, including lexical, semantic, and neural models. We check if and how ranking algorithms based on WEs inherit the biases of the underlying embeddings. We also consider the most common debiasing approaches for WEs proposed in the literature and test their impact in terms of GSR and common performance measures. To the best of our knowledge, GSR is the first specifically tailored measure for IR, capable of quantifying representational harms.Comment: To appear in Information Processing & Managemen

Functional bisphosphonate synthesis for the development of new anti-resorption bone drug candidates

Herein we present the synthesis of b-mono and b-bis-substituted vinylidenebisphosphonate esters bearing a carboxylic ester moiety to be used as building blocks for further functionalizations. Reactions of these new bisphosphonate scaffolds through hydrogenation of the unsaturated CQC bond and through metal mediated addition of aryl boronic acids and indoles provide a wide range of new bisphosphonate products as potential leads to contrast osteoporosis

Operational Risk framework and Standardised Measurement Approach (SMA)

On December 2017, the Basel Committee published the “Basel III: Finalising post-crisis reforms” (also known as Basel IV) that introduces the Standardised Measurement Approach (SMA) to define the Pillar I operational risk capital requirement that is foreseen to entry into force on the 1st of January 2025, replacing all the existing approaches. This approach not only introduces a new method to be used to calculate the operational risk capital requirement but details several updates that have to be applied to the main components of the framework such as Governance, Loss Data Collection and Risk Self-Assessment. With the entry into force of the SMA, banks have the chance to fully re-think their operational risk Management Framework (ORMF) integrating the different components and making it more efficient and effective in terms of data governance, process management and reporting. This paper describes the SMA methodology to be implemented to calculate the Pillar I operational risk capital requirement and provides an overview of the expected impact on the different components of the ORMF of the bank

Selective Hydrogenations and Dechlorinations in Water Mediated by Anionic Surfactant Stabilized Pd Nanoparticles

We report a facile, inexpensive and green method for the preparation of Pd nanoparticles in aqueous medium stabilized by anionic sulfonated surfactants sodium 1-dodecanesulfonate 1a, sodium dodecylbenzenesulfonate 1b, dioctyl sulfosuccinate sodium salt 1c and poly(ethylene glycol) 4-nonylphenyl-3-sulfopropyl ether potassium salt 1d simply obtained by stirring aqueous solutions of Pd(OAc)2 with the commercial anionic surfactants further treated under hydrogen atmosphere for variable times. The aqueous Pd nanoparticles solutions were tested in the selective hydrogenation reactions of aryl-alcohols, -aldehydes and -ketones leading to complete conversion to the deoxygenated products even in the absence of strong Brønsted acids in the reduction of aromatic aldehydes and ketones, in the controlled semi-hydrogenation of alkynes leading to alkenes and in the efficient hydro-dechlorination of aromatic substrates. In all cases the micellar media were crucial to stabilize the metal nanoparticles, to dissolve substrates, to steer product selectivity and to enable recycling. What is interesting is also that a benchmark catalyst like Pd/C can be often surpassed in activity and/or selectivity in the reactions tested by simply switching to the appropriate commercially available surfactant, thereby providing an easy to use, flexible and practical catalytic system capable of efficiently addressing a variety of synthetically significant hydrogenation reactions