984 research outputs found
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
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