MMsPred: a bioactivity and toxicology predictive system

In the last decade, the development and use of new methods in combinatorial chemistry and high-throughput screening has dramatically increased the number of known biologically active compounds. Paradoxically, the number of drugs reaching the market has not followed the same trend, often because many of the candidate drugs present poor qualities in absorption, distribution, metabolism, excretion, and toxicological properties (ADME-Tox). The ability to recognize and discard bad candidates early in the drug discovery steps would save lost investments in time and money. Machine learning techniques could provide solutions to this problem.
The goal of my research is to develop classifiers that accurately discriminate between active and inactive molecules for a specific target. To this end, I am comparing the effectiveness of the application of different machine learning techniques to this problem.	As a source of data we have selected a set of PubChem's public BioAssays1. In addition, with the objective of realizing a real-time query service with our predictors, we aim to keep the features describing the chemical compounds relatively simple.
At the end of this process, we should better understand how to build statistical models that are able to recognize molecules active in a specific bioassay, including how to select the most appropriate classification technique, and how to describe compounds in such a way that is not excessively resource-consuming to generate, yet contains sufficient information for the classification. We see immediate applications of such technology to recognize compounds with high-risk of toxicity, and also to suggest likely metabolic pathways that would process it

How does a dark compact object ringdown?

A generic feature of nearly out-of-equilibrium dissipative systems is that they resonate through a set of quasinormal modes. Black holes - the absorbing objects par excellence - are no exception. When formed in a merger, black holes vibrate in a process called "ringdown", which leaves the gravitational-wave footprint of the event horizon. In some models of quantum gravity which attempt to solve the information-loss paradox and the singularities of General Relativity, black holes are replaced by regular, horizonless objects with a tiny effective reflectivity. Motivated by these scenarios, here we develop a generic framework to the study of the ringdown of a compact object with various shades of darkness. By extending the black-hole membrane paradigm, we map the interior of any compact object in terms of the bulk and shear viscosities of a fictitious fluid located at the surface, with the black-hole limit being a single point in a three-dimensional parameter space. We unveil some remarkable features of the ringdown and some universal properties of the light ring in this framework. We also identify the region of the parameter space which can be probed by current and future gravitational-wave detectors. A general feature is the appearance of mode doublets which are degenerate only in the black-hole limit. We argue that the merger event GW150914 already imposes a strong lower bound on the compactness of the merger remnant of approximately 99% of the black-hole compactness. This places model-independent constraints on black-hole alternatives such as diffuse "fuzzballs" and nonlocal stars.Comment: 11+7 pages, 8 figures. v2: minor revisions to match the version to appear in PR

How Time Window Influences Biometrics Performance: An EEG-Based Fingerprint Connectivity Study

EEG-based biometrics represent a relatively recent research field that aims to recognize individuals based on their recorded brain activity using electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, using the phase lag index (PLI) and the phase locking value (PLV) methods, we investigate how the performance of a connectivity-based EEG biometric system varies with respect to different time windows (using epochs of different lengths ranging from 0.5 s to 12 s with a step of 0.5 s) to understand if it is possible to define the optimal duration of the EEG signal required to extract those distinctive features. All the analyses were performed on two freely available EEG datasets, including 109 and 23 subjects, respectively. Overall, as expected, the results have shown a pronounced effect of the time window length on the biometric performance measured in terms of EER (equal error rate) and AUC (area under the curve), with an evident increase in the biometric performance as the time window increases. Furthermore, our initial findings strongly suggest that enlarging the window size beyond a specific maximum threshold fails to enhance the performance of biometric systems. In conclusions, we want to highlight that EEG connectivity has the potential to represent an optimal candidate as an EEG fingerprint and that, in this context, it is essential to establish an adequate time window capable of capturing subject-specific features. Furthermore, we speculate that the poor performance obtained with short time windows mainly depends on the difficulty of correctly estimating the connectivity metrics from very small EEG epochs (shorter than 8 s)

How time window influences biometrics performance: an EEG-based fingerprints connectivity study

EEG-based biometric represents a relatively recent research field that aims to recognize individuals based on their recorded brain activity by means of electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, we investigate how the performance of an EEG biometric system varies with respect to different time windows to understand if it is possible to define the optimal duration of EEG signal that can be used to extract those distinctive features. Overall, the results have shown a pronounced effect of the time window on the biometric performance measured in terms of EER (equal error rate) and AUC (area under the curve), with an evident increase of the biometric performance with an increase of the time window. In conclusion, we want to highlight that EEG connectivity has the potential to represent an optimal candidate as EEG fingerprint and that, in this context, it is very important to define a sufficient time window able to collect the subject specific features. Moreover, our preliminary results show that extending the window size beyond a certain maximum does not improve biometric systems' performance

The Evolution of Primordial Black Holes and their Final Observable Spins

Primordial black holes in the mass range of ground-based gravitational-wave detectors can comprise a significant fraction of the dark matter. Mass and spin measurements from coalescences can be used to distinguish between an astrophysical or a primordial origin of the binary black holes. In standard scenarios the spin of primordial black holes is very small at formation. However, the mass and spin can evolve through the cosmic history due to accretion. We show that the mass and spin of primordial black holes are correlated in a redshift-dependent fashion, in particular primordial black holes with masses below ${\cal O}(30)M_\odot$ are likely non-spinning at any redshift, whereas heavier black holes can be nearly extremal up to redshift $z\sim10$. The dependence of the mass and spin distributions on the redshift can be probed with future detectors such as the Einstein Telescope. The mass and spin evolution affect the gravitational waveform parameters, in particular the distribution of the final mass and spin of the merger remnant, and that of the effective spin of the binary. We argue that, compared to the astrophysical-formation scenario, a primordial origin of black hole binaries might better explain the spin distribution of merger events detected by LIGO-Virgo, in which the effective spin parameter of the binary is compatible to zero except possibly for few high-mass events. Upcoming results from LIGO-Virgo third observation run might reinforce or weaken these predictions.Comment: 13 figures, 31 pages. v2: Section and appendix added. Results unchange

Tidal Love numbers and approximate universal relations for fermion soliton stars

Fermion soliton stars are a consistent model of exotic compact objects which involve a nonlinear interaction between a real scalar field and fermions through a Yukawa term. This interaction results in an effective fermion mass that depends upon the vacuum structure in the scalar potential. In this work we investigate the tidal deformations of fermion soliton stars and compute the corresponding tidal Love numbers for different model parameters. Furthermore, we discuss the existence of approximate universal relations for the electric and magnetic tidal deformabilities of these stars, and compare them with other solutions of general relativity, such as neutron stars or boson stars. These relations for fermion soliton stars are less universal than for neutron stars, but they are sufficiently different from the ordinary neutron star case that a measurement of the electric and magnetic tidal Love numbers (as potentially achievable by next-generation gravitational wave detectors) can be used to disentangle these families of compact objects. Finally, we discuss the conditions for tidal disruption of fermion soliton stars in a binary system and estimate the detectability of the electromagnetic signal associated with such tidal disruption events.Comment: 15 pages, 4 figures. v2: new figure added, matches version accepted in PR

Predictors of response to erenumab after 12 months of treatment

Objective: Erenumab is a monoclonal antibody acting against calcitonin gene-related peptide receptor and approved for the preventive treatment of chronic migraine. The aim of the present study is to identify clinical predictors of good response in patients with chronic migraine and medication overuse-headache. Material and methods: This was a retrospective single-center not funded study. Enrolled patients were affected by chronic migraine and medication overuse-headache treated with erenumab monthly, up to 1 year. At 1 year, patients were classified as good responders if they displayed a ≥50% reduction in the number of headache days per months compared to the baseline. Results: After 1 year, a significant improvement in the number of headache days per months, analgesic consumption, 6-items headache impact test, and migraine disability assessment questionnaire scores were obtained compared to the baseline. Patients who obtained a ≥50% reduction in the number of headache days per month compared to the baseline displayed a longer history of medication overuse-headache, a higher number of painkillers taken per month at the baseline and a higher number of failed preventive treatments in the past. Conclusions: Patients with longer medication overuse-headache duration, higher analgesic intake, and a higher number of previous preventive treatment failures may receive less benefit with erenumab