The Role of Data in Model Building and Prediction: A Survey Through Examples

The goal of Science is to understand phenomena and systems in order to predict their development and gain control over them. In the scientific process of knowledge elaboration, a crucial role is played by models which, in the language of quantitative sciences, mean abstract mathematical or algorithmical representations. This short review discusses a few key examples from Physics, taken from dynamical systems theory, biophysics, and statistical mechanics, representing three paradigmatic procedures to build models and predictions from available data. In the case of dynamical systems we show how predictions can be obtained in a virtually model-free framework using the methods of analogues, and we briefly discuss other approaches based on machine learning methods. In cases where the complexity of systems is challenging, like in biophysics, we stress the necessity to include part of the empirical knowledge in the models to gain the minimal amount of realism. Finally, we consider many body systems where many (temporal or spatial) scales are at play and show how to derive from data a dimensional reduction in terms of a Langevin dynamics for their slow components

The role of data in model building and prediction: a survey through examples

The goal of Science is to understand phenomena and systems in order to predict their development and gain control over them. In the scientific process of knowledge elaboration, a crucial role is played by models which, in the language of quantitative sciences, mean abstract mathematical or algorithmical representations. This short review discusses a few key examples from Physics, taken from dynamical systems theory, biophysics, and statistical mechanics, representing three paradigmatic procedures to build models and predictions from available data. In the case of dynamical systems we show how predictions can be obtained in a virtually model-free framework using the methods of analogues, and we briefly discuss other approaches based on machine learning methods. In cases where the complexity of systems is challenging, like in biophysics, we stress the necessity to include part of the empirical knowledge in the models to gain the minimal amount of realism. Finally, we consider many body systems where many (temporal or spatial) scales are at play-and show how to derive from data a dimensional reduction in terms of a Langevin dynamics for their slow components

Inelastic hard-rods in a periodic potential

A simple model of inelastic hard-rods subject to a one-dimensional array of identical wells is introduced. The energy loss due to inelastic collisions is balanced by the work supplied by an external stochastic heat-bath. We explore the effect of the spatial non uniformity on the steady states of the system. The spatial variations of the density, granular temperature and pressure induced by the gradient of the external potential are investigated and compared with the analogous variations in an elastic system. Finally, we study the clustering process by considering the relaxation of the system starting from a uniform homogeneous state.Comment: RevTex4, 10 pages, 14 eps-figures, new versio

Diffusion properties of self-propelled particles in cellular flows

We study the dynamics of a self-propelled particle advected by a steady laminar flow. The persistent motion of the self-propelled particle is described by an active Ornstein-Uhlenbeck process. We focus on the diffusivity properties of the particle as a function of persistence time and free-diffusion coefficient, revealing non-monotonic behaviors, with the occurrence of a minimum and steep growth in the regime of large persistence time. In the latter limit, we obtain an analytical prediction for the scaling of the diffusion coefficient with the parameters of the active force. Our study sheds light on the effect of an inhomogeneous environment on the diffusion of active particles, such as living microorganisms and motile phytoplankton in fluids

Noise activated granular dynamics

We study the behavior of two particles moving in a bistable potential, colliding inelastically with each other and driven by a stochastic heat bath. The system has the tendency to clusterize, placing the particles in the same well at low drivings, and to fill all of the available space at high temperatures. We show that the hopping over the potential barrier occurs following the Arrhenius rate, where the heat bath temperature is replaced by the granular temperature. Moreover, within the clusterized ``phase'' one encounters two different scenarios: for moderate inelasticity, the jumps from one well to the other involve one particle at a time, whereas for strong inelasticity the two particles hop simultaneously.Comment: RevTex4, 4 pages, 4 eps figures, Minor revisio

BRAF and MEK Inhibitors and Their Toxicities: A Meta-Analysis

Purpose: This meta-analysis summarizes the incidence of treatment-related adverse events (AE) of BRAFi and MEKi. Methods: A systematic search of Medline/PubMed was conducted to identify suitable articles published in English up to 31 December 2021. The primary outcomes were profiles for all-grade and grade 3 or higher treatment-related AEs, and the analysis of single side effects belonging to both categories. Results: The overall incidence of treatment-related all-grade Aes was 99% for Encorafenib (95% CI: 0.97-1.00) and 97% for Trametinib (95% CI: 0.92-0.99; I2 = 66%) and Binimetinib (95% CI: 0.94-0.99; I2 = 0%). In combined therapies, the rate was 98% for both Vemurafenib + Cobimetinib (95% CI: 0.96-0.99; I2 = 77%) and Encorafenib + Binimetinib (95% CI: 0.96-1.00). Grade 3 or higher adverse events were reported in 69% of cases for Binimetinib (95% CI: 0.50-0.84; I2 = 71%), 68% for Encorafenib (95% CI: 0.61-0.74), and 72% for Vemurafenib + Cobimetinib (95% CI: 0.65-0.79; I2 = 84%). The most common grade 1-2 AEs were pyrexia (43%) and fatigue (28%) for Dabrafenib + Trametinib and diarrhea for both Vemurafenib + Cobimetinib (52%) and Encorafenib + Binimetinib (34%). The most common AEs of grade 3 or higher were pyrexia, rash, and hypertension for Dabrafenib + Trametinib (6%), rash and hypertension for Encorafenib + Binimetinib (6%), and increased AST and ALT for Vemurafenib + Cobimetinib (10%). Conclusions: Our study provides comprehensive data on treatment-related adverse events of BRAFi and MEKi combination therapies, showing related toxicity profiles to offer a helpful tool for clinicians in the choice of therapy

Treatment of metastatic breast cancer in a real-world scenario: Is progression-free survival with first line predictive of benefit from second and later lines?

INTRODUCTION: Despite the availability of several therapeutic options for metastatic breast cancer (MBC), no robust predictive factors are available to help clinical decision making. Nevertheless, a decreasing benefit from first line to subsequent lines of treatment is commonly observed. The aim of this study was to assess the impact of benefit from first-line therapy on outcome with subsequent lines. METHODS: We analyzed a consecutive series of 472 MBC patients treated with chemotherapy (CT) and/or endocrine therapy (ET) between 2004 and 2012. We evaluated progression-free survival (PFS) at first (PFS1), second, third, and fourth therapeutic lines, according to treatment (ET and/or CT) and tumor subtypes. RESULTS: In the whole cohort, median overall survival was 34 months, and median PFS1 was 9 months. A 6-month benefit was shown by 289 patients (63.5%) at first line, 128 (40.5%) at second line, 76 (33.8%) at third line, and 34 (23.3%) at fourth line. Not having a 6-month benefit at PFS1 was associated with less chance of benefit at second line (odds ratio [OR]: 0.48; 95% confidence interval [CI]: 0.29-0.77, p = .0026) and at any line beyond first (OR: 0.39; 95% CI: 0.24-0.62, p < .0001). In the total series, after stratification for tumor subtypes, a strong predictive effect was observed among HER2-positive tumors (OR: 0.2; 95% CI: 0.05-0.73, p = .0152). CONCLUSION: Our results suggest that the absence of at least a 6-month benefit in terms of PFS with first-line therapy predicts a reduced probability of benefit from subsequent therapeutic lines, especially in HER2-positive disease. IMPLICATIONS FOR PRACTICE: This study supports evidence showing that the absence of a 6-month benefit in terms of progression-free survival with first-line therapy predicts a lack of benefit from subsequent therapeutic lines in metastatic breast cancer. The random distribution of benefit experienced by a subset of the cohort further spurs an interest in identifying predictive factors capable of identifying the most appropriate therapeutic strategy

News from the San Antonio Breast Cancer Symposium 2022

The 45th San Antonio Breast Cancer Symposium, held December 6–10 in San Antonio, Texas is the largest breast cancer conference and this year saw the participation of nearly 10,000 clinicians, researchers, and patient advocates, in person. Scientists shared many important new findings that are going to change the clinical practice in the near future. Here, we will present the most important news with a group of Italian colleagues and we will discuss how these results will impact the management of breast cancer