Learning Physics from Data: a Thermodynamic Interpretation

Experimental data bases are typically very large and high dimensional. To learn from them requires to recognize important features (a pattern), often present at scales different to that of the recorded data. Following the experience collected in statistical mechanics and thermodynamics, the process of recognizing the pattern (the learning process) can be seen as a dissipative time evolution driven by entropy from a detailed level of description to less detailed. This is the way thermodynamics enters machine learning. On the other hand, reversible (typically Hamiltonian) evolution is propagation within the levels of description, that is also to be recognized. This is how Poisson geometry enters machine learning. Learning to handle free surface liquids and damped rigid body rotation serves as an illustration.Comment: Submitted to the proceedings of the Les Houches Summer Schoo

Direct Poisson neural networks: learning non-symplectic mechanical systems

ABSTRACT: In this paper, we present neural networks learning mechanical systems that are both symplectic (for instance particle mechanics) and non-symplectic (for instance rotating rigid body). Mechanical systems have Hamiltonian evolution, which consists of two building blocks: a Poisson bracket and an energy functional. We feed a set of snapshots of a Hamiltonian system to our neural network models which then find both the two building blocks. In particular, the models distinguish between symplectic systems (with non-degenerate Poisson brackets) and non-symplectic systems (degenerate brackets). In contrast with earlier works, our approach does not assume any further a priori information about the dynamics except its Hamiltonianity, and it returns Poisson brackets that satisfy Jacobi identity. Finally, the models indicate whether a system of equations is Hamiltonian or not

TetR-Based Gene Regulation Systems for Francisella tularensis

ABSTRACT There are a number of genetic tools available for studying Francisella tularensis , the etiological agent of tularemia; however, there is no effective inducible or repressible gene expression system. Here, we describe inducible and repressible gene expression systems for F. tularensis based on the Tet repressor, TetR. For the inducible system, a tet operator sequence was cloned into a modified F. tularensis groESL promoter sequence and carried in a plasmid that constitutively expressed TetR. To monitor regulation the luminescence operon, luxCDABE , was cloned under the hybrid Francisella tetracycline-regulated promoter ( FTRp ), and transcription was initiated with addition of anhydrotetracycline (ATc), which binds TetR and alleviates TetR association with tetO. Expression levels measured by luminescence correlated with ATc inducer concentrations ranging from 20 to 250 ng ml −1 . In the absence of ATc, luminescence was below the level of detection. The inducible system was also functional during the infection of J774A.1 macrophages, as determined by both luminescence and rescue of a mutant strain with an intracellular growth defect. The repressible system consists of FTRp regulated by a reverse TetR mutant (revTetR), TetR r1.7. Using this system with the lux reporter, the addition of ATc resulted in decreased luminescence, while in the absence of ATc the level of luminescence was not significantly different from that of a construct lacking TetR r1.7. Utilizing both systems, the essentiality of SecA, the protein translocase ATPase, was confirmed, establishing that they can effectively regulate gene expression. These two systems will be invaluable in exploring F. tularensis protein function

Prognostic factors for abatacept retention in patients who received at least one prior biologic agent: an interim analysis from the observational, prospective ACTION study

Background: The emergence of new therapies for the treatment of rheumatoid arthritis (RA), the paucity of head-to-head studies, and the heterogeneous nature of responses to current biologics highlight the need for the identification of prognostic factors for treatment response and retention in clinical practice. Prognostic factors for patient retention have not been explored thoroughly despite data for abatacept and other biologics being available from national registries. Real-world data from the ACTION study may supplement the findings of randomized controlled trials and show how abatacept is used in clinical practice. The aim of this interim analysis was to identify prognostic factors for abatacept retention in patients with RA who received at least one prior biologic agent. Methods: A large, international, non-interventional cohort of patients with moderate-to-severe RA who initiated intravenous abatacept in Canada and Europe (May 2008–January 2011) enrolled in the ACTION study. Potential prognostic factors for retention in this interim analysis (data cut-off February 2012; including patients from Canada, Germany, Greece, and Italy) were baseline demographics and disease characteristics, medical history, and previous and concomitant medication. Clinically relevant variables with p ≤ 0.20 in univariate analysis and no collinearity were entered into a Cox proportional hazards regression model, adjusted for clustered data. Variables with p ≤ 0.10 were retained in the final model (backward selection). Results: The multivariate model included 834 patients. Anti-cyclic citrullinated peptide (CCP) antibody positivity (hazard ratio [95 % confidence interval]: 0.55 [0.40, 0.75], p < 0.001), failure of <2 prior anti-tumor necrosis factors (TNFs) (0.71 [0.56, 0.90], p = 0.005 versus ≥2 prior anti-TNFs), and cardiovascular comorbidity at abatacept initiation (0.48 [0.28, 0.83], p = 0.009) were associated with lower risk of abatacept discontinuation. Patients in Greece and Italy were less likely to discontinue abatacept than patients in Germany and Canada (Greece: 0.30 [0.16, 0.58]; Italy: 0.50 [0.33, 0.76]; Canada: 1.04 [0.78, 1.40], p < 0.001 versus Germany). Conclusions: Real-world prognostic factors for abatacept retention include anti-CCP positivity and fewer prior anti-TNF failures. Differences in retention rates between countries may reflect differences in healthcare systems. The finding that abatacept has potential advantages in patients with cardiovascular comorbidities needs to be confirmed in further research

an interim analysis from the observational, prospective ACTION study

Background The emergence of new therapies for the treatment of rheumatoid arthritis (RA), the paucity of head-to-head studies, and the heterogeneous nature of responses to current biologics highlight the need for the identification of prognostic factors for treatment response and retention in clinical practice. Prognostic factors for patient retention have not been explored thoroughly despite data for abatacept and other biologics being available from national registries. Real-world data from the ACTION study may supplement the findings of randomized controlled trials and show how abatacept is used in clinical practice. The aim of this interim analysis was to identify prognostic factors for abatacept retention in patients with RA who received at least one prior biologic agent. Methods A large, international, non- interventional cohort of patients with moderate-to-severe RA who initiated intravenous abatacept in Canada and Europe (May 2008–January 2011) enrolled in the ACTION study. Potential prognostic factors for retention in this interim analysis (data cut-off February 2012; including patients from Canada, Germany, Greece, and Italy) were baseline demographics and disease characteristics, medical history, and previous and concomitant medication. Clinically relevant variables with p ≤ 0.20 in univariate analysis and no collinearity were entered into a Cox proportional hazards regression model, adjusted for clustered data. Variables with p ≤ 0.10 were retained in the final model (backward selection). Results The multivariate model included 834 patients. Anti-cyclic citrullinated peptide (CCP) antibody positivity (hazard ratio [95 % confidence interval]: 0.55 [0.40, 0.75], p < 0.001), failure of <2 prior anti-tumor necrosis factors (TNFs) (0.71 [0.56, 0.90], p = 0.005 versus ≥2 prior anti-TNFs), and cardiovascular comorbidity at abatacept initiation (0.48 [0.28, 0.83], p = 0.009) were associated with lower risk of abatacept discontinuation. Patients in Greece and Italy were less likely to discontinue abatacept than patients in Germany and Canada (Greece: 0.30 [0.16, 0.58]; Italy: 0.50 [0.33, 0.76]; Canada: 1.04 [0.78, 1.40], p < 0.001 versus Germany). Conclusions Real-world prognostic factors for abatacept retention include anti-CCP positivity and fewer prior anti-TNF failures. Differences in retention rates between countries may reflect differences in healthcare systems. The finding that abatacept has potential advantages in patients with cardiovascular comorbidities needs to be confirmed in further research

CAVER Analyst 1.0: graphic tool for interactive visualization and analysis of tunnels and channels in protein structures

ABSTRACT Summary: The transport of ligands, ions or solvent molecules into proteins with buried binding sites or through the membrane is enabled by protein tunnels and channels. CAVER Analyst is a software tool for calculation, analysis and real-time visualization of access tunnels and channels in static and dynamic protein structures. It provides an intuitive graphic user interface for setting up the calculation and interactive exploration of identified tunnels/channels and their characteristics. Availability and Implementation: CAVER Analyst is a multi-platform software written in JAVA. Binaries and documentation are freely available for non-commercial use at http://www.caver.cz