Quantum liquids resulting from quark systems with four-quark interaction

Quark ensembles influenced by strong stochastic vacuum gluon fields are investigated within the four-fermion interaction approximation. The comparative analysis of several quantum liquid models is performed and this analysis leads to the conclusion that the presence of a gas–liquid phase transition is their characteristic feature. The problem of the instability of small quark number droplets is discussed and it is argued that it is rooted in the chiral soliton formation. The existence of a mixed phase of the vacuum and baryon matter is proposed as a possible explanation of the latter stability

DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis

Motivation Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. Results We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space. Availability and implementation The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php

Application of machine learning techniques to tuberculosis drug resistance analysis

Motivation: Timely identification of Mycobacterium tuberculosis (MTB) resistance to existing drugs is vital to decrease mortality and prevent the amplification of existing antibiotic resistance. Machine learning methods have been widely applied for timely predicting resistance of MTB given a specific drug and identifying resistance markers. However, they have been not validated on a large cohort of MTB samples from multi-centers across the world in terms of resistance prediction and resistance marker identification. Several machine learning classifiers and linear dimension reduction techniques were developed and compared for a cohort of 13 402 isolates collected from 16 countries across 6 continents and tested 11 drugs. Results: Compared to conventional molecular diagnostic test, area under curve of the best machine learning classifier increased for all drugs especially by 23.11%, 15.22% and 10.14% for pyrazinamide, ciprofloxacin and ofloxacin, respectively (P < 0.01). Logistic regression and gradient tree boosting found to perform better than other techniques. Moreover, logistic regression/gradient tree boosting with a sparse principal component analysis/non-negative matrix factorization step compared with the classifier alone enhanced the best performance in terms of F1-score by 12.54%, 4.61%, 7.45% and 9.58% for amikacin, moxifloxacin, ofloxacin and capreomycin, respectively, as well increasing area under curve for amikacin and capreomycin. Results provided a comprehensive comparison of various techniques and confirmed the application of machine learning for better prediction of the large diverse tuberculosis data. Furthermore, mutation ranking showed the possibility of finding new resistance/susceptible markers

ALICE: Physics Performance Report, Volume II

ALICE is a general-purpose heavy-ion experiment designed to study the physics of strongly interacting matter and the quark-gluon plasma in nucleus-nucleus collisions at the LHC. It currently involves more than 900 physicists and senior engineers, from both the nuclear and high-energy physics sectors, from over 90 institutions in about 30 countries. The ALICE detector is designed to cope with the highest particle multiplicities above those anticipated for Pb-Pb collisions (dN(ch)/dy up to 8000) and it will be operational at the start-up of the LHC. In addition to heavy systems, the ALICE Collaboration will study collisions of lower-mass ions, which are a means of varying the energy density, and protons (both pp and pA), which primarily provide reference data for the nucleus-nucleus collisions. In addition, the pp data will allow for a number of genuine pp physics studies. The detailed design of the different detector systems has been laid down in a number of Technical Design Reports issued between mid-1998 and the end of 2004. The experiment is currently under construction and will be ready for data taking with both proton and heavy-ion beams at the start-up of the LHC. Since the comprehensive information on detector and physics performance was last published in the ALICE Technical Proposal in 1996, the detector, as well as simulation, reconstruction and analysis software have undergone significant development. The Physics Performance Report (PPR) provides an updated and comprehensive summary of the performance of the various ALICE subsystems, including updates to the Technical Design Reports, as appropriate. The PPR is divided into two volumes. Volume I, published in 2004 (CERN/LHCC 2003-049, ALICE Collaboration 2004 J. Phys. G: Nucl. Part. Phys. 30 1517-1763), contains in four chapters a short theoretical overview and an extensive reference list concerning the physics topics of interest to ALICE, the experimental conditions at the LHC, a short summary and update of the subsystem designs, and a description of the offline framework and Monte Carlo event generators. The present volume, Volume II, contains the majority of the information relevant to the physics performance in proton-proton, proton-nucleus, and nucleus-nucleus collisions. Following an introductory overview, Chapter 5 describes the combined detector performance and the event reconstruction procedures, based on detailed simulations of the individual subsystems. Chapter 6 describes the analysis and physics reach for a representative sample of physics observables, from global event characteristics to hard processes