Azimuthal anisotropy of charged jet production in root s(NN)=2.76 TeV Pb-Pb collisions

We present measurements of the azimuthal dependence of charged jet production in central and semi-central root s(NN) = 2.76 TeV Pb-Pb collisions with respect to the second harmonic event plane, quantified as nu(ch)(2) (jet). Jet finding is performed employing the anti-k(T) algorithm with a resolution parameter R = 0.2 using charged tracks from the ALICE tracking system. The contribution of the azimuthal anisotropy of the underlying event is taken into account event-by-event. The remaining (statistical) region-to-region fluctuations are removed on an ensemble basis by unfolding the jet spectra for different event plane orientations independently. Significant non-zero nu(ch)(2) (jet) is observed in semi-central collisions (30-50% centrality) for 20 <p(T)(ch) (jet) <90 GeV/c. The azimuthal dependence of the charged jet production is similar to the dependence observed for jets comprising both charged and neutral fragments, and compatible with measurements of the nu(2) of single charged particles at high p(T). Good agreement between the data and predictions from JEWEL, an event generator simulating parton shower evolution in the presence of a dense QCD medium, is found in semi-central collisions. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Forward-central two-particle correlations in p-Pb collisions at root s(NN)=5.02 TeV

Two-particle angular correlations between trigger particles in the forward pseudorapidity range (2.5 2GeV/c. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B. V.Peer reviewe

Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at root(NN)-N-S=2.76 TeV

Peer reviewe

Pseudorapidity and transverse-momentum distributions of charged particles in proton-proton collisions at root s=13 TeV

The pseudorapidity (eta) and transverse-momentum (p(T)) distributions of charged particles produced in proton-proton collisions are measured at the centre-of-mass energy root s = 13 TeV. The pseudorapidity distribution in vertical bar eta vertical bar <1.8 is reported for inelastic events and for events with at least one charged particle in vertical bar eta vertical bar <1. The pseudorapidity density of charged particles produced in the pseudorapidity region vertical bar eta vertical bar <0.5 is 5.31 +/- 0.18 and 6.46 +/- 0.19 for the two event classes, respectively. The transverse-momentum distribution of charged particles is measured in the range 0.15 <p(T) <20 GeV/c and vertical bar eta vertical bar <0.8 for events with at least one charged particle in vertical bar eta vertical bar <1. The evolution of the transverse momentum spectra of charged particles is also investigated as a function of event multiplicity. The results are compared with calculations from PYTHIA and EPOS Monte Carlo generators. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Elliptic flow of muons from heavy-flavour hadron decays at forward rapidity in Pb-Pb collisions at root s(NN)=2.76TeV

The elliptic flow, v(2), of muons from heavy-flavour hadron decays at forward rapidity (2.5 <y <4) is measured in Pb-Pb collisions at root s(NN)= 2.76TeVwith the ALICE detector at the LHC. The scalar product, two- and four-particle Q cumulants and Lee-Yang zeros methods are used. The dependence of the v(2) of muons from heavy-flavour hadron decays on the collision centrality, in the range 0-40%, and on transverse momentum, p(T), is studied in the interval 3 <p(T)<10 GeV/c. A positive v(2) is observed with the scalar product and two-particle Q cumulants in semi-central collisions (10-20% and 20-40% centrality classes) for the p(T) interval from 3 to about 5GeV/c with a significance larger than 3 sigma, based on the combination of statistical and systematic uncertainties. The v(2) magnitude tends to decrease towards more central collisions and with increasing pT. It becomes compatible with zero in the interval 6 <p(T)<10 GeV/c. The results are compared to models describing the interaction of heavy quarks and open heavy-flavour hadrons with the high-density medium formed in high-energy heavy-ion collisions. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V.Peer reviewe

Conditional generative modeling for de novo protein design with hierarchical functions

Motivation Protein design has become increasingly important for medical and biotechnological applications. Because of the complex mechanisms underlying protein formation, the creation of a novel protein requires tedious and time-consuming computational or experimental protocols. At the same time, machine learning has enabled the solving of complex problems by leveraging large amounts of available data, more recently with great improvements on the domain of generative modeling. Yet, generative models have mainly been applied to specific sub-problems of protein design. Results Here, we approach the problem of general-purpose protein design conditioned on functional labels of the hierarchical Gene Ontology. Since a canonical way to evaluate generative models in this domain is missing, we devise an evaluation scheme of several biologically and statistically inspired metrics. We then develop the conditional generative adversarial network ProteoGAN and show that it outperforms several classic and more recent deep-learning baselines for protein sequence generation. We further give insights into the model by analyzing hyperparameters and ablation baselines. Lastly, we hypothesize that a functionally conditional model could generate proteins with novel functions by combining labels and provide first steps into this direction of research.ISSN:1367-4803ISSN:1460-205

Electrophysiological correlates of perceptual auditory priming without explicit recognition memory

The aim of this study was to identify an event-related potential (ERP correlate) of perceptual auditory priming using a method that can dissociate it from explicit memory similar to Rugg et al. (1998). EEG was recorded during performance of an auditory word recognition test, where 17 participants discriminated "old" from "new" aural words, encoded using either a "deep" or "shallow" levels-of-processing (LOP) study task. A right-lateralized P200 effect was modulated by words' old/new status but not by accuracy of recognition or LOP manipulation. Because this effect was driven by simple repetition rather than factors known to influence episodic recognition memory, a "bottom-up" perceptual priming function was inferred which was substantiated by its early temporal appearance. A similar ERP amplitude modulation was evident across a broader topographical region during the subsequent N400 time interval. Conversely the late posterior component (LPC; 500-800 ms) for deeply-encoded, correctly-recognized words was of higher amplitude than LPCs for shallowly-encoded and new words, consistent with proposals that this ERP component indexes episodic memory. To our knowledge this is the first report of an ERP correlate of auditory perceptual priming dissociated from explicit episodic memory

Perillyl alcohol-mediated inhibition of lung cancer cell line proliferation: potential mechanisms for its chemotherapeutic effects

Perillyl alcohol (POH) is currently being tested in clinical trials as an anticancer agent, though its mechanism of action has not been definitively established. We treated two human lung cancer cell lines, H322 and H838, with POH to determine its antitumor properties. A sulforhodamine B (SRB) cell proliferation assay was used to determine the effects of POH after 1 and 5 days of treatment with 0.25, 0.5, 0.75, 1.0, and 1.5 mM POH. After 1 day of treatment, little difference could be seen between the lowest and highest concentrations of POH. However, after 5 days, both cell lines showed a dose-dependent decrease in cell proliferation that ranged from 15% to 83%. A clonogenic assay confirmed these results—while there was no significant effect of POH after 1 day of exposure, a dose-dependent decrease in colony formation, ranging from 15% to 100%, was seen after 5 days of treatment. Time-lapse video microscopy revealed that apoptotic cells were evident within 24–48 h of treatment with 1.5 mM POH. The appearance of apoptotic cells was preceded by increased caspase-3 activity and cleavage of poly (ADP-ribose) polymerase (PARP) as POH activated caspase-3 activity 3–6-fold. Nuclear staining with 4′,6-diamidino-2-phenylindole (DAPI) confirmed the classical characteristics of apoptosis in POH-treated cells. DNA microarray expression analysis was performed following 8 and 24 (H322) or 8 and 48 (H838) h of treatment with 1.5 mM POH. While a large number of genes were up- or downregulated in the two cell lines at various times after POH treatment, the levels of expression of only eight genes were up- or down-related in both cell lines at both of the time points examined. The significance of these genes as potential mediators of POH action is still uncertain, but the limited number of commonly up- or downregulated genes detected by microarray expression analysis suggests that POH may mediate its effects via posttranscriptional mechanisms. Our results suggest that POH may have potential use as an anticancer drug that stimulates or sensitizes lung tumor cells to apoptosis, and this effect may depend on genetic lesions present in tumor cells

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics

Motivation Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plant breeding programs. While methods to predict yield from genotype or phenotype data have been proposed, improved performance and integrated models are needed. Results We propose a machine learning model that leverages both genotype and phenotype measurements by fusing genetic variants with multiple data sources collected by unmanned aerial systems. We use a deep multiple instance learning framework with an attention mechanism that sheds light on the importance given to each input during prediction, enhancing interpretability. Our model reaches 0.754 ± 0.024 Pearson correlation coefficient when predicting yield in similar environmental conditions; a 34.8% improvement over the genotype-only linear baseline (0.559 ± 0.050). We further predict yield on new lines in an unseen environment using only genotypes, obtaining a prediction accuracy of 0.386 ± 0.010, a 13.5% improvement over the linear baseline. Our multi-modal deep learning architecture efficiently accounts for plant health and environment, distilling the genetic contribution and providing excellent predictions. Yield prediction algorithms leveraging phenotypic observations during training therefore promise to improve breeding programs, ultimately speeding up delivery of improved varieties.ISSN:1367-4803ISSN:1460-205