Steps in the bacterial flagellar motor

The bacterial flagellar motor is a highly efficient rotary machine used by many bacteria to propel themselves. It has recently been shown that at low speeds its rotation proceeds in steps [Sowa et al. (2005) Nature 437, 916--919]. Here we propose a simple physical model that accounts for this stepping behavior as a random walk in a tilted corrugated potential that combines torque and contact forces. We argue that the absolute angular position of the rotor is crucial for understanding step properties, and show this hypothesis to be consistent with the available data, in particular the observation that backward steps are smaller on average than forward steps. Our model also predicts a sublinear torque-speed relationship at low torque, and a peak in rotor diffusion as a function of torque

The Spheres & Shield Maze Task: A virtual reality serious game for the assessment of risk taking in decision making

[EN] Risk taking (RT) is an essential component in decision-making process that depicts the propensity to make risky decisions. RT assessment has traditionally focused on self-report questionnaires. These classical tools have shown clear distance from real-life responses. Behavioral tasks assess human behavior with more fidelity, but still show some limitations related to transferability. A way to overcome these constraints is to take advantage from virtual reality (VR), to recreate real-simulated situations that might arise from performance-based assessments, supporting RT research. This article presents results of a pilot study in which 41 individuals explored a gamified VR environment: the Spheres & Shield Maze Task (SSMT). By eliciting implicit behavioral measures, we found relationships between scores obtained in the SSMT and self-reported risk-related constructs, as engagement in risky behaviors and marijuana consumption. We conclude that decontextualized Virtual Reality Serious Games are appropriate to assess RT, since they could be used as a cross-disciplinary tool to assess individuals' capabilities under the stealth assessment paradigm.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness funded projects "Advanced Therapeutic Tools for Mental Health'' (DPI2016-77396-R), and "Assessment and Training on Decision Making in Risk Environments'' (RTC-2017-6523-6) (MINECO/AEI/FEDER,UE) and by the Generalitat Valenciana funded project "Rebrand'' (PROMETEU/2019/105).Juan-Ripoll, CD.; Soler-Domínguez, JL.; Chicchi-Giglioli, IA.; Contero, M.; Alcañiz Raya, ML. (2020). The Spheres & Shield Maze Task: A virtual reality serious game for the assessment of risk taking in decision making. Cyberpsychology Behavior and Social Networking. 23(11):773-781. https://doi.org/10.1089/cyber.2019.0761S7737812311Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (2005). The Iowa Gambling Task and the somatic marker hypothesis: some questions and answers. Trends in Cognitive Sciences, 9(4), 159-162. doi:10.1016/j.tics.2005.02.002Krain, A. L., Wilson, A. M., Arbuckle, R., Castellanos, F. X., & Milham, M. P. (2006). Distinct neural mechanisms of risk and ambiguity: A meta-analysis of decision-making. NeuroImage, 32(1), 477-484. doi:10.1016/j.neuroimage.2006.02.047Einhorn, H. J. (1970). The use of nonlinear, noncompensatory models in decision making. Psychological Bulletin, 73(3), 221-230. doi:10.1037/h0028695Figner, B., & Weber, E. U. (2011). Who Takes Risks When and Why? Current Directions in Psychological Science, 20(4), 211-216. doi:10.1177/0963721411415790Endsley, M. R., & Garland, D. J. (Eds.). (2000). Situation Awareness Analysis and Measurement. doi:10.1201/b12461Lauriola, M., & Levin, I. P. (2001). Personality traits and risky decision-making in a controlled experimental task: an exploratory study. Personality and Individual Differences, 31(2), 215-226. doi:10.1016/s0191-8869(00)00130-6Rundmo, T. (1996). Associations between risk perception and safety. Safety Science, 24(3), 197-209. doi:10.1016/s0925-7535(97)00038-6Zuckerman, M., & Kuhlman, D. M. (2000). Personality and Risk‐Taking: Common Bisocial Factors. Journal of Personality, 68(6), 999-1029. doi:10.1111/1467-6494.00124Dahlen, E. R., Martin, R. C., Ragan, K., & Kuhlman, M. M. (2005). Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving. Accident Analysis & Prevention, 37(2), 341-348. doi:10.1016/j.aap.2004.10.006Donohew, L., Zimmerman, R., Cupp, P. S., Novak, S., Colon, S., & Abell, R. (2000). Sensation seeking, impulsive decision-making, and risky sex: implications for risk-taking and design of interventions. Personality and Individual Differences, 28(6), 1079-1091. doi:10.1016/s0191-8869(99)00158-0Moreno, M., Estevez, A. F., Zaldivar, F., Montes, J. M. G., Gutiérrez-Ferre, V. E., Esteban, L., … Flores, P. (2012). Impulsivity differences in recreational cannabis users and binge drinkers in a university population. Drug and Alcohol Dependence, 124(3), 355-362. doi:10.1016/j.drugalcdep.2012.02.011Dvorak, R. D., & Day, A. M. (2014). Marijuana and self-regulation: Examining likelihood and intensity of use and problems. Addictive Behaviors, 39(3), 709-712. doi:10.1016/j.addbeh.2013.11.001Trocki, K. F., Drabble, L. A., & Midanik, L. T. (2009). Tobacco, marijuana, and sensation seeking: Comparisons across gay, lesbian, bisexual, and heterosexual groups. Psychology of Addictive Behaviors, 23(4), 620-631. doi:10.1037/a0017334Ames, S. L., Zogg, J. B., & Stacy, A. W. (2002). Implicit cognition, sensation seeking, marijuana use and driving behavior among drug offenders. Personality and Individual Differences, 33(7), 1055-1072. doi:10.1016/s0191-8869(01)00212-4Highhouse, S., Nye, C. D., Zhang, D. C., & Rada, T. B. (2016). Structure of the Dospert: Is There Evidence for a General Risk Factor? Journal of Behavioral Decision Making, 30(2), 400-406. doi:10.1002/bdm.1953Jackson, D. N., Hourany, L., & Vidmar, N. J. (1972). A four-dimensional interpretation of risk taking1. Journal of Personality, 40(3), 483-501. doi:10.1111/j.1467-6494.1972.tb00075.xSkeel, R. L., Neudecker, J., Pilarski, C., & Pytlak, K. (2007). The utility of personality variables and behaviorally-based measures in the prediction of risk-taking behavior. Personality and Individual Differences, 43(1), 203-214. doi:10.1016/j.paid.2006.11.025Horvath, P., & Zuckerman, M. (1993). Sensation seeking, risk appraisal, and risky behavior. Personality and Individual Differences, 14(1), 41-52. doi:10.1016/0191-8869(93)90173-zLejuez, C. W., Aklin, W. M., Zvolensky, M. J., & Pedulla, C. M. (2003). Evaluation of the Balloon Analogue Risk Task (BART) as a predictor of adolescent real-world risk-taking behaviours. Journal of Adolescence, 26(4), 475-479. doi:10.1016/s0140-1971(03)00036-8Verhulst, N., De Keyser, A., Gustafsson, A., Shams, P., & Van Vaerenbergh, Y. (2019). Neuroscience in service research: an overview and discussion of its possibilities. Journal of Service Management, 30(5), 621-649. doi:10.1108/josm-05-2019-0135de-Juan-Ripoll, C., Soler-Domínguez, J. L., Guixeres, J., Contero, M., Álvarez Gutiérrez, N., & Alcañiz, M. (2018). Virtual Reality as a New Approach for Risk Taking Assessment. Frontiers in Psychology, 9. doi:10.3389/fpsyg.2018.02532Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1-3), 7-15. doi:10.1016/0010-0277(94)90018-3Bottari, C., Dassa, C., Rainville, C., & Dutil, É. (2009). The factorial validity and internal consistency of the Instrumental Activities of Daily Living Profile in individuals with a traumatic brain injury. Neuropsychological Rehabilitation, 19(2), 177-207. doi:10.1080/09602010802188435Verschoor, A., D’Exelle, B., & Perez-Viana, B. (2016). Lab and life: Does risky choice behaviour observed in experiments reflect that in the real world? Journal of Economic Behavior & Organization, 128, 134-148. doi:10.1016/j.jebo.2016.05.009Tarr, M. J., & Warren, W. H. (2002). Virtual reality in behavioral neuroscience and beyond. Nature Neuroscience, 5(S11), 1089-1092. doi:10.1038/nn948Alcañiz, M., Rey, B., Tembl, J., & Parkhutik, V. (2009). A Neuroscience Approach to Virtual Reality Experience Using Transcranial Doppler Monitoring. Presence: Teleoperators and Virtual Environments, 18(2), 97-111. doi:10.1162/pres.18.2.97Chittaro, L., & Ranon, R. (2009). Serious Games for Training Occupants of a Building in Personal Fire Safety Skills. 2009 Conference in Games and Virtual Worlds for Serious Applications. doi:10.1109/vs-games.2009.8Lovreglio, R., Gonzalez, V., Amor, R., Spearpoint, M., Thomas, J., Trotter, M., & Sacks, R. (2017). The Need for Enhancing Earthquake Evacuee Safety by Using Virtual Reality Serious Games. Lean and Computing in Construction Congress - Volume 1: Proceedings of the Joint Conference on Computing in Construction. doi:10.24928/jc3-2017/0058Rizzo, A. A., Bowerly, T., Buckwalter, J. G., Klimchuk, D., Mitura, R., & Parsons, T. D. (2009). A Virtual Reality Scenario for All Seasons:The Virtual Classroom. CNS Spectrums, 11(1), 35-44. doi:10.1017/s1092852900024196Chicchi Giglioli, I. A., de Juan Ripoll, C., Parra, E., & Alcañiz Raya, M. (2019). Are 3D virtual environments better than 2D interfaces in serious games performance? An explorative study for the assessment of executive functions. Applied Neuropsychology: Adult, 28(2), 148-157. doi:10.1080/23279095.2019.1607735Huang, H.-M., Rauch, U., & Liaw, S.-S. (2010). Investigating learners’ attitudes toward virtual reality learning environments: Based on a constructivist approach. Computers & Education, 55(3), 1171-1182. doi:10.1016/j.compedu.2010.05.014Dalgarno, B., & Lee, M. J. W. (2009). What are the learning affordances of 3-D virtual environments? British Journal of Educational Technology, 41(1), 10-32. doi:10.1111/j.1467-8535.2009.01038.xFowler, C. (2014). Virtual reality and learning: Where is the pedagogy? British Journal of Educational Technology, 46(2), 412-422. doi:10.1111/bjet.12135Zuckerman, M., Kolin, E. A., Price, L., & Zoob, I. (1964). Development of a sensation-seeking scale. Journal of Consulting Psychology, 28(6), 477-482. doi:10.1037/h0040995Patton, J. H., Stanford, M. S., & Barratt, E. S. (1995). Factor structure of the barratt impulsiveness scale. Journal of Clinical Psychology, 51(6), 768-774. doi:10.1002/1097-4679(199511)51:63.0.co;2-1So, R. H. Y., Lo, W. T., & Ho, A. T. K. (2001). Effects of Navigation Speed on Motion Sickness Caused by an Immersive Virtual Environment. Human Factors: The Journal of the Human Factors and Ergonomics Society, 43(3), 452-461. doi:10.1518/001872001775898223Zuckerman, M. (2008). Sensation Seeking. The International Encyclopedia of Communication. doi:10.1002/9781405186407.wbiecs029Orlebeke, J. F., Van Der Molen, M. W., Dolan, C., & Stoffels, E. J. (1990). The additive factor logic applied to the personality trait disinhibition. Personality and Individual Differences, 11(6), 553-558. doi:10.1016/0191-8869(90)90037-rPopham, L. E., Kennison, S. M., & Bradley, K. I. (2011). Ageism, Sensation-Seeking, and Risk-Taking Behavior in Young Adults. Current Psychology, 30(2), 184-193. doi:10.1007/s12144-011-9107-0Roberti, J. W. (2004). A review of behavioral and biological correlates of sensation seeking. Journal of Research in Personality, 38(3), 256-279. doi:10.1016/s0092-6566(03)00067-9Zuckerman, M., Eysenck, S. B., & Eysenck, H. J. (1978). Sensation seeking in England and America: Cross-cultural, age, and sex comparisons. Journal of Consulting and Clinical Psychology, 46(1), 139-149. doi:10.1037/0022-006x.46.1.139Television campaigns and adolescent marijuana use: tests of sensation seeking targeting. (2001). American Journal of Public Health, 91(2), 292-296. doi:10.2105/ajph.91.2.292Barry, D., & Petry, N. M. (2008). Predictors of decision-making on the Iowa Gambling Task: Independent effects of lifetime history of substance use disorders and performance on the Trail Making Test. Brain and Cognition, 66(3), 243-252. doi:10.1016/j.bandc.2007.09.00

c-Rel Controls Multiple Discrete Steps in the Thymic Development of Foxp3+ CD4 Regulatory T Cells

The development of natural Foxp3+ CD4 regulatory T cells (nTregs) proceeds via two steps that involve the initial antigen dependent generation of CD25+GITRhiFoxp3−CD4+ nTreg precursors followed by the cytokine induction of Foxp3. Using mutant mouse models that lack c-Rel, the critical NF-κB transcription factor required for nTreg differentiation, we establish that c-Rel regulates both of these developmental steps. c-Rel controls the generation of nTreg precursors via a haplo-insufficient mechanism, indicating that this step is highly sensitive to c-Rel levels. However, maintenance of c-Rel in an inactive state in nTreg precursors demonstrates that it is not required for a constitutive function in these cells. While the subsequent IL-2 induction of Foxp3 in nTreg precursors requires c-Rel, this developmental transition does not coincide with the nuclear expression of c-Rel. Collectively, our results support a model of nTreg differentiation in which c-Rel generates a permissive state for foxp3 transcription during the development of nTreg precursors that influences the subsequent IL-2 dependent induction of Foxp3 without a need for c-Rel reactivation

Characterization of MgtC, a Virulence Factor of Salmonella enterica Serovar Typhi

The MgtC is a virulence factor in Salmonella Typhimurium that is required for growth at low-Mg2+ concentrations and intramacrophage survival. This gene is codified in a conserved region of the Salmonella pathogenicity island 3 (SPI-3), and is also present in the chromosome of other Salmonella serovars. In this study we characterized the MgtC factor in S. Typhi, a human specific pathogen, by using mgtC and SPI-3 mutant strains. We found that MgtC is the most important factor codified in the SPI-3 of S. Typhi for growth in low-Mg2+ media and survival within human cells. In addition, by using reporter genes we determined that the low-Mg2+ concentration, acidic media and PhoP regulator induce mgtC expression in S. Typhi. We suggest that MgtC is the most important virulence factor codified in the SPI-3 of S. Typhi

Deciphering the complex interplay between pancreatic cancer, diabetes mellitus subtypes and obesity/BMI through causal inference and mediation analyses.

OBJECTIVES: To characterise the association between type 2 diabetes mellitus (T2DM) subtypes (new-onset T2DM (NODM) or long-standing T2DM (LSDM)) and pancreatic cancer (PC) risk, to explore the direction of causation through Mendelian randomisation (MR) analysis and to assess the mediation role of body mass index (BMI). DESIGN: Information about T2DM and related factors was collected from 2018 PC cases and 1540 controls from the PanGenEU (European Study into Digestive Illnesses and Genetics) study. A subset of PC cases and controls had glycated haemoglobin, C-peptide and genotype data. Multivariate logistic regression models were applied to derive ORs and 95% CIs. T2DM and PC-related single nucleotide polymorphism (SNP) were used as instrumental variables (IVs) in bidirectional MR analysis to test for two-way causal associations between PC, NODM and LSDM. Indirect and direct effects of the BMI-T2DM-PC association were further explored using mediation analysis. RESULTS: T2DM was associated with an increased PC risk when compared with non-T2DM (OR=2.50; 95% CI: 2.05 to 3.05), the risk being greater for NODM (OR=6.39; 95% CI: 4.18 to 9.78) and insulin users (OR=3.69; 95% CI: 2.80 to 4.86). The causal association between T2DM (57-SNP IV) and PC was not statistically significant (ORLSDM=1.08, 95% CI: 0.86 to 1.29, ORNODM=1.06, 95% CI: 0.95 to 1.17). In contrast, there was a causal association between PC (40-SNP IV) and NODM (OR=2.85; 95% CI: 2.04 to 3.98), although genetic pleiotropy was present (MR-Egger: p value=0.03). Potential mediating effects of BMI (125-SNPs as IV), particularly in terms of weight loss, were evidenced on the NODM-PC association (indirect effect for BMI in previous years=0.55). CONCLUSION: Findings of this study do not support a causal effect of LSDM on PC, but suggest that PC causes NODM. The interplay between obesity, PC and T2DM is complex

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

Jet energy measurement with the ATLAS detector in proton-proton collisions at root s=7 TeV

The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of √s = 7TeV corresponding to an integrated luminosity of 38 pb-1. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0. 4 or R=0. 6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pT≥20 GeV and pseudorapidities {pipe}η{pipe}<4. 5. The jet energy systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The jet energy uncertainty is less than 2. 5 % in the central calorimeter region ({pipe}η{pipe}<0. 8) for jets with 60≤pT<800 GeV, and is maximally 14 % for pT<30 GeV in the most forward region 3. 2≤{pipe}η{pipe}<4. 5. The jet energy is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, aiming for an improved jet energy resolution and a reduced flavour dependence of the jet response. The systematic uncertainty of the jet energy determined from a combination of in situ techniques is consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined. © 2013 CERN for the benefit of the ATLAS collaboration

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Search for high-mass resonances decaying to dilepton final states in pp collisions at s√=7 TeV with the ATLAS detector

The ATLAS detector at the Large Hadron Collider is used to search for high-mass resonances decaying to an electron-positron pair or a muon-antimuon pair. The search is sensitive to heavy neutral Z′ gauge bosons, Randall-Sundrum gravitons, Z * bosons, techni-mesons, Kaluza-Klein Z/γ bosons, and bosons predicted by Torsion models. Results are presented based on an analysis of pp collisions at a center-of-mass energy of 7 TeV corresponding to an integrated luminosity of 4.9 fb−1 in the e + e − channel and 5.0 fb−1 in the μ + μ −channel. A Z ′ boson with Standard Model-like couplings is excluded at 95 % confidence level for masses below 2.22 TeV. A Randall-Sundrum graviton with coupling k/MPl=0.1 is excluded at 95 % confidence level for masses below 2.16 TeV. Limits on the other models are also presented, including Technicolor and Minimal Z′ Models

Search for R-parity-violating supersymmetry in events with four or more leptons in sqrt(s) =7 TeV pp collisions with the ATLAS detector

A search for new phenomena in final states with four or more leptons (electrons or muons) is presented. The analysis is based on 4.7 fb−1 of $\sqrt{s}=7\;\mathrm{TeV}$ proton-proton collisions delivered by the Large Hadron Collider and recorded with the ATLAS detector. Observations are consistent with Standard Model expectations in two signal regions: one that requires moderate values of missing transverse momentum and another that requires large effective mass. The results are interpreted in a simplified model of R-parity-violating supersymmetry in which a 95% CL exclusion region is set for charged wino masses up to 540 GeV. In an R-parity-violating MSUGRA/CMSSM model, values of m 1/2 up to 820 GeV are excluded for 10 < tan β < 40