Articles de revues : « Resistance-to-time »

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The Lancet. "Time to contain artemisinin resistance." Lancet 383, no. 9927 (2014): 1438. http://dx.doi.org/10.1016/s0140-6736(14)60700-5.

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Woods, Anne. "Antibiotic resistance: Time to fight back." Dimensions of Critical Care Nursing 20, no. 3 (2001): 56. http://dx.doi.org/10.1097/00003465-200105000-00014.

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Howarth, Frank, and David Poulter. "Vancomycin resistance: time to ban avoparcin?" Lancet 347, no. 9007 (1996): 1047. http://dx.doi.org/10.1016/s0140-6736(96)90187-7.

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Munro, Rob. "Antimicrobial resistance - time to start spending." Prescriber 28, no. 1 (2017): 4. http://dx.doi.org/10.1002/psb.1527.

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Stebbing, Justin, and Tim Crook. "Cytotoxic-free treatment designs: time to resistance." Nature Clinical Practice Oncology 4, no. 1 (2007): 1. http://dx.doi.org/10.1038/ncponc0695.

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Babkin, Sergey, and Anna Epishkina. "One-Time Passwords: Resistance to Masquerade Attack." Procedia Computer Science 145 (2018): 199–203. http://dx.doi.org/10.1016/j.procs.2018.11.040.

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Liu, Yong-Biao, Bruce E. Tabashnik, Timothy J. Dennehy, Amanda L. Patin, and Alan C. Bartlett. "Development time and resistance to Bt crops." Nature 400, no. 6744 (1999): 519. http://dx.doi.org/10.1038/22919.

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Yerly, Sabine, and Alexandra Calmy. "Time to overcome pretreatment HIV drug resistance." Lancet Infectious Diseases 18, no. 3 (2018): 239–40. http://dx.doi.org/10.1016/s1473-3099(17)30709-0.

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Swartz, Bradford L. "Resistance of Voice Onset Time Variability to Intoxication." Perceptual and Motor Skills 75, no. 2 (1992): 415–24. http://dx.doi.org/10.2466/pms.1992.75.2.415.

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Voice onset times of /d/ and /t/ were measured for 16 adult subjects (age range 21 to 26 years) under conditions of sobriety and intoxication. Subjects consumed beer to reach intoxication levels between 0.075 and 0.100% as measured using a portable breathalyzer test. Analysis indicated consistent variabilities over time for each subject and resistance of VOT variability to alcohol influence.

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The Lancet. "Antimicrobial resistance: time to repurpose the Global Fund." Lancet 399, no. 10322 (2022): 335. http://dx.doi.org/10.1016/s0140-6736(22)00091-5.

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Charlie, Wayne A., Mutabihirwa F. J. Rwebyogo, and Donald O. Doehring. "Time‐Dependent Cone Penetration Resistance due to Blasting." Journal of Geotechnical Engineering 118, no. 8 (1992): 1200–1215. http://dx.doi.org/10.1061/(asce)0733-9410(1992)118:8(1200).

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Hawkey, Peter M. "Action against antibiotic resistance: no time to lose." Lancet 351, no. 9112 (1998): 1298–99. http://dx.doi.org/10.1016/s0140-6736(98)22018-6.

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Wood, Evan, and Julio SG Montaner. "Time to get serious about HIV antiretroviral resistance." Lancet Infectious Diseases 11, no. 10 (2011): 723–24. http://dx.doi.org/10.1016/s1473-3099(11)70216-x.

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May, Tim. "From Banana Time to Just-in-Time: Power and Resistance at Work." Sociology 33, no. 4 (1999): 767–83. http://dx.doi.org/10.1177/s0038038599000486.

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MAY, TIM. "FROM BANANA TIME TO JUST-IN-TIME: POWER AND RESISTANCE AT WORK." Sociology 33, no. 4 (1999): 767–83. http://dx.doi.org/10.1017/s0038038599000486.

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Su, Lujun, Maxwell K. Hsu, and Brian Huels. "First-time versus repeat tourists: resistance to negative information." Journal of Service Theory and Practice 32, no. 2 (2021): 258–82. http://dx.doi.org/10.1108/jstp-04-2020-0076.

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Purpose The purpose of this paper is to review the literature regarding negative information’s impact on consumer behavior in the context of tourism services. In addition, this paper empirically examines the likely difference between first-time and repeat tourists in terms of their: resistance to negative information.Design/methodology/approach Using a sample of 539 visitors to Mount Yuelu, a popular tourist destination in China, this study explores the differences between first-time and repeat tourists regarding how destination social responsibility (DSR) and service quality (SQ) influence tourist resistance to negative information.Findings The effect of SQ on resistance to negative information is stronger for repeat tourists than for first-time tourists. In addition, the study identifies that DSR and SQ have a positive impact on tourists’ resistance to negative information. Finally, findings indicate that destination identification partially mediates the relationship between DSR, SQ and tourists’ response to negative information, respectively.Research limitations/implications The findings provide valuable theoretical and empirical insights into the driving factors that influence consumer resistance to negative information.Practical implications The paper brings together DSR, SQ and tourist-destination identification to better understand the impact that visitation frequency (first-time versus repeat tourists) has on how tourists resist negative information about a tourist destination.Social implications Negative information that is generated about a destination may cause the number of future tourism visits to decline. Findings of this paper provide insight as to the framework that can make tourists more resistant to said negative information.Originality/value This study contributes to the services marketing and tourism literature by investigating the degree to which DSR and SQ affect tourist resistance to negative information as mediated by tourist-destination identification and moderated by visiting frequency.

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Duerr, Hans P., Wolfgang H. Hoffmann, and Martin Eichner. "Does resistance to filarial reinfections become leaky over time?" Trends in Parasitology 24, no. 8 (2008): 350–54. http://dx.doi.org/10.1016/j.pt.2008.04.005.

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Syed, Shahida N., Marie J. Ducrotoy, and Till T. Bachmann. "Antimicrobial resistance diagnostics: time to call in the young?" Lancet Infectious Diseases 16, no. 5 (2016): 519–21. http://dx.doi.org/10.1016/s1473-3099(16)30011-1.

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Idemyor, Vincent. "Bacterial Resistance to Antimicrobial Agents—The Time for Concern." Annals of Pharmacotherapy 27, no. 10 (1993): 1285. http://dx.doi.org/10.1177/106002809302701023.

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Yang, Jinglei, Zhong Zhang, Klaus Friedrich, and Alois K. Schlarb. "Resistance to time-dependent deformation of nanoparticle/polymer composites." Applied Physics Letters 91, no. 1 (2007): 011901. http://dx.doi.org/10.1063/1.2753494.

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Virgil, Sharon. "More Time and Choices Overcome Students’ Resistance to Reading." Clearing House: A Journal of Educational Strategies, Issues and Ideas 68, no. 1 (1994): 52–54. http://dx.doi.org/10.1080/00098655.1994.11478625.

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Kaliyugavaradan, S. "A linear resistance-to-time converter with high resolution." IEEE Transactions on Instrumentation and Measurement 49, no. 1 (2000): 151–53. http://dx.doi.org/10.1109/19.836326.

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Hu, Lanxi, and Li Yang. "Time to Fight: Molecular Mechanisms of Age-Related Resistance." Phytopathology® 109, no. 9 (2019): 1500–1508. http://dx.doi.org/10.1094/phyto-11-18-0443-rvw.

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Plant age is a crucial factor in determining the outcome of a host−pathogen interaction. In successive developmental stages throughout their life cycles, plants face dynamic changes in biotic and abiotic conditions that create distinct ecological niches for host−pathogen interactions. As an adaptive strategy, plants have evolved intrinsic regulatory networks that integrate developmental signals with those from pathogen perception and defense activation. As a result, amplitude and timing of defense responses are optimized, so as to balance the cost and benefit of immunity activation. A general term “age-related resistance” refers to a gain of disease resistance against a certain pathogen when plants reach a relatively mature stage. Age-related resistance is a common observation on fruits, vegetables, and row crops for their resistance against viruses, bacteria, fungi, oomycetes pathogens, and insects. This review focuses on the recent advances in understanding the molecular mechanisms of how plants coordinate developmental timing and immune response.

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Barrera, R., and V. Medialdea. "Development time and resistance to starvation of mosquito larvae." Journal of Natural History 30, no. 3 (1996): 447–58. http://dx.doi.org/10.1080/00222939600770231.

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Castagneyrol, Bastien, Mikhail V. Kozlov, Charlotte Poeydebat, Maude Toïgo, and Hervé Jactel. "Associational resistance to a pest insect fades with time." Journal of Pest Science 93, no. 1 (2019): 427–37. http://dx.doi.org/10.1007/s10340-019-01148-y.

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Lovell, W. George. "Maya Resistance to Spanish Rule: Time and History on a Colonial Frontier:Maya Resistance to Spanish Rule: Time and History on a Colonial Frontier." Latin American Anthropology Review 3, no. 2 (1991): 79. http://dx.doi.org/10.1525/jlat.1991.3.2.79.1.

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Chung, W. S., M. Y. An, and S. H. Son. "A simple resistance-to-time converter for resistive bridge sensors." IEICE Electronics Express 5, no. 9 (2008): 310–15. http://dx.doi.org/10.1587/elex.5.310.

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Kim, Hee-young. "Nam June Paik’s Electronic Television: Resistance to the Controlled Time." Journal of Contemporary Art Studies 23, no. 1 (2019): 63–83. http://dx.doi.org/10.29330/jcas.2019.06.23.1.63.

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Boldrini, José L., and Michel I. S. Costa. "Chemotherapeutic Treatments with Time Increasing Mutation Rate to Drug Resistance." Journal of Biological Systems 05, no. 01 (1997): 49–62. http://dx.doi.org/10.1142/s0218339097000059.

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A system of differential equations for the control of tumor growth cells in a cycle nonspecific chemotherapy is analyzed. Spontaneously acquired drug resistance is taken into account by means of a mutation rate increasingly dependent on time. For general tumor growth and drug kill rates the optimal treatment consists of maximum allowable drug concentration throughout, supporting the conjecture that variable mutation rate to drug resistance does not basically alter the corresponding results of constant mutation rate.

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Wang, Liyun, Hefei Ling, Fuhao zou, and Zhengding Lu. "Real-Time Compressed- Domain Video Watermarking Resistance to Geometric Distortions." IEEE Multimedia 19, no. 1 (2012): 70–79. http://dx.doi.org/10.1109/mmul.2011.76.

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Kelly, Stephen A., Aoife M. Rodgers, Séamus C. O’Brien, Ryan F. Donnelly, and Brendan F. Gilmore. "Gut Check Time: Antibiotic Delivery Strategies to Reduce Antimicrobial Resistance." Trends in Biotechnology 38, no. 4 (2020): 447–62. http://dx.doi.org/10.1016/j.tibtech.2019.10.008.

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Pellicano, Rinaldo, and Mario Rizzetto. "Helicobacter Pylori and Insulin Resistance: Time to Do Interventional Studies." Digestive Diseases and Sciences 54, no. 12 (2009): 2765–66. http://dx.doi.org/10.1007/s10620-009-0972-4.

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Gaurav, V., S. N. Bhattacharya, N. Sharma, et al. "Terbinafine resistance in dermatophytes: Time to revisit alternate antifungal therapy." Journal of Medical Mycology 31, no. 1 (2021): 101087. http://dx.doi.org/10.1016/j.mycmed.2020.101087.

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Rezende, Osvaldo M., Luciana F. Guimarães, Francis M. Miranda, Assed N. Haddad, and Marcelo G. Miguez. "A Time-Integrated Index for Flood Risk to Resistance Capacity." Water 11, no. 7 (2019): 1321. http://dx.doi.org/10.3390/w11071321.

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The lack of open spaces and the intense land use occupation in flood plains makes floods in consolidated urban areas difficult to mitigate. In these areas, setting a standard pre-defined return period for projects can limit and even preclude flood mitigation actions. However, it is possible to propose flood control alternatives that are compatible with available spaces. Thus, determining how much the original risk is reduced and how significant the residual risk can be becomes the main target. In this context, a time-integrated index for risk to resistance capacity is proposed to address these questions. This index correlates the exposure of buildings and urban infrastructure to the hazard of a given flood and is then evaluated over a project horizon through a sequence of events. The proposed index is applied to the Canal do Mangue catchment, a highly urbanized watershed located in Rio de Janeiro. The results demonstrate the difficulty of designing flood mitigation measures in extremely occupied watersheds and the importance of evaluating residual risks associated with proposed projects. As an additional result, a scenario with concentrated measures is compared to another with distributed interventions, evidencing the greater coverage of the latter.

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Nicolelis, Miguel A. L., and Luiz Antonio Baccala. "Time series analysis of rhythmic bacterial resistance development to antibiotics." Computers and Biomedical Research 21, no. 2 (1988): 137–57. http://dx.doi.org/10.1016/0010-4809(88)90022-5.

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Gültürk, İlkay, Gülçin Şahingöz Erdal, Gülru Birce Sönmezoz, et al. "Time to Castration Resistance as a Predictor of Response to Docetaxel in Metastatic Castration Resistance in Prostate Cancer." Istanbul Medical Journal 23, no. 4 (2022): 269–74. http://dx.doi.org/10.4274/imj.galenos.2022.56804.

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Pearcey, G. E. P., S. Alizedah, K. E. Power, and D. C. Button. "Chronic resistance training: is it time to rethink the time course of neural contributions to strength gain?" European Journal of Applied Physiology 121, no. 9 (2021): 2413–22. http://dx.doi.org/10.1007/s00421-021-04730-4.

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Bagrationi, Konstantin, and Thomas Thurner. "Using the future time perspective to analyse resistance to, and readiness for, change." Employee Relations: The International Journal 42, no. 1 (2020): 262–79. http://dx.doi.org/10.1108/er-04-2018-0113.

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Purpose When a major Russian energy provider introduced a new technology that required organisational adjustment, the company’s management was surprised by the degree of internal resistance these changes provoked. The paper aims to discuss this issue. Design/methodology/approach The authors took reference to the work on readiness to change and studied how the future time perspective, which connects with early writings by Lewin (1942), would explain the attitudes and behaviour of 148 managers. Findings The findings indicate that only a small number of employees perceived the future as offering many opportunities and showed willingness to pursue them. The majority of employees are either fearful of future changes, or do not have a strong sense of belonging to the company and hence are disinterested in prospective opportunities within the firm. Originality/value The different constructs of the future introduce an emic perspective to the study of organisational change and answer calls to enrich the measurements that are currently in use.

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Haenssgen, Marco J., Nutcha Charoenboon, and Yuzana Khine Zaw. "It is time to give social research a voice to tackle antimicrobial resistance?" Journal of Antimicrobial Chemotherapy 73, no. 4 (2018): 1112–13. http://dx.doi.org/10.1093/jac/dkx533.

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Kampf, G. "Acquired resistance to chlorhexidine – is it time to establish an ‘antiseptic stewardship’ initiative?" Journal of Hospital Infection 94, no. 3 (2016): 213–27. http://dx.doi.org/10.1016/j.jhin.2016.08.018.

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Alvarez, Isabel, and Carles Dulsat. "Resistance to a First-Time Online Training Course for Sport Technicians." International Journal of Education 8, no. 4 (2016): 92. http://dx.doi.org/10.5296/ije.v8i4.10413.

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Decreasing enrollment at The Royal Spanish Skating Federation for Training SportTechnicians compelled the adoption of an online version of a training course. This paperidentifies the resistance experienced by participants and explores the relations among fourpower variables: communication and participation, daily routines, flexibility of the courses,and training programs. Participants included students and instructors who completed aquestionnaire. The results indicated that the students felt that they lack contact and interaction,leading to a feeling of apprehension. In addition, they had limited Internet access, whichaggravated their lack of participation, isolating them and leaving them feeling inadequatelyinformed. In contrast, the instructors focused their attention on the training programs. Thispaper proposes solutions and improvements for future versions of the course for redressingimbalances in power relationships among the participants.

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Nowotny, Alois, Mary Ellen Moore, Grace Nejman, and Anna M. Nowotny. "Time Dependency of Endotoxin-Induced Resistance to Transplantable Tumors in Mice." Cancer Investigation 5, no. 3 (1987): 195–203. http://dx.doi.org/10.3109/07357908709011736.

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Penta, Leo J. "Resistance to the Rule of Time or a "Post-Metaphysical Metaphysics"." Philosophy Today 37, no. 2 (1993): 211–24. http://dx.doi.org/10.5840/philtoday199337227.

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Aljundi, Lamia, Nichole Miller, Andrew Taylor, Jonathan Hung, and Clara Hwang. "Time to castration-resistance and docetaxel outcomes in metastatic prostate cancer." Journal of Clinical Oncology 34, no. 15_suppl (2016): e16519-e16519. http://dx.doi.org/10.1200/jco.2016.34.15_suppl.e16519.

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MacKenzie, K., and S. C. Bishop. "A discrete-time epidemiological model to quantify selection for disease resistance." Animal Science 69, no. 3 (1999): 543–51. http://dx.doi.org/10.1017/s1357729800051390.

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AbstractA genetic epidemiological model (GEM) for investigating the effect of selection for disease resistance on the epidemiology of infectious diseases is presented and applied to a pig breeding scenario. Fundamental to the model is R0, the basic reproductive ratio. R0 is the expected number of secondary infections caused by a single infection. If R0 is greater than 1, there will be an epidemic. The aim of the model is to quantify the effect of selection on R0 and the consequences this has on disease epidemiology. Two implementations are presented: selection for reduced susceptibility/infectivity to a disease and introgression of a major resistance gene. The results suggest that the effects of selection for reduced susceptibility I infectivity are critically dependent on the infectiousness of the disease. Under the assumptions made in the model, for a disease with a low infection level, it takes approximately 15 years of selection until R0 is less than 1 and the population is safe from epidemics should the infectious agent be present. For a highly infectious disease, this time may be as long as 100 years. For gene introgression, the population is expected to be free from epidemics within 5 years and the time to reduce R0 to less than 1 is largely independent of the disease being considered. With gene introgression, the proportion of the population which needs to be resistant to ensure that R0 is less than one is shown to be a function of the initial R0 for the disease. Although selection, as modelled, results in a linear decline in R0, the reduction in the proportion of animals infected during an epidemic is non-linear. The selection process reduces the amount of infectious material that is in the environment when an infection occurs and this decreases the force of infection on unselected animals. This phenomenon results in a marked interaction between host genotype and disease epidemiology. Thus, the results of the model show that altering the genetics of individual animals affects the epidemiology of the disease at the population level. The model can be applied to any farm structure and any microparasitic infectious disease.

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Tinsley, Grant M., Jeffrey S. Forsse, Grant B. Morgan, et al. "Variation in Individual Responses to Time-Restricted Feeding and Resistance Training." Medicine & Science in Sports & Exercise 48 (May 2016): 950. http://dx.doi.org/10.1249/01.mss.0000487845.88331.f9.

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Peck, Steven L. "Antibiotic and insecticide resistance modeling – is it time to start talking?" Trends in Microbiology 9, no. 6 (2001): 286–92. http://dx.doi.org/10.1016/s0966-842x(01)02042-x.

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Anandanatarajan, Ramya, Umapathy Mangalanathan, and Uma Gandhi. "Enhanced Microcontroller Interface of Resistive Sensors Through Resistance-to-Time Converter." IEEE Transactions on Instrumentation and Measurement 69, no. 6 (2020): 2698–706. http://dx.doi.org/10.1109/tim.2019.2928348.

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Kellner, C. H., and A. Nordenskjöld. "‘Treatment resistance’ in electroconvulsive therapy (ECT) patients: time to move on." Acta Psychiatrica Scandinavica 140, no. 5 (2019): 490–91. http://dx.doi.org/10.1111/acps.13091.

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Burgess, I. S., and J. E. Wright. "Resistance to variable-time schedules produced by spaced-response reinforcement schedules." Behavioural Processes 11, no. 4 (1985): 389–404. http://dx.doi.org/10.1016/0376-6357(85)90004-x.

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