829 research outputs found
New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.
Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes
H. pylori-infection and antibody immune response in a rural Tanzanian population
BACKGROUND: Helicobacter pylori (H. pylori) infection is ubiquitous in sub-Saharan Africa, but paradoxically gastric cancer is rare. METHODS: Sera collected during a household-based survey in rural Tanzania in 1985 were tested for anti-H. pylori IgG and IgG subclass antibodies by enzyme immunoassay. Odds ratios (OR) and confidence intervals (CI) of association of seropositivity with demographic variables were computed by logistic regression models. RESULTS: Of 788 participants, 513 were aged ≤17 years. H. pylori seropositivity increased from 76% at 0–4 years to 99% by ≥18 years of age. Seropositivity was associated with age (OR 11.5, 95% CI 4.2–31.4 for 10–17 vs. 0–4 years), higher birth-order (11.1; 3.6–34.1 for ≥3(rd )vs. 1(st )born), and having a seropositive next-older sibling (2.7; 0.9–8.3). Median values of IgG subclass were 7.2 for IgG1 and 2.0 for IgG2. The median IgG1/IgG2 ratio was 3.1 (IQR: 1.7–5.6), consistent with a Th2-dominant immune profile. Th2-dominant response was more frequent in children than adults (OR 2.4, 95% CI 1.3–4.4). CONCLUSION: H. pylori seropositivity was highly prevalent in Tanzania and the immunological response was Th2-dominant. Th2-dominant immune response, possibly caused by concurrent bacterial or parasitic infections, could explain, in part, the lower risk of H. pylori-associated gastric cancer in Africa
Compressed representation of a partially defined integer function over multiple arguments
In OLAP (OnLine Analitical Processing) data are analysed in an n-dimensional cube. The cube may be represented as a partially defined function over n arguments. Considering that often the function is not defined everywhere, we ask: is there a known way of representing the function or the points in which it is defined, in a more compact manner than the trivial one
TERRA Promotes Telomere Shortening through Exonuclease 1–Mediated Resection of Chromosome Ends
The long noncoding telomeric repeat containing RNA (TERRA) is expressed at chromosome ends. TERRA upregulation upon experimental manipulation or in ICF (immunodeficiency, centromeric instability, facial anomalies) patients correlates with short telomeres. To study the mechanism of telomere length control by TERRA in Saccharomyces cerevisiae, we mapped the transcriptional start site of TERRA at telomere 1L and inserted a doxycycline regulatable promoter upstream. Induction of TERRA transcription led to telomere shortening of 1L but not of other chromosome ends. TERRA interacts with the Exo1-inhibiting Ku70/80 complex, and deletion of EXO1 but not MRE11 fully suppressed the TERRA–mediated short telomere phenotype in presence and absence of telomerase. Thus TERRA transcription facilitates the 5′-3′ nuclease activity of Exo1 at chromosome ends, providing a means to regulate the telomere shortening rate. Thereby, telomere transcription can regulate cellular lifespan through modulation of chromosome end processing activities
Pharmacological evidence for the stimulation of NADPH oxidase by P2X7 receptors in mouse submandibular glands
ATP in the 100 μM-1 mM concentration range provoked a calcium-independent increase of the oxidation of dichlorodihydrofluorescein (DCFH) to dichlorofluorescein (DCF) by mouse submandibular cells. 3′-O-(4-benzoyl)benzoyl adenosine 5′-triphosphate (BzATP), a P2X7 agonist, but not a muscarinic or an adrenergic agonist, reproduced the effect of ATP. The inhibition of phospholipase C by U73122 or the potentiation of P2X4 receptor activation with ivermectin did not modify the response to ATP. ATP did not increase the oxidation of DCFH in cells isolated from submandibular glands of P2X7 knockout mice or in cells pretreated with a P2X7 antagonist. The inhibition of protein kinase C or of mitogen-activated protein kinase (MAP kinase) or of reduced nicotinamide adenine dinucleotide phosphate (NADPH) oxidase blocked the oxidation of DCFH without affecting the increase of the intracellular concentration of calcium or the uptake of ethidium bromide in response to extracellular ATP. From these results it is concluded that the activation of the P2X7 receptors from submandibular glands triggers an intracellular signalling cascade involving protein kinase C and MAP kinase leading to the stimulation of NADPH oxidase and the subsequent generation of reactive oxygen species
Influence of socio-economic status on habitual physical activity and sedentary behavior in 8- to 11-year old children
<p>Abstract</p> <p>Background</p> <p>While socio-economic status has been shown to be an important determinant of health and physical activity in adults, results for children and adolescents are less consistent. The purpose of this study, therefore, is to examine whether physical activity and sedentary behavior differs in children by socio-economic status (SES) independent of body mass index.</p> <p>Methods</p> <p>Data were from two cohorts including 271 children (117 males; 154 females) in study 1 and 131 children in study 2 (63 males; 68 females). The average age was 9.6 and 8.8 years respectively. Height and body mass were assessed according to standard procedures and body mass index (BMI, kg/m<sup>2</sup>) was calculated. Parent-reported household income was used to determine SES. Habitual, free-living physical activity (PA) was assessed by a pedometer (steps/day) in study 1 and accelerometer (time spent in moderate-to-vigorous PA) in study 2. Self-reported time spent watching TV and on the computer was used as measure of sedentary behavior. Differences in PA and sedentary behavior by SES were initially tested using ANOVA. Further analyses used ANCOVA controlling for BMI, as well as leg length in the pedometer cohort.</p> <p>Results</p> <p>In study 1, mean daily steps differed significantly among SES groups with lower SES groups approximating 10,500 steps/day compared to about 12,000 steps/day in the higher SES groups. These differences remained significant (p < 0.05) when controlling for leg length. Lower SES children, however, had higher body mass and BMI compared to higher SES groups (p < 0.05) and PA no longer remained significant when further controlling for BMI. In study 2 results depended on the methodology used to determine time spent in moderate-to-vigorous physical activity (MVPA). Only one equation resulted in significant group differences (p = 0.015), and these differences remained after controlling for BMI. Significant differences between SES groups were shown for sedentary behavior in both cohorts (P < 0.05) with higher SES groups spending less time watching TV than low SES groups.</p> <p>Conclusions</p> <p>Children from a low SES show a trend of lower PA levels and spend more time in sedentary behavior than high SES children; however, differences in PA were influenced by BMI. The higher BMI in these children might be another factor contributing to increased health risks among low SES children compared to children from with a higher SES.</p
A Novel System of Cytoskeletal Elements in the Human Pathogen Helicobacter pylori
Pathogenicity of the human pathogen Helicobacter pylori relies upon its capacity to adapt to a hostile environment and to escape from the host response. Therefore, cell shape, motility, and pH homeostasis of these bacteria are specifically adapted to the gastric mucus. We have found that the helical shape of H. pylori depends on coiled coil rich proteins (Ccrp), which form extended filamentous structures in vitro and in vivo, and are differentially required for the maintenance of cell morphology. We have developed an in vivo localization system for this pathogen. Consistent with a cytoskeleton-like structure, Ccrp proteins localized in a regular punctuate and static pattern within H. pylori cells. Ccrp genes show a high degree of sequence variation, which could be the reason for the morphological diversity between H. pylori strains. In contrast to other bacteria, the actin-like MreB protein is dispensable for viability in H. pylori, and does not affect cell shape, but cell length and chromosome segregation. In addition, mreB mutant cells displayed significantly reduced urease activity, and thus compromise a major pathogenicity factor of H. pylori. Our findings reveal that Ccrp proteins, but not MreB, affect cell morphology, while both cytoskeletal components affect the development of pathogenicity factors and/or cell cycle progression
Alternating irinotecan with oxaliplatin combined with UFT plus leucovorin (SCOUT) in metastatic colorectal cancer
Tegafur–uracil (UFT) plus leucovorin® (LV, folinic acid) with alternating irinotecan and oxaliplatin were effective and well tolerated in patients with metastatic colorectal cancer (mCRC) in a phase I study. This study expanded the maximum tolerated dose group. Patients aged ⩾18 years had histologically confirmed, inoperable, previously untreated, measurable mCRC. Patients received irinotecan 180 mg m−2 on day 1, oxaliplatin 100 mg m−2 on day 15 and UFT 250 mg m−2 plus LV 90 mg on days 1–21 every 28 days. The phase I/II study comprised 45 patients, 29 at the maximum tolerated dose (MTD). The response rate in 38 evaluable patients was 63% (95% confidence interval (CI): 49–80). Median time to progression and overall survival were 8.7 months (95% CI: 7.9–10.4) and 16.8 months (95% CI: 9.6–25.3), respectively. In the MTD group, one patient had grade 3 leucopaenia; one had grade 3 neutropaenia; three had grade 3 diarrhoea; and one had grade 3 neurotoxicity. No hand–foot syndrome grade >1 was seen. In total, 67% of eligible patients received second-line therapy. UFT plus LV with alternating irinotecan and oxaliplatin is an efficacious first-line treatment for mCRC, with minimal neurotoxicity and hand–foot syndrome
An Engineering Approach to Extending Lifespan in C. elegans
We have taken an engineering approach to extending the lifespan of Caenorhabditis elegans. Aging stands out as a complex trait, because events that occur in old animals are not under strong natural selection. As a result, lifespan can be lengthened rationally using bioengineering to modulate gene expression or to add exogenous components. Here, we engineered longer lifespan by expressing genes from zebrafish encoding molecular functions not normally present in worms. Additionally, we extended lifespan by increasing the activity of four endogenous worm aging pathways. Next, we used a modular approach to extend lifespan by combining components. Finally, we used cell- and worm-based assays to analyze changes in cell physiology and as a rapid means to evaluate whether multi-component transgenic lines were likely to have extended longevity. Using engineering to add novel functions and to tune endogenous functions provides a new framework for lifespan extension that goes beyond the constraints of the worm genome
Flow shop rescheduling under different types of disruption
This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 2013, available online:http://www.tandfonline.com/10.1080/00207543.2012.666856Almost all manufacturing facilities need to use production planning and scheduling systems to increase productivity and to reduce production costs. Real-life production operations are subject to a large number of unexpected disruptions that may invalidate the original schedules. In these cases, rescheduling is essential to minimise the impact on the performance of the system. In this work we consider flow shop layouts that have seldom been studied in the rescheduling literature. We generate and employ three types of disruption that interrupt the original schedules simultaneously. We develop rescheduling algorithms to finally accomplish the twofold objective of establishing a standard framework on the one hand, and proposing rescheduling methods that seek a good trade-off between schedule quality and stability on the other.The authors would like to thank the anonymous referees for their careful and detailed comments that helped to improve the paper considerably. This work is partially financed by the Small and Medium Industry of the Generalitat Valenciana (IMPIVA) and by the European Union through the European Regional Development Fund (FEDER) inside the R + D program "Ayudas dirigidas a Institutos tecnologicos de la Red IMPIVA" during the year 2011, with project number IMDEEA/2011/142.Katragjini Prifti, K.; Vallada Regalado, E.; Ruiz García, R. (2013). Flow shop rescheduling under different types of disruption. International Journal of Production Research. 51(3):780-797. https://doi.org/10.1080/00207543.2012.666856S780797513Abumaizar, R. J., & Svestka, J. A. (1997). Rescheduling job shops under random disruptions. International Journal of Production Research, 35(7), 2065-2082. doi:10.1080/002075497195074Adiri, I., Frostig, E., & Kan, A. H. G. R. (1991). Scheduling on a single machine with a single breakdown to minimize stochastically the number of tardy jobs. Naval Research Logistics, 38(2), 261-271. doi:10.1002/1520-6750(199104)38:23.0.co;2-iAkturk, M. S., & Gorgulu, E. (1999). Match-up scheduling under a machine breakdown. European Journal of Operational Research, 112(1), 81-97. doi:10.1016/s0377-2217(97)00396-2Allahverdi, A. (1996). Two-machine proportionate flowshop scheduling with breakdowns to minimize maximum lateness. Computers & Operations Research, 23(10), 909-916. doi:10.1016/0305-0548(96)00012-3Arnaout, J. P., & Rabadi, G. (2008). Rescheduling of unrelated parallel machines under machine breakdowns. International Journal of Applied Management Science, 1(1), 75. doi:10.1504/ijams.2008.020040Artigues, C., Billaut, J.-C., & Esswein, C. (2005). Maximization of solution flexibility for robust shop scheduling. European Journal of Operational Research, 165(2), 314-328. doi:10.1016/j.ejor.2004.04.004Azizoglu, M., & Alagöz, O. (2005). Parallel-machine rescheduling with machine disruptions. IIE Transactions, 37(12), 1113-1118. doi:10.1080/07408170500288133Bean, J. C., Birge, J. R., Mittenthal, J., & Noon, C. E. (1991). Matchup Scheduling with Multiple Resources, Release Dates and Disruptions. Operations Research, 39(3), 470-483. doi:10.1287/opre.39.3.470Caricato, P., & Grieco, A. (2008). An online approach to dynamic rescheduling for production planning applications. International Journal of Production Research, 46(16), 4597-4617. doi:10.1080/00207540601136225CHURCH, L. K., & UZSOY, R. (1992). Analysis of periodic and event-driven rescheduling policies in dynamic shops. International Journal of Computer Integrated Manufacturing, 5(3), 153-163. doi:10.1080/09511929208944524Cowling, P., & Johansson, M. (2002). Using real time information for effective dynamic scheduling. European Journal of Operational Research, 139(2), 230-244. doi:10.1016/s0377-2217(01)00355-1Curry, J., & Peters *, B. (2005). Rescheduling parallel machines with stepwise increasing tardiness and machine assignment stability objectives. International Journal of Production Research, 43(15), 3231-3246. doi:10.1080/00207540500103953DUTTA, A. (1990). Reacting to Scheduling Exceptions in FMS Environments. IIE Transactions, 22(4), 300-314. doi:10.1080/07408179008964185Ghezail, F., Pierreval, H., & Hajri-Gabouj, S. (2010). Analysis of robustness in proactive scheduling: A graphical approach. Computers & Industrial Engineering, 58(2), 193-198. doi:10.1016/j.cie.2009.03.004Goren, S., & Sabuncuoglu, I. (2008). Robustness and stability measures for scheduling: single-machine environment. IIE Transactions, 40(1), 66-83. doi:10.1080/07408170701283198Hall, N. G., & Potts, C. N. (2004). Rescheduling for New Orders. Operations Research, 52(3), 440-453. doi:10.1287/opre.1030.0101Herrmann, J. W., Lee, C.-Y., & Snowdon, J. L. (1993). A Classification of Static Scheduling Problems. Complexity in Numerical Optimization, 203-253. doi:10.1142/9789814354363_0011Herroelen, W., & Leus, R. (2005). Project scheduling under uncertainty: Survey and research potentials. European Journal of Operational Research, 165(2), 289-306. doi:10.1016/j.ejor.2004.04.002Hozak, K., & Hill, J. A. (2009). Issues and opportunities regarding replanning and rescheduling frequencies. International Journal of Production Research, 47(18), 4955-4970. doi:10.1080/00207540802047106Huaccho Huatuco, L., Efstathiou, J., Calinescu, A., Sivadasan, S., & Kariuki, S. (2009). Comparing the impact of different rescheduling strategies on the entropic-related complexity of manufacturing systems. International Journal of Production Research, 47(15), 4305-4325. doi:10.1080/00207540701871036Jensen, M. T. (2003). Generating robust and flexible job shop schedules using genetic algorithms. IEEE Transactions on Evolutionary Computation, 7(3), 275-288. doi:10.1109/tevc.2003.810067King, J. R. (1976). The theory-practice gap in job-shop scheduling. Production Engineer, 55(3), 137. doi:10.1049/tpe.1976.0044Kopanos, G. M., Capón-García, E., Espuña,, A., & Puigjaner, L. (2008). Costs for Rescheduling Actions: A Critical Issue for Reducing the Gap between Scheduling Theory and Practice. Industrial & Engineering Chemistry Research, 47(22), 8785-8795. doi:10.1021/ie8005676Lee, C.-Y., Leung, J. Y.-T., & Yu, G. (2006). Two Machine Scheduling under Disruptions with Transportation Considerations. Journal of Scheduling, 9(1), 35-48. doi:10.1007/s10951-006-5592-7Li, Z., & Ierapetritou, M. (2008). Process scheduling under uncertainty: Review and challenges. Computers & Chemical Engineering, 32(4-5), 715-727. doi:10.1016/j.compchemeng.2007.03.001Liao, C. J., & Chen, W. J. (2004). Scheduling under machine breakdown in a continuous process industry. Computers & Operations Research, 31(3), 415-428. doi:10.1016/s0305-0548(02)00224-1Mehta, S. V. (1999). Predictable scheduling of a single machine subject to breakdowns. International Journal of Computer Integrated Manufacturing, 12(1), 15-38. doi:10.1080/095119299130443MUHLEMANN, A. P., LOCKETT, A. G., & FARN, C.-K. (1982). Job shop scheduling heuristics and frequency of scheduling. International Journal of Production Research, 20(2), 227-241. doi:10.1080/00207548208947763Nawaz, M., Enscore, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91-95. doi:10.1016/0305-0483(83)90088-9O’Donovan, R., Uzsoy, R., & McKay, K. N. (1999). Predictable scheduling of a single machine with breakdowns and sensitive jobs. International Journal of Production Research, 37(18), 4217-4233. doi:10.1080/002075499189745Özlen, M., & Azizoğlu, M. (2009). Generating all efficient solutions of a rescheduling problem on unrelated parallel machines. International Journal of Production Research, 47(19), 5245-5270. doi:10.1080/00207540802043998Pfeiffer, A., Kádár, B., & Monostori, L. (2007). Stability-oriented evaluation of rescheduling strategies, by using simulation. Computers in Industry, 58(7), 630-643. doi:10.1016/j.compind.2007.05.009Pierreval, H., & Durieux-Paris, S. (2007). Robust simulation with a base environmental scenario. European Journal of Operational Research, 182(2), 783-793. doi:10.1016/j.ejor.2006.07.045Damodaran, P., Hirani, N. S., & Gallego, M. C. V. (2009). Scheduling identical parallel batch processing machines to minimise makespan using genetic algorithms. European J. of Industrial Engineering, 3(2), 187. doi:10.1504/ejie.2009.023605Qi, X., Bard, J. F., & Yu, G. (2006). Disruption management for machine scheduling: The case of SPT schedules. International Journal of Production Economics, 103(1), 166-184. doi:10.1016/j.ijpe.2005.05.021Rangsaritratsamee, R., Ferrell, W. G., & Kurz, M. B. (2004). Dynamic rescheduling that simultaneously considers efficiency and stability. Computers & Industrial Engineering, 46(1), 1-15. doi:10.1016/j.cie.2003.09.007Ruiz, R., & Stützle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033-2049. doi:10.1016/j.ejor.2005.12.009Sabuncuoglu, I., & Goren, S. (2009). Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research. International Journal of Computer Integrated Manufacturing, 22(2), 138-157. doi:10.1080/09511920802209033Sabuncuoglu, I., & Kizilisik, O. B. (2003). Reactive scheduling in a dynamic and stochastic FMS environment. International Journal of Production Research, 41(17), 4211-4231. doi:10.1080/0020754031000149202Salveson, M. E. (1952). On a Quantitative Method in Production Planning and Scheduling. Econometrica, 20(4), 554. doi:10.2307/1907643Samarghandi, H., & ElMekkawy, T. Y. (2011). An efficient hybrid algorithm for the two-machine no-wait flow shop problem with separable setup times and single server. European J. of Industrial Engineering, 5(2), 111. doi:10.1504/ejie.2011.039869Subramaniam *, V., Raheja, A. S., & Rama Bhupal Reddy, K. (2005). Reactive repair tool for job shop schedules. International Journal of Production Research, 43(1), 1-23. doi:10.1080/0020754042000270412Taillard, E. (1990). Some efficient heuristic methods for the flow shop sequencing problem. European Journal of Operational Research, 47(1), 65-74. doi:10.1016/0377-2217(90)90090-xTaillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64(2), 278-285. doi:10.1016/0377-2217(93)90182-mValente, J. M. S., & Schaller, J. E. (2010). Improved heuristics for the single machine scheduling problem with linear early and quadratic tardy penalties. European J. of Industrial Engineering, 4(1), 99. doi:10.1504/ejie.2010.029572Vallada, E., & Ruiz, R. (2010). Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem☆. Omega, 38(1-2), 57-67. doi:10.1016/j.omega.2009.04.002Vieira, G. E., Herrmann, J. W., & Lin, E. (2000). Predicting the performance of rescheduling strategies for parallel machine systems. Journal of Manufacturing Systems, 19(4), 256-266. doi:10.1016/s0278-6125(01)80005-4Vieira, G. E., Herrmann, J. W., & Lin, E. (2003). Journal of Scheduling, 6(1), 39-62. doi:10.1023/a:1022235519958Yang, J., & Yu, G. (2002). Journal of Combinatorial Optimization, 6(1), 17-33. doi:10.1023/a:1013333232691Zandieh, M., & Gholami, M. (2009). An immune algorithm for scheduling a hybrid flow shop with sequence-dependent setup times and machines with random breakdowns. International Journal of Production Research, 47(24), 6999-7027. doi:10.1080/0020754080240063
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