518 research outputs found
Characterisation of road bumps using smartphones
Introduction: Speed bumps are used as the main means of controlling vehicle speeds all over the world. It is not too infrequent, especially in the emerging economies, to have unmarked bumps that can be perilous for the passengers. Fortuitously, the roadways and mobile phone networks have grown simultaneously in emerging economies. This paper demonstrates the capability of smartphones placed inside the vehicles in characterisation of road bumps. The smart mobile phones have accelerometers and position sensors that can be useful for autonomous monitoring roads. This can empower the user community in monitoring of roads. However, the capability of the smartphone in discerning different types of speed bumps while travelling in heterogeneous vehicle types needs to be examined. Methods: A range of road vehicles is mathematically modelled as mass, spring, and damper systems. The mathematical model of the vehicle is excited with parameters analogous to some common speed bumps and its acceleration response is calculated. The accelerometer of a smartphone is validated by comparing it with high precision accelerometers. The acceleration response of the phone while passing over the corresponding road bumps, which was used in the model earlier, is recorded using an Android based application. The experiment is repeated for different classes of vehicles. Filters have been used to reduce noise in the signals. A time averaging technique has been employed to compress the collected data.Results and conclusions: The acceleration signals have been digitally processed to capture road bumps. The importance of using a mathematical model to understand the acceleration response of a vehicle has been established. Also, the use of pass filters to extract the signal of concern from the noisy data has been exhibited. The ability of the technique to discern different types of speed bumps while travelling in a variety of vehicle types has been demonstrated. This investigation demonstrates the potential to automatically monitor the condition of roadways obviating costly manual inspections. As smartphones are ubiquitous, the methodology has the potential to empower the user community in the maintenance of infrastructure
Impact of shortened crop rotation of oilseed rape on soil and rhizosphere microbial diversity in relation to yield decline
Oilseed rape (OSR) grown in monoculture shows a decline in yield relative to virgin OSR of up to 25%, but the mechanisms responsible are unknown. A long term field experiment of OSR grown in a range of rotations with wheat was used to determine whether shifts in fungal and bacterial populations of the rhizosphere and bulk soil were associated with the development of OSR yield decline. The communities of fungi and bacteria in the rhizosphere and bulk soil from the field experiment were profiled using terminal restriction fragment length polymorphism (TRFLP) and sequencing of cloned internal transcribed spacer regions and 16S rRNA genes, respectively. OSR cropping frequency had no effect on rhizosphere bacterial communities. However, the rhizosphere fungal communities from continuously grown OSR were significantly different to those from other rotations. This was due primarily to an increase in abundance of two fungi which showed 100% and 95% DNA identity to the plant pathogens Olpidium brassicae and Pyrenochaeta lycopersici, respectively. Real-time PCR confirmed that there was significantly more of these fungi in the continuously grown OSR than the other rotations. These two fungi were isolated from the field and used to inoculate OSR and Brassica oleracea grown under controlled conditions in a glasshouse to determine their effect on yield. At high doses, Olpidium brassicae reduced top growth and root biomass in seedlings and reduced branching and subsequent pod and seed production. Pyrenochaeta sp. formed lesions on the roots of seedlings, and at high doses delayed flowering and had a negative impact on seed quantity and quality
Plasmodium vivax but not Plasmodium falciparum blood-stage infection in humans is associated with the expansion of a CD8+ T cell population with cytotoxic potential
P. vivax and P. falciparum parasites display different tropism for host cells and induce very different clinical symptoms and pathology, suggesting that the immune responses required for protection may differ between these two species. However, no study has qualitatively compared the immune responses to P. falciparum or P. vivax in humans following primary exposure and infection. Here, we show that the two species differ in terms of the cellular immune responses elicited following primary infection. Specifically, P. vivax induced the expansion of a subset of CD8+ T cells expressing the activation marker CD38, whereas P. falciparum induced the expansion of CD38+ CD4+ T cells. The CD38+ CD8+ T cell population that expanded following P. vivax infection displayed greater cytotoxic potential compared to CD38- CD8+ T cells, and compared to CD38+ CD8+ T cells circulating during P. falciparum infection. We hypothesize that P. vivax infection leads to a stronger CD38+ CD8+ T cell activation because of its preferred tropism for MHC-I-expressing reticulocytes that, unlike mature red blood cells, can present antigen directly to CD8+ T cells. This study provides the first line of evidence to suggest an effector role for CD8+ T cells in P. vivax blood-stage immunity. It is also the first report of species-specific differences in the subset of T cells that are expanded following primary Plasmodium infection, suggesting that malaria vaccine development may require optimization according to the target parasite
Arterial line pressure control enhanced extracorporeal blood flow prescription in hemodialysis patients
<p>Abstract</p> <p>Background</p> <p>In hemodialysis, extracorporeal blood flow (Qb) recommendation is 300â500 mL/min. To achieve the best Qb, we based our prescription on dynamic arterial line pressure (DALP).</p> <p>Methods</p> <p>This prospective study included 72 patients with catheter Group 1 (G1), 1877 treatments and 35 arterio-venous (AV) fistulae Group 2 (G2), 1868 treatments. The dialysis staff was trained to prescribe Qb sufficient to obtain DALP between -200 to -250 mmHg. We measured ionic clearance (IK: mL/min), access recirculation, DALP (mmHg) and Qb (mL/min). Six prescription zones were identified: from an optimal A zone (Qb > 400, DALP -200 to -250) to zones with lower Qb E (Qb < 300, DALP -200 to -250) and F (Qb < 300, DALP > -199).</p> <p>Results</p> <p>Treatments distribution in A was 695 (37%) in G1 vs. 704 (37.7%) in G2 (<it>P </it>= 0.7). In B 150 (8%) in G1 vs. 458 (24.5%) in G2 (<it>P </it>< 0.0001). Recirculation in A was 10.0% (Inter quartile rank, IQR 6.5, 14.2) in G1 vs. 9.8% (IQR 7.5, 14.1) in G2 (<it>P </it>= 0.62). IK in A was 214 ± 34 (G1) vs. 213 ± 35 (G2) (<it>P </it>= 0.65). IK Anova between G2 zones was: A vs. C and D (<it>P </it>< 0.000001). Staff prescription adherence was 81.3% (G1) vs. 84.1% (G2) (<it>P </it>= 0.02).</p> <p>Conclusion</p> <p>In conclusion, an optimal Qb can de prescribed with DALP of -200 mmHg. Staff adherence to DLAP treatment prescription could be reached up to 81.3% in catheters and 84.1% in AV fistulae.</p
Routine pattern discovery and anomaly detection in individual travel behavior
Discovering patterns and detecting anomalies in individual travel behavior is
a crucial problem in both research and practice. In this paper, we address this
problem by building a probabilistic framework to model individual
spatiotemporal travel behavior data (e.g., trip records and trajectory data).
We develop a two-dimensional latent Dirichlet allocation (LDA) model to
characterize the generative mechanism of spatiotemporal trip records of each
traveler. This model introduces two separate factor matrices for the spatial
dimension and the temporal dimension, respectively, and use a two-dimensional
core structure at the individual level to effectively model the joint
interactions and complex dependencies. This model can efficiently summarize
travel behavior patterns on both spatial and temporal dimensions from very
sparse trip sequences in an unsupervised way. In this way, complex travel
behavior can be modeled as a mixture of representative and interpretable
spatiotemporal patterns. By applying the trained model on future/unseen
spatiotemporal records of a traveler, we can detect her behavior anomalies by
scoring those observations using perplexity. We demonstrate the effectiveness
of the proposed modeling framework on a real-world license plate recognition
(LPR) data set. The results confirm the advantage of statistical learning
methods in modeling sparse individual travel behavior data. This type of
pattern discovery and anomaly detection applications can provide useful
insights for traffic monitoring, law enforcement, and individual travel
behavior profiling
Turnip yellow mosaic virus in Chinese cabbage in Spain: Commercial seed transmission and molecular characterization
[EN] Seed transmission of Turnip yellow mosaic virus (TYMV, genus Tymovirus) was evaluated in the whole seeds and seedlings that emerged from three commercial Chinese cabbage (Brassica pekinensis) seed batches. Seedlings in the cotyledon stage and adult plants were assayed for TYMV by DAS-ELISA and confirmed by RT-PCR. The proportion of whole seeds infected with TYMV was at least 0.15 %. The seeds of the three seed batches were grown in Petri dishes, and surveyed in the cotyledon stage in trays that contained a peat:sand mixture grown in greenhouses or growth chambers, which were analysed in the cotyledon and adult stages. The seed-to-seedling transmission rate ranged from 2.5 % to 2.9 % in two different seed batches (lot-08 and lot-09, respectively). Spanish isolates derived from turnip (Sp-03) and Chinese cabbage (Sp-09 and Sp-13), collected in 2003, 2009 and 2013 in two different Spanish regions, were molecularly characterised by analysing the partial nucleotide sequences of three TYMV genome regions: partial RNA-dependent RNA polymerase (RdRp), methyltransferase (MTR) and coat protein (CP) genes. Phylogenetic analyses showed that the CP gene represented two different groups: TYMV-1 and TYMV-2. The first was subdivided into three subclades: European, Australian and Japanese. Spanish isolate Sp-03 clustered together with European TYMV group, whereas Sp-09 and Sp-13 grouped with the Japanese TYMV group, and all differed from group TYMV-2. The sequences of the three different genomic regions examined clustered into the same groups. The results suggested that Spanish isolates grouped according to the original hosts from which they were isolated. The inoculation of the Spanish TYMV isolates to four crucifer plants species (turnip, broccoli, Brunswick cabbage and radish) revealed that all the isolates infected turnip with typical symptoms, although differences were observed in other hosts.Alfaro FernĂĄndez, AO.; Serrano, A.; Tornos, T.; Cebrian Mico, MC.; CĂłrdoba-SellĂ©s, MDC.; JordĂĄ, C.; Font San Ambrosio, MI. (2016). Turnip yellow mosaic virus in Chinese cabbage in Spain: Commercial seed transmission and molecular characterization. EUROPEAN JOURNAL OF PLANT PATHOLOGY. 146(2):433-442. doi:10.1007/s10658-016-0929-3S4334421462Assis Filho, M., & Sherwood, J. L. (2000). Evaluation of seed transmission of Turnip yellow mosaic virus and Tobacco mosaic virus in Arabidopsis thaliana. Phytopathology, 90, 1233â1238.Benetti, M. P., & Kaswalder, F. (1983). Trasmisione per seme del virus del mosaico giallo rapa. Annali dell Istituto Sperimentale per la Patologia Vegetale, 8, 67â70.Blok, J., Mackenzie, A., Guy, P., & Gibbs, A. (1987). Nucleotide sequence comparisons of Turnip yellow mosaic virus isolates from Australia and Europe. Archives of Virology, 97, 283â295.Brunt, A., Crabtree, K., Dallwitz, M., Gibbs, A., Watson, L., & Zurcher, E.J. (1996). Plant Viruses Online: Descriptions and Lists from the VIDE Database. Version: 20th August 1996. URL http://biology.anu.edu.au/Groups/MES/vide/ .Campbell, R. N., Wipf-Scheibel, C., & Lecoq, H. (1996). Vector-assissted seed transmission of melon necrotic spot virus in melon. Phytopathology, 86, 1294â1298.Dreher, T. W., & Bransom, K. L. (1992). Genomic RNA sequence of Turnip yellow mosaic virus isolate TYMC, a cDNA-based clone with verified infectivity. Plant Molecular Biology, 18, 403â406.Fakhro, A., Von Bargen, S., Bandte, M., BĂŒttner, C., Franken, P., & Schwarz, D. (2011). Susceptibility of different plant species and tomato cultivars to two isolates of Pepino mosaic virus. European Journal of Plant Pathology, 129, 579â590.Gibbs, A. J., & Gower, J. C. (1960). The use of a multiple-transfer method in plant virus transmission studies: some statistical points arising in the analysis of results. Annals of Applied Biology, 48, 75â83.Hayden, C. M., Mackenzie, A. M., & Gibbs, A. J. (1998a). Virion protein sequence variation among Australian isolates of turnip yellow mosaic tymovirus. Archives of Virology, 143, 191â201.Hayden, C. M., Mackenzie, A. M., Skotnicki, M. L., & Gibbs, A. (1998b). Turnip yellow mosaic virus isolates with experimentally produced recombinant virion proteins. Journal of General Virology, 79, 395â403.Hein, A. (1984). Transmission of Turnip yellow mosaic virus through seed of Camelina sativa gold of pleasure. Journal of Plant Diseases and Protection, 91, 549â551.Herrera-VĂĄsquez, J. A., CĂłrdoba-SellĂ©s, M. C., CebriĂĄn, M. C., Alfaro-FernĂĄndez, A., & JordĂĄ, C. (2009). Seed transmission of Melon necrotic spot virus and efficacy of seed-disinfection treatments. Plant Pathology, 58, 436â452.Hull, R. (2002). Matthewsâ plant virology (4a ed.1001 pp). San Diego: Academic Press.Johansen, E., Edwards, M. C., & Hampton, R. O. (1994). Seed transmission of viruses: current perspectives. Annual Review of Phytopathology, 32, 363â386.Kirino, N., Inoue, K., Tanina, K., Yamazaki, Y., & Ohki, S. T. (2008). Turnip yellow mosaic virus isolated from Chinese cabbage in Japan. Journal of General Plant Pathology, 74, 331â334.Markham, R., & Smith, K. S. (1949). Studies on the virus of turnip yellow mosaic. Parasitology, 39, 330â342.Mathews, R. E. F. (1980). Turnip yellow mosaic virus, CMI/AAB Descriptions of plant virus No. 230 (No. 2 revised). Kew: Commonwealth Mycology Institute/Association of Applied Biologists.Mitchell, E. J., & Bond, J. M. (2005). Variation in the coat protein sequence of British isolates of Turnip yellow mosaic virus and comparison with previously published isolates. Archives of Virology, 150, 2347â2355.PagĂĄn, I., Fraile, A., FernĂĄndez-Fueyo, E., Montes, N., Alonso-Blanco, C., & GarcĂa-Arenal, F. (2010). Arabidopsis thaliana as a model for the study of plant-virus co-evolution. Philosophical Transations of the Royal Society Biological Sciences, 365, 1983â1995.Paul, H. L., Gibbs, A., & Wittman-Liebold, B. (1980). The relationships of certain Tymoviruses assessed from the amino acid composition of their coat proteins. Intervirology, 13, 99â109.Pelikanova, J. (1990). Garlic mustard a spontaneous host of TYMV. Ochrana Rostlin, 26, 17â22.ProchĂĄzkovĂĄ, Z. (1980). Host range and symptom differences between isolates of Turnip mosaic virus obtained from Sisymbrium loeselii. Biologia Plantarum, 22, 341â347.Rimmer, S. R., Shtattuck, V. I., & Buchwaldt, L. (2007). Compendium of brassica diseases (1ÂȘ EdiciĂłn ed.p. 117). USA: APS press.Rot, M. E., & Jelkman, W. (2001). Characterization and detection of several filamentous viruses of cherry: Adaptation of an alternative cloning method (DOP-PCR), and modification of an RNA extraction protocol. European Journal of Plant Pathology, 107, 411â420.Sabanadzovic, S., Abou-Ghanem, N., Castellano, M. A., Digiaero, M., & Martelli, G. P. (2000). Grapevine fleck virus-like in Vitis. Archives of Virology, 145, 553â565.Ć pack, J., & KubelkovĂĄ, D. (2000). Serological variability among European isolates of Radish mosaic virus. Plant Pathology, 49, 295â301.Ć pack, J., KubelkovĂĄ, D., & Hnilicka, E. (1993). Seed transmission of Turnip yellow mosaic virus in winter turnip and winter oilseed rapes. Annals of Applied Biology, 123, 33â35.Stobbs, L. W., Cerkauskas, R. F., Lowery, T., & VanDriel, L. (1998). Occurrence of Turnip yellow mosaic virus on oriental cruciferours vegetables in Southern Ontario, Canada. Plant Disease, 82, 351.Tamura, K., Peterson, D., Peterson, N., Stecher, G., Nei, M., & Kumar, S. (2011). MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Molecular Biology and Evolution, 28, 2731â2739
Observation of associated near-side and away-side long-range correlations in âsNN=5.02ââTeV proton-lead collisions with the ATLAS detector
Two-particle correlations in relative azimuthal angle (ÎÏ) and pseudorapidity (Îη) are measured in âsNN=5.02ââTeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1ââÎŒb-1 of data as a function of transverse momentum (pT) and the transverse energy (ÎŁETPb) 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<|Îη|<5) ânear-sideâ (ÎÏâŒ0) correlation that grows rapidly with increasing ÎŁETPb. A long-range âaway-sideâ (ÎÏâŒÏ) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ÎŁETPb, is found to match the near-side correlation in magnitude, shape (in Îη and ÎÏ) and ÎŁETPb dependence. The resultant ÎÏ correlation is approximately symmetric about Ï/2, and is consistent with a dominant cosâĄ2ÎÏ modulation for all ÎŁETPb ranges and particle pT
Measurement of the cross-section of high transverse momentum vector bosons reconstructed as single jets and studies of jet substructure in pp collisions at âs = 7 TeV with the ATLAS detector
This paper presents a measurement of the cross-section for high transverse momentum W and Z bosons produced in pp collisions and decaying to all-hadronic final states. The data used in the analysis were recorded by the ATLAS detector at the CERN Large Hadron Collider at a centre-of-mass energy of âs = 7 TeV;{\rm Te}{\rm V}4.6\;{\rm f}{{{\rm b}}^{-1}}{{p}_{{\rm T}}}\gt 320\;{\rm Ge}{\rm V}|\eta |\lt 1.9{{\sigma }_{W+Z}}=8.5\pm 1.7$ pb and is compared to next-to-leading-order calculations. The selected events are further used to study jet grooming techniques
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