Bayesian latent time joint mixed-effects model of progression in the Alzheimer's Disease Neuroimaging Initiative.

IntroductionWe characterize long-term disease dynamics from cognitively healthy to dementia using data from the Alzheimer's Disease Neuroimaging Initiative.MethodsWe apply a latent time joint mixed-effects model to 16 cognitive, functional, biomarker, and imaging outcomes in Alzheimer's Disease Neuroimaging Initiative. Markov chain Monte Carlo methods are used for estimation and inference.ResultsWe find good concordance between latent time and diagnosis. Change in amyloid positron emission tomography shows a moderate correlation with change in cerebrospinal fluid tau (ρ = 0.310) and phosphorylated tau (ρ = 0.294) and weaker correlation with amyloid-β 42 (ρ = 0.176). In comparison to amyloid positron emission tomography, change in volumetric magnetic resonance imaging summaries is more strongly correlated with cognitive measures (e.g., ρ = 0.731 for ventricles and Alzheimer's Disease Assessment Scale). The average disease trends are consistent with the amyloid cascade hypothesis.DiscussionThe latent time joint mixed-effects model can (1) uncover long-term disease trends; (2) estimate the sequence of pathological abnormalities; and (3) provide subject-specific prognostic estimates of the time until onset of symptoms

Predicting the course of Alzheimer's progression.

Alzheimer's disease is the most common neurodegenerative disease and is characterized by the accumulation of amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological features precede cognitive impairment and Alzheimer's dementia by many years. To better understand and predict the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fluid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive impairment, or dementia) from predictions of continuous markers based on the first-stage model. The combination of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize the predictive accuracy of this two-stage approach using data from the Alzheimer's Disease Neuroimaging Initiative. The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for each of the continuous outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5 years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only

Effect of drought stress on growth, proline and antioxidant enzyme activities of upland rice

Responses of eight upland rice (Oryza sativa L.) varieties subjected to different drought levels were investigated in laboratory to evaluate eight local upland rice varieties against five drought levels (0, -2, -4, -6, and -8 bars) at germination and early seedling growth stage of plant development. Data were analyzed statistically for growth parameters; shoot length, root length, and dry matter yield, and biochemical parameters; proline and antioxidant enzymes activity (catalase, superoxide dismutase and peroxidase), were measured. Experiment units were arranged factorial completely randomized design with four replications. The drought-tolerant variety, Pulot Wangi tolerated PEG at the highest drought level (-8 bar) and showed no significantly difference relation to control. However, drought-sensitive variety, Kusam was markedly affected even at the lowest drought level used. Concomitantly, the activity of antioxidant enzymes catalase, peroxidase and superoxide dismutase in the drought-tolerant varieties increased markedly during drought stress, while decreased by drought stress in the drought sensitive variety. Consequently, this led to a marked difference in the accumulation of proline in the upland rice varieties. It may be concluded that the activities of antioxidant enzymes and proline accumulation were associated with the dry mass production and consequently with the drought tolerance of the upland rice varieties

XRCC2 R188H (rs3218536), XRCC3 T241M (rs861539) and R243H (rs77381814) single nucleotide polymorphisms in cervical cancer risk

Human Papillomavirus (HPV) is the main cause of cervical cancer and its precursor lesions. Transformation may be induced by several mechanisms, including oncogene activation and genome instability. Individual differences in DNA damage recognition and repair have been hypothesized to influence cervical cancer risk. The aim of this study was to evaluate whether the double strand break gene polymorphisms XRCC2 R188H G>A (rs3218536), XRCC3 T241M C>T (rs861539) and R243H G>A (rs77381814) are associated to cervical cancer in Argentine women. A case control study consisting of 322 samples (205 cases and 117 controls) was carried out. HPV DNA detection was performed by PCR and genotyping of positive samples by EIA (enzyme immunoassay). XRCC2 and 3 polymorphisms were determined by pyrosequencing. The HPV-adjusted odds ratio (OR) of XRCC2 188 GG/AG genotypes was OR = 2.4 (CI = 1.1-4.9, p = 0.02) for cervical cancer. In contrast, there was no increased risk for cervical cancer with XRCC3 241 TT/CC genotypes (OR = 0.48; CI = 0.2-1; p = 0.1) or XRCC3 241 CT/CC (OR = 0.87; CI = 0.52-1.4; p = 0.6). Regarding XRCC3 R243H, the G allele was almost fixed in the population studied. In conclusion, although the sample size was modest, the present data indicate a statistical association between cervical cancer and XRCC2 R188H polymorphism. Future studies are needed to confirm these findings.Fil: Perez, Luis Orlando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; ArgentinaFil: Crivaro, Andrea Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; ArgentinaFil: Barbisan, Gisela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; ArgentinaFil: Poleri, Lucía Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; ArgentinaFil: Golijow, Carlos Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; Argentin

Multi-scale simulation of the nano-metric cutting process

Molecular dynamics (MD) simulation and the finite element (FE) method are two popular numerical techniques for the simulation of machining processes. The two methods have their own strengths and limitations. MD simulation can cover the phenomena occurring at nano-metric scale but is limited by the computational cost and capacity, whilst the FE method is suitable for modelling meso- to macro-scale machining and for simulating macro-parameters, such as the temperature in a cutting zone, the stress/strain distribution and cutting forces, etc. With the successful application of multi-scale simulations in many research fields, the application of simulation to the machining processes is emerging, particularly in relation to machined surface generation and integrity formation, i.e. the machined surface roughness, residual stress, micro-hardness, microstructure and fatigue. Based on the quasi-continuum (QC) method, the multi-scale simulation of nano-metric cutting has been proposed. Cutting simulations are performed on single-crystal aluminium to investigate the chip formation, generation and propagation of the material dislocation during the cutting process. In addition, the effect of the tool rake angle on the cutting force and internal stress under the workpiece surface is investigated: The cutting force and internal stress in the workpiece material decrease with the increase of the rake angle. Finally, to ease multi-scale modelling and its simulation steps and to increase their speed, a computationally efficient MATLAB-based programme has been developed, which facilitates the geometrical modelling of cutting, the simulation conditions, the implementation of simulation and the analysis of results within a unified integrated virtual-simulation environment

Study of genetic variation of some eggplant cultivars through RAPD-PCR molecular markers and its relatedness to phomopsis blight disease reaction

Disease susceptibility and genetic variability in 10 eggplant genotypes were studied after inoculating Phomopsis vexans under confined field conditions. Random amplified polymorphic DNA (RAPD) markers were used to assess genetic variation and relationships among eggplant genotypes. The disease index of leaves ranged 0.208-13.79%, while fruit infection ranged 2.15-42.76%. Two varieties, Dohazari G and Laffa S, were found to be susceptible, 6 were moderately resistant, 1 was moderately susceptible, and BAU Begun-1 was resistant to P. vexans. Amplification of genomic DNA by using 3 RAPD primers produced 20 bands: 14 (70%) were polymorphic and 6 (30%) were monomorphic. The highest intra-variety similarity indices values were found in ISD 006, Ishurdi L, Jessore L, and BAU Begun-1 (100%), while the lowest was in Dohazari G (90%). The lowest genetic distance (0.0513) and the highest genetic identity (0.9500) were observed between the ISD 006 and Ishurdi L combinations. A comparatively higher genetic distance (0.3724) and the lowest genetic identity (0.6891) were observed between the ISD 006 and Dohazari G combinations. A dendogram was constructed based on Nei’s genetic distance, which produced 2 main clusters of the genotypes - Cluster I: ISD 006, Ishurdi L, Marich begun L, BAU Begun-1, Marich begun S, and Chega and Cluster 2: Laffa S, Dohazari G, Jessore L, and Singhnath. Genetic variation and its relationship with disease susceptibility were assessed using RAPD markers, to develop disease-resistant varieties and improve eggplant crops

Identification of genotypes resistant to blast, bacterial leaf blight, sheath blight and tungro and efficacy of seed treating fungicides against blast disease of rice

A total of 35 inbred and 13 hybrid varieties including susceptible checks were screened against the 4 major diseases of rice (blast, bacterial leaf blight, sheath blight and tungro) as well as experiments on management of blast were conducted in the rain-fed and irrigated rice ecosystems during 1999 to 2003. Results showed that none of the tested high yielding varieties (HYV) were resistant to blast, while the hybrids, sonarbangla1, aalock6201, KRH2, IR71101H, IR68877H and IR76901H, and inbreds BR12, BR15 and IR72 were moderately resistant in the irrigated rice ecosystem. On the other hand, all the varieties tested against bacterial leaf blight (BLB) and sheath blight (ShB) were moderately susceptible in the same ecosystem. The inbred varieties BR22, BR25, BRRI dhan31, BRRI dhan32, BRRI dhan33, BRRI dhan34, BRRI dhan38 and BRRI dhan39 demonstrated moderately resistant reactions but all the hybrids were moderately susceptible to BLB in the rain-fed ecosystem. Eight inbreds, predominantly, BR22, BR23, BRRI dhan27, BRRI dhan31, BRRI dhan32, BRRI dhan37, BRRI dhan38 and BRRI dhan40 were moderately resistant to tungro disease. Among the 3 fungicides tested in 2 different trials, adivistin and haydazim 50 WP (carbendazim) at the rate of 0.4% were more effective as seed-treating fungicides for the control of rice blast disease

Analysis of simple sequence repeat markers linked with blast disease resistance genes in a segregating population of rice (Oryza sativa).

Among 120 simple sequence repeat (SSR) markers, 23 polymorphic markers were used to identify the segregation ratio in 320 individuals of an F(2) rice population derived from Pongsu Seribu 2, a resistant variety, and Mahsuri, a susceptible rice cultivar. For phenotypic study, the most virulent blast (Magnaporthe oryzae) pathotype, P7.2, was used in screening of F(2) population in order to understand the inheritance of blast resistance as well as linkage with SSR markers. Only 11 markers showed a good fit to the expected segregation ratio (1:2:1) for the single gene model (d.f. = 1.0, P < 0.05) in chi-square (χ(2)) analyses. In the phenotypic data analysis, the F(2) population segregated in a 3:1 (R:S) ratio for resistant and susceptible plants, respectively. Therefore, resistance to blast pathotype P7.2 in Pongsu Seribu 2 is most likely controlled by a single nuclear gene. The plants from F(2) lines that showed resistance to blast pathotype P7.2 were linked to six alleles of SSR markers, RM168 (116 bp), RM8225 (221 bp), RM1233 (175 bp), RM6836 (240 bp), RM5961 (129 bp), and RM413 (79 bp). These diagnostic markers could be used in marker assisted selection programs to develop a durable blast resistant variety