A gene signature in histologically normal surgical margins is predictive of oral carcinoma recurrence

Background: Oral Squamous Cell Carcinoma (OSCC) is a major cause of cancer death worldwide, which is mainly due to recurrence leading to treatment failure and patient death. Histological status of surgical margins is a currently available assessment for recurrence risk in OSCC; however histological status does not predict recurrence, even in patients with histologically negative margins. Therefore, molecular analysis of histologically normal resection margins and the corresponding OSCC may aid in identifying a gene signature predictive of recurrence.Methods: We used a meta-analysis of 199 samples (OSCCs and normal oral tissues) from five public microarray datasets, in addition to our microarray analysis of 96 OSCCs and histologically normal margins from 24 patients, to train a gene signature for recurrence. Validation was performed by quantitative real-time PCR using 136 samples from an independent cohort of 30 patients.Results: We identified 138 significantly over-expressed genes (> 2-fold, false discovery rate of 0.01) in OSCC. By penalized likelihood Cox regression, we identified a 4-gene signature with prognostic value for recurrence in our training set. This signature comprised the invasion-related genes MMP1, COL4A1, P4HA2, and THBS2. Overexpression of this 4-gene signature in histologically normal margins was associated with recurrence in our training cohort (p = 0.0003, logrank test) and in our independent validation cohort (p = 0.04, HR = 6.8, logrank test).Conclusion: Gene expression alterations occur in histologically normal margins in OSCC. Over-expression of the 4-gene signature in histologically normal surgical margins was validated and highly predictive of recurrence in an independent patient cohort. Our findings may be applied to develop a molecular test, which would be clinically useful to help predict which patients are at a higher risk of local recurrence.

Avaliação de fatores de ambiente e estimativas de parâmetros genéticos para a característica dias para o parto na raça Nelore

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Likelihood approximations and discrete models for tied survival data

Ties among event times are often recorded in survival studies. For example, in a two week laboratory study where event times are measured in days, ties are very likely to occur. The proportional hazards model might be used in this setting using an approximated partial likelihood function. This approximation works well when the number of ties is small. on the other hand, discrete regression models are suggested when the data are heavily tied. However, in many situations it is not clear which approach should be used in practice. In this work, empirical guidelines based on Monte Carlo simulations are provided. These recommendations are based on a measure of the amount of tied data present and the mean square error. An example illustrates the proposed criterion.

Genetic parameters of milk, fat, and protein yields in the first three lactations, using an animal model and restricted maximum likelihood

Milk, fat, and protein yields of Holstein cows from the States of New York and California in the United States were used to estimate (co)variances among yields in the first three lactations, using an animal model and a derivative-free restricted maximum likelihood (REML) algorithm, and to verify if yields in different lactations are the same trait. The data were split in 20 samples, 10 from each state, with means of 5463 and 5543 cows per sample from California and New York. Mean heritability estimates for milk, fat, and protein yields for California data were, respectively, 0.34, 0.35, and 0.40 for first; 0.31, 0.33, and 0.39 for second; and 0.28, 0.31, and 0.37 for third lactations. For New York data, estimates were 0.35, 0.40, and 0.34 for first; 0.34, 0.44, and 0.38 for second; and 0.32, 0.43, and 0.38 for third lactations. Means of estimates of genetic correlations between first and second, first and third, and second and third lactations for California data were 0.86, 0.77, and 0.96 for milk; 0.89, 0.84, and 0.97 for fat; and 0.90, 0.84, and 0.97 for protein yields. Mean estimates for New York data were 0.87, 0.81, and 0.97 for milk; 0.91, 0.86, and 0.98 for fat; and 0.88, 0.82, and 0.98 for protein yields. Environmental correlations varied from 0.30 to 0.50 and were larger between second and third lactations. Phenotypic correlations were similar for both states and varied from 0.52 to 0.66 for milk, fat and protein yields. These estimates are consistent with previous estimates obtained with animal models. Yields in different lactations are not statistically the same trait but for selection programs such yields can be modelled as the same trait because of the high genetic correlations.

Ovarian follicle diameter at timed insemination and estrous response influence likelihood of ovulation and pregnancy after estrous synchronization with progesterone or progestin-based protocols in suckled Bos indicus cows

The objectives of the present study were to evaluate factors associated with estrous synchronization responses and pregnancy per insemination (P/AI) in Bos indicus beef cows submitted to progesterone-based fixed-time artificial insemination (FTAI) protocols. A total of 2388 cows (1869 Nellore and 519 crossbred Nellore x Angus) from 10 commercial farms were evaluated to determine the relationships among breed, body condition score (BCS) on the first day of the FTAI protocol, the occurrence of estrus between progesterone device removal and FTAI and diameter of largest ovarian follicle (LF) at FTAI on estrous synchronization responses and P/AI. Cows (n=412 primiparous; 1976 multiparous) received an intravaginal device containing progesterone or an ear implant containing norgestomet (a progestin), and an injection of estradiol at the beginning of the estrous synchronization protocol. Body condition was scored using a 1-5 scale on the first day of the FTAI protocol and at 30-60 days postpartum. Females received 300IU of equine chorionic gonadotropin (eCG) and PGF(2 alpha) on the day the progesterone device/implant was removed and were inseminated 48-60h later. At insemination, cows (n=2388) were submitted to an ultrasonographic exam to determine the diameter of the LF. Follicles were classified into four categories based on mean and standard deviation (SD) of the LF (LF1 = two SD below the mean; LF2 = mean minus one SD; LF3 = mean plus one SD; LF4 = two SD above the mean). Ovulation rate was determined in a subset of cows (n=813) by three consecutive ultrasonographic exams: (1) at time of progesterone device/implant removal, (2) at time of FTAI and (3) 48 h after FTAI. Ovulation was defined as the disappearance of a large follicle (>= 8.0 mm) that was previously recorded. Estrus was determined in a subset of the cows (n = 445) by the activation of a detection of estrous patch placed on the tail head on the day of progesterone device/implant removal. Pregnancy was diagnosed 30 days after FTAI. Pregnancy was influenced (P = 0.001) by follicle diameter [LF1 = 27.5% (81/295), LF2 = 46.6% (328/705), LF3 = 57.9% (647/1118), LF4 = 63.3% (171/270)] and the occurrence of estrus [estrus = 67.7% (174/257) and no estrus = 36.2% (68/188)]. Follicle diameter at FTAI influenced ovulation rate [LF1 = 42.5% (34/80), LF2 = 73.9% (161/218), LF3 = 95.8% (407/425), LF4 = 97.8% (88/90)], the occurrence of estrus [LF1 = 54.8% (51/93), LF2 = 33.6% (43/128), LF3 = 68.9% (126/183), LF4 = 90.2% (37/41)] and P/AI among cows that had ovulations [LF1 =32.4% (11/34), LF2 = 50.3% (81/161), LF3 = 60.0% (244/407), LF4 = 68.2% (60/88)]. Improving estrous responses between progesterone device withdrawal and FTAI and increasing the diameter of the LF at FTAI may be important aspects to achieve improved estrous synchronization responses and P/AI following progesterone/progestin and estradiol based FTAI protocols in suckled Bos indicus cows. (C) 2010 Elsevier B.V. All rights reserved.

Mitigating systematic errors for single frequency GPS receivers employing a penalized least squares methodology

Among the positioning systems that compose GNSS (Global Navigation Satellite System), GPS has the capability of providing low, medium and high precision positioning data. However, GPS observables may be subject to many different types of errors. These systematic errors can degrade the accuracy of the positioning provided by GPS. These errors are mainly related to GPS satellite orbits, multipath, and atmospheric effects. In order to mitigate these errors, a semiparametric model and the penalized least squares technique were employed in this study. This is similar to changing the stochastical model, in which error functions are incorporated and the results are similar to those in which the functional model is changed instead. Using this method, it was shown that ambiguities and the estimation of station coordinates were more reliable and accurate than when employing a conventional least squares methodology.

String-averaging expectation-maximization for maximum likelihood estimation in emission tomography

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)