Automated 3D Lumbar Intervertebral Disc Segmentation from MRI Data Sets

This paper proposed an automated 3D lumbar intervertebral disc (IVD) segmentation strategy from MRI data. Starting from two user supplied landmarks, the geometrical parameters of all lumbar vertebral bodies and intervertebral discs are automatically extracted from a mid-sagittal slice using a graphical model based approach. After that, a three-dimensional (3D) variable-radius soft tube model of the lumbar spine column is built to guide the 3D disc segmentation. The disc segmentation is achieved as a multi-kernel diffeomorphic registration between a 3D template of the disc and the observed MRI data. Experiments on 15 patient data sets showed the robustness and the accuracy of the proposed algorithm.

Variance estimation in ranked set sampling using a concomitant variable

We propose a nonparametric variance estimator when ranked set sampling (RSS) and judgment post stratification (JPS) are applied by measuring a concomitant variable. Our proposed estimator is obtained by conditioning on observed concomitant values and using nonparametric kernel regression.

Automated 3D Lumbar Intervertebral Disc Segmentation from MRI Data Sets

This paper proposed an automated three-dimensional (3D) lumbar intervertebral disc (IVD) segmentation strategy from Magnetic Resonance Imaging (MRI) data. Starting from two user supplied landmarks, the geometrical parameters of all lumbar vertebral bodies and intervertebral discs are automatically extracted from a mid-sagittal slice using a graphical model based template matching approach. Based on the estimated two-dimensional (2D) geometrical parameters, a 3D variable-radius soft tube model of the lumbar spine column is built by model fitting to the 3D data volume. Taking the geometrical information from the 3D lumbar spine column as constraints and segmentation initialization, the disc segmentation is achieved by a multi-kernel diffeomorphic registration between a 3D template of the disc and the observed MRI data. Experiments on 15 patient data sets showed the robustness and the accuracy of the proposed algorithm.

Extracting more out of relocation data: building movement models as mixtures of random walks

We present a framework for fitting multiple random walks to animal movement paths consisting of ordered sets of step lengths and turning angles. Each step and turn is assigned to one of a number of random walks, each characteristic of a different behavioral state. Behavioral state assignments may be inferred purely from movement data or may include the habitat type in which the animals are located. Switching between different behavioral states may be modeled explicitly using a state transition matrix estimated directly from data, or switching probabilities may take into account the proximity of animals to landscape features. Model fitting is undertaken within a Bayesian framework using the WinBUGS software. These methods allow for identification of different movement states using several properties of observed paths and lead naturally to the formulation of movement models. Analysis of relocation data from elk released in east-central Ontario, Canada, suggests a biphasic movement behavior: elk are either in an "encamped" state in which step lengths are small and turning angles are high, or in an "exploratory" state, in which daily step lengths are several kilometers and turning angles are small. Animals encamp in open habitat (agricultural fields and opened forest), but the exploratory state is not associated with any particular habitat type.

Income Inequality in the 1990s: Re-Forging a Lost Relationship?

Using data from March Current Population Surveys we find gains from economic growth over the 1990s business cycle (1989-2000) were more equitably distributed than over the 1980s business cycle (1979-1989) using summary inequality measures as well as kernel density estimations. The entire distribution of household size-adjusted income moved upwards in the 1990s with profound improvements for African Americans, single mothers and those living in households receiving welfare. Most gains occurred over the growth period 1993-2000. Improvements in average income and income inequity over the latter period are reminiscent of gains seen in the first three decades after World War II.

A Bootstrap-Regression Procedure to Capture Unit Specific Effects in Data Envelopment Analysis

The Data Envelopment Analysis (DEA) efficiency score obtained for an individual firm is a point estimate without any confidence interval around it. In recent years, researchers have resorted to bootstrapping in order to generate empirical distributions of efficiency scores. This procedure assumes that all firms have the same probability of getting an efficiency score from any specified interval within the [0,1] range. We propose a bootstrap procedure that empirically generates the conditional distribution of efficiency for each individual firm given systematic factors that influence its efficiency. Instead of resampling directly from the pooled DEA scores, we first regress these scores on a set of explanatory variables not included at the DEA stage and bootstrap the residuals from this regression. These pseudo-efficiency scores incorporate the systematic effects of unit-specific factors along with the contribution of the randomly drawn residual. Data from the U.S. airline industry are utilized in an empirical application.

Exploration of genetic susceptibility to pancreatic cancer: A GWAS approach

Pancreatic cancer is the 4th most common cause for cancer death in the United States, accompanied by less than 5% five-year survival rate based on current treatments, particularly because it is usually detected at a late stage. Identifying a high-risk population to launch an effective preventive strategy and intervention to control this highly lethal disease is desperately needed. The genetic etiology of pancreatic cancer has not been well profiled. We hypothesized that unidentified genetic variants by previous genome-wide association study (GWAS) for pancreatic cancer, due to stringent statistical threshold or missing interaction analysis, may be unveiled using alternative approaches. To achieve this aim, we explored genetic susceptibility to pancreatic cancer in terms of marginal associations of pathway and genes, as well as their interactions with risk factors. We conducted pathway- and gene-based analysis using GWAS data from 3141 pancreatic cancer patients and 3367 controls with European ancestry. Using the gene set ridge regression in association studies (GRASS) method, we analyzed 197 pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Using the logistic kernel machine (LKM) test, we analyzed 17906 genes defined by University of California Santa Cruz (UCSC) database. Using the likelihood ratio test (LRT) in a logistic regression model, we analyzed 177 pathways and 17906 genes for interactions with risk factors in 2028 pancreatic cancer patients and 2109 controls with European ancestry. After adjusting for multiple comparisons, six pathways were marginally associated with risk of pancreatic cancer ( P < 0.00025): Fc epsilon RI signaling, maturity onset diabetes of the young, neuroactive ligand-receptor interaction, long-term depression (Ps < 0.0002), and the olfactory transduction and vascular smooth muscle contraction pathways (P = 0.0002; Nine genes were marginally associated with pancreatic cancer risk (P < 2.62 × 10−5), including five reported genes (ABO, HNF1A, CLPTM1L, SHH and MYC), as well as four novel genes (OR13C4, OR 13C3, KCNA6 and HNF4 G); three pathways significantly interacted with risk factors on modifying the risk of pancreatic cancer (P < 2.82 × 10−4): chemokine signaling pathway with obesity ( P < 1.43 × 10−4), calcium signaling pathway (P < 2.27 × 10−4) and MAPK signaling pathway with diabetes (P < 2.77 × 10−4). However, none of the 17906 genes tested for interactions survived the multiple comparisons corrections. In summary, our current GWAS study unveiled unidentified genetic susceptibility to pancreatic cancer using alternative methods. These novel findings provide new perspectives on genetic susceptibility to and molecular mechanisms of pancreatic cancer, once confirmed, will shed promising light on the prevention and treatment of this disease. ^

Nonlinear dimension reduction in pathway-based genome-wide association analysis

Pathway based genome wide association study evolves from pathway analysis for microarray gene expression and is under rapid development as a complementary for single-SNP based genome wide association study. However, it faces new challenges, such as the summarization of SNP statistics to pathway statistics. The current study applies the ridge regularized Kernel Sliced Inverse Regression (KSIR) to achieve dimension reduction and compared this method to the other two widely used methods, the minimal-p-value (minP) approach of assigning the best test statistics of all SNPs in each pathway as the statistics of the pathway and the principal component analysis (PCA) method of utilizing PCA to calculate the principal components of each pathway. Comparison of the three methods using simulated datasets consisting of 500 cases, 500 controls and100 SNPs demonstrated that KSIR method outperformed the other two methods in terms of causal pathway ranking and the statistical power. PCA method showed similar performance as the minP method. KSIR method also showed a better performance over the other two methods in analyzing a real dataset, the WTCCC Ulcerative Colitis dataset consisting of 1762 cases, 3773 controls as the discovery cohort and 591 cases, 1639 controls as the replication cohort. Several immune and non-immune pathways relevant to ulcerative colitis were identified by these methods. Results from the current study provided a reference for further methodology development and identified novel pathways that may be of importance to the development of ulcerative colitis.^

Development of a SPECT -based three-dimensional treatment planner for radionuclide therapy with iodine -131

Accurate calculation of absorbed dose to target tumors and normal tissues in the body is an important requirement for establishing fundamental dose-response relationships for radioimmunotherapy. Two major obstacles have been the difficulty in obtaining an accurate patient-specific 3-D activity map in-vivo and calculating the resulting absorbed dose. This study investigated a methodology for 3-D internal dosimetry, which integrates the 3-D biodistribution of the radionuclide acquired from SPECT with a dose-point kernel convolution technique to provide the 3-D distribution of absorbed dose. Accurate SPECT images were reconstructed with appropriate methods for noise filtering, attenuation correction, and Compton scatter correction. The SPECT images were converted into activity maps using a calibration phantom. The activity map was convolved with an $\sp{131}$I dose-point kernel using a 3-D fast Fourier transform to yield a 3-D distribution of absorbed dose. The 3-D absorbed dose map was then processed to provide the absorbed dose distribution in regions of interest. This methodology can provide heterogeneous distributions of absorbed dose in volumes of any size and shape with nonuniform distributions of activity. Comparison of the activities quantitated by our SPECT methodology to true activities in an Alderson abdominal phantom (with spleen, liver, and spherical tumor) yielded errors of $-$16.3% to 4.4%. Volume quantitation errors ranged from $-$4.0 to 5.9% for volumes greater than 88 ml. The percentage differences of the average absorbed dose rates calculated by this methodology and the MIRD S-values were 9.1% for liver, 13.7% for spleen, and 0.9% for the tumor. Good agreement (percent differences were less than 8%) was found between the absorbed dose due to penetrating radiation calculated from this methodology and TLD measurement. More accurate estimates of the 3-D distribution of absorbed dose can be used as a guide in specifying the minimum activity to be administered to patients to deliver a prescribed absorbed dose to tumor without exceeding the toxicity limits of normal tissues. ^

Variación del peso de la semilla de Nim y sus componentes durante su almacenamiento

En el presente trabajo se muestran los resultados de las investigaciones realizadas en el Instituto de Investigaciones Fundamentales en Agricultura Tropical (INIFAT), acerca de la acción del almacenamiento sobre la semilla de Nim y sus componentes. Se pudo determinar que durante el proceso de beneficio de los frutos de Nim cosechados sólo una cuarta parte del volumen de los frutos se convierte en semillas, definiéndose además que el peso de un fruto maduro es de 1.8 g y de una semilla seca 0.4 g. Por otra parte, se pudo conocer que en el proceso de almacenamiento de la semilla seca de Nim se produce una disminución notable del peso de la misma y sus componentes (almendra, aceite, torta y residuo), dada por la influencia del tiempo de almacenamiento y factores ambientales tales como humedad relativa y temperatura. Además, se observó de igual forma, que la mayor pérdida de peso de la semilla se produjo durante los dos primeros meses de almacenadas, que incluyen el período de envejecimiento fisiológico.

Variabilidad en poblaciones de maíz nativo de la Mixteca Baja Oaxaqueña, México

La diversidad genética poblacional de maíz en México es muy dinámica y depende de factores biológicos, agroecológicos y socio-económicos, y necesidades familiares. En este trabajo se describió y clasificó la variabilidad morfológica de una colección de 60 muestras poblacionales de maíz, colectadas en 44 municipios de la Mixteca Baja Oaxaqueña (846 msnm a 1842 msnm). Las muestras se sembraron y cultivaron durante el ciclo primavera-verano de 2010, en Santo Domingo Tonala, Oaxaca, bajo un diseño de bloques completos al azar con cuatro repeticiones. Se evaluaron 19 caracteres morfológicos de planta, mazorca, grano y espiga (panoja), y se determinaron diferencias significativas entre poblacionales en estos caracteres. Los caracteres altura de planta y mazorca, días a floración masculina y femenina, y número de granos por hilera en la mazorca fueron determinantes para describir la variabilidad morfológica total. La variación morfológica y fenológica de las poblaciones de maíz se asocia con los patrones altitudinales y geográficos de donde proceden. Se determinaron seis grupos fenotípicos significativamente diferentes con características de mazorca, grano y planta semejantes a las descritas para las razas Celaya, Bolita, Pepitilla, Ancho, y ciertos complejos raciales entre Ancho, Mixteco, Celaya y Bolita.

Distancias genéticas entre perfiles moleculares obtenidos desde marcadores multilocus multialélicos

Para expresar la magnitud de la identidad genética (similaridad) o su complemento (distancia) entre dos individuos caracterizados molecularmente a través de marcadores del tipo microsatélites (SSR), que son multilocusmultialélicos, es necesario elegir una métrica acorde con la naturaleza multivariada de los datos. Comúnmente, las métricas de distancias genéticas son diseñadas para expresar, en un único número, la diferencia genética entre dos poblaciones y son expresadas como función de la frecuencia alélica poblacional. Dichas métricas pueden también ser utilizadas para calcular la distancia entre perfiles individuales, pero las frecuencias alélicas no son continuas en este caso. Alternativamente, se pueden usar distancias geométricas obtenidas como el complemento del índice de similaridad para datos binarios que indican la presencia/ ausencia de cada alelo en un individuo. El objetivo de este trabajo fue evaluar simultáneamente el desempeño de ambos tipos de métricas para ordenar y clasificar individuos en una base de datos generadas a partir de loci de marcadores microsatélites SSR. Se calcularon 11 métricas de distancias a partir de 17 loci SSR obtenidos desde 17 introducciones de un banco de germoplasma de soja [Glycine max (L.) Merr.]. Se evaluó el consenso de los resultados obtenidos para la clasificación de los 17 perfiles moleculares desde varias métricas. Los resultados sugieren que los diferentes tipos de métricas producen información similar para comparar individuos. No obstante, se realizó una clasificación de las métricas que responden a diferencias entre los núcleos de las expresiones de cálculo.

Travel time and speed of gray whales and amphipod abundance along Chukotka Peninsula in 2006

The purpose of this study was to evaluate summer and fall residency and habitat selection by gray whales, Eschrichtius robustus, together with the biomass of benthic amphipod prey on the coastal feeding grounds along the Chukotka Peninsula. Thirteen gray whales were instrumented with satellite transmitters in September 2006 near the Chukotka Peninsula, Russia. Nine transmitters provided positions from whales for up to 81 days. The whales travelled within 5 km of the Chukotka coast for most of the period they were tracked with only occasional movements offshore. The average daily travel speeds were 23 km/day (range 9-53 km/day). Four of the whales had daily average travel speeds <1 km/day suggesting strong fidelity to the study area. The area containing 95% of the locations for individual whales during biweekly periods was on average 13,027 km**2 (range 7,097-15,896 km**2). More than 65% of all locations were in water <30 m, and between 45 and 70% of biweekly kernel home ranges were located in depths between 31 and 50 m. Benthic density of amphipods within the Bering Strait at depths <50 m was on average ~54 g wet wt/m**2 in 2006. It is likely that the abundant benthic biomass is more than sufficient forage to support the current gray whale population. The use of satellite telemetry in this study quantifies space use and movement patterns of gray whales along the Chukotka coast and identifies key feeding areas.