Multiscale adaptive smoothing models for the hemodynamic response function in fMRI

In the event-related functional magnetic resonance imaging (fMRI) data analysis, there is an extensive interest in accurately and robustly estimating the hemodynamic response function (HRF) and its associated statistics (e.g., the magnitude and duration of the activation). Most methods to date are developed in the time domain and they have utilized almost exclusively the temporal information of fMRI data without accounting for the spatial information. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) in the frequency domain by integrating the spatial and frequency information to adaptively and accurately estimate HRFs pertaining to each stimulus sequence across all voxels in a three-dimensional (3D) volume. We use two sets of simulation studies and a real data set to examine the finite sample performance of MASM in estimating HRFs. Our real and simulated data analyses confirm that MASM outperforms several other state-of-the-art methods, such as the smooth finite impulse response (sFIR) model.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS609 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Forecasting return of used products for remanufacturing using graphical evaluation and review technique (GERT)

This research develops a forecasting model that can predict the quantity, time and probability of product return, recyclable parts/components/materials and disposal. It adopts the Graphical Evaluation and Review Technique (GERT) by translating the remanufacturing operational process into a stochastic network. This stochastic network possesses two characteristics: activities having a probability of occurrence associated with them; and time to perform an activity. Together with the GERT method, Mason’s rule is applied to calculate the equivalence transfer function of the system, therefore predicting the desired outcomes. A generic eight-step process on how to implement this method in any structure of return products and remanufacturing network is provided. A numerical example is presented to demonstrate the result of using GERT on forecasting printer remanufacturing outcomes. The main contribution of this research is: Instead of giving one result such as either return quantity, or time, or probability, our research can forecast three of these outcomes simultaneously, and the algorithm is generalised to be applicable to any product structure and remanufacturing network

Research on manufacturing dry mixed cement mortar with high compressive strength, high flexural strength, low shrinkage and high watertightness for restoration of damaged hydraulic structures in Vietnam

Using normal materials to manufacture the mixed mortar is necessary for restoration of hydraulic structures in Vietnam. It will salvage the materials and decreases the cost price of the mortar. In this research, we used cement made in Vietnam (Chinfon - Haiphong cement), natural sand (Lo River sand), polymer acrylic and high range water reducing (of SIKA company)' with proportion 1 : 3 : 0.03 : 0.003 by weight. The water to cement ratio is 0.5, which always ensure the compressive strength of mortar more than 40 MPa and small shrinkage, good watertightness, and high adhesion. That is suitable for the restoration of concrete structures in general and hydraulic structures in particular of Vietnam. The dry mixed mortar is manufactured and in bag of 15±0.5 kg weight

TwinMARM: Two-Stage Multiscale Adaptive Regression Methods for Twin Neuroimaging Data

Twin imaging studies have been valuable for understanding the relative contribution of the environment and genes on brain structures and their functions. Conventional analyses of twin imaging data include three sequential steps: spatially smoothing imaging data, independently fitting a structural equation model at each voxel, and finally correcting for multiple comparisons. However, conventional analyses are limited due to the same amount of smoothing throughout the whole image, the arbitrary choice of smoothing extent, and the decreased power in detecting environmental and genetic effects introduced by smoothing raw images. The goal of this article is to develop a two-stage multiscale adaptive regression method (TwinMARM) for spatial and adaptive analysis of twin neuroimaging and behavioral data. The first stage is to establish the relationship between twin imaging data and a set of covariates of interest, such as age and gender. The second stage is to disentangle the environmental and genetic influences on brain structures and their functions. In each stage, TwinMARM employs hierarchically nested spheres with increasing radii at each location and then captures spatial dependence among imaging observations via consecutively connected spheres across all voxels. Simulation studies show that our TwinMARM significantly outperforms conventional analyses of twin imaging data. Finally, we use our method to detect statistically significant effects of genetic and environmental variations on white matter structures in a neonatal twin study

An Improvement of Shotgun Proteomics Analysis by Adding Next-Generation Sequencing Transcriptome Data in Orange

BACKGROUND: Shotgun proteomics data analysis usually relies on database search. Because commonly employed protein sequence databases of most species do not contain sufficient protein information, the application of shotgun proteomics to the research of protein sequence profile remains a big challenge, especially to the species whose genome has not been sequenced yet. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we present a workflow with integrated database to partly address this problem. First, we downloaded the homologous species database. Next, we identified the transcriptome of the sample, created a protein sequence database based on the transcriptome data, and integtrated it with homologous species database. Lastly, we developed a workflow for identifying peptides simultaneously from shotgun proteomics data. CONCLUSIONS/SIGNIFICANCE: We used datasets from orange leaves samples to demonstrate our workflow. The results showed that the integrated database had great advantage on orange shotgun proteomics data analysis compared to the homologous species database, an 18.5% increase in number of proteins identification

Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing

Information in electronic medical records is often in an unstructured free-text format. This format presents challenges for expedient data retrieval and may fail to convey important findings. Natural language processing (NLP) is an emerging technique for rapid and efficient clinical data retrieval. While proven in disease detection, the utility of NLP in discerning disease progression from free-text reports is untested. We aimed to (1) assess whether unstructured radiology reports contained sufficient information for tumor status classification; (2) develop an NLP-based data extraction tool to determine tumor status from unstructured reports; and (3) compare NLP and human tumor status classification outcomes. Consecutive follow-up brain tumor magnetic resonance imaging reports (2000–­2007) from a tertiary center were manually annotated using consensus guidelines on tumor status. Reports were randomized to NLP training (70%) or testing (30%) groups. The NLP tool utilized a support vector machines model with statistical and rule-based outcomes. Most reports had sufficient information for tumor status classification, although 0.8% did not describe status despite reference to prior examinations. Tumor size was unreported in 68.7% of documents, while 50.3% lacked data on change magnitude when there was detectable progression or regression. Using retrospective human classification as the gold standard, NLP achieved 80.6% sensitivity and 91.6% specificity for tumor status determination (mean positive predictive value, 82.4%; negative predictive value, 92.0%). In conclusion, most reports contained sufficient information for tumor status determination, though variable features were used to describe status. NLP demonstrated good accuracy for tumor status classification and may have novel application for automated disease status classification from electronic databases

Time to full enteral feeding for very low-birth-weight infants varies markedly among hospitals worldwide but may not be associated with incidence of necrotizing enterocolitis:The NEOMUNE-NeoNutriNet Cohort Study

Background: Transition to enteral feeding is difficult for very low-birth-weight (VLBW; ≤1500 g) infants, and optimal nutrition is important for clinical outcomes. Method: Data on feeding practices and short-term clinical outcomes (growth, necrotizing enterocolitis [NEC], mortality) in VLBW infants were collected from 13 neonatal intensive care units (NICUs) in 5 continents (n = 2947). Specifically, 5 NICUs in Guangdong province in China (GD), mainly using formula feeding and slow feeding advancement (n = 1366), were compared with the remaining NICUs (non-GD, n = 1581, Oceania, Europe, United States, Taiwan, Africa) using mainly human milk with faster advancement rates. Results: Across NICUs, large differences were observed for time to reach full enteral feeding (TFF; 8–33 days), weight gain (5.0–14.6 g/kg/day), ∆z-scores (−0.54 to −1.64), incidence of NEC (1%–13%), and mortality (1%–18%). Adjusted for gestational age, GD units had longer TFF (26 vs 11 days), lower weight gain (8.7 vs 10.9 g/kg/day), and more days on antibiotics (17 vs 11 days; all P <.001) than non-GD units, but NEC incidence and mortality were similar. Conclusion: Feeding practices for VLBW infants vary markedly around the world. Use of formula and long TFF in South China was associated with more use of antibiotics and slower weight gain, but apparently not with more NEC or higher mortality. Both infant- and hospital-related factors influence feeding practices for preterm infants. Multicenter, randomized controlled trials are required to identify the optimal feeding strategy during the first weeks of life