An R-package for the Estimation and Testing of Multiple Covariates and Biomarker Interactions for Survival Data Based on Local Partial Likelihood

When we study the variables that a ffect survival time, we usually estimate their eff ects by the Cox regression model. In biomedical research, e ffects of the covariates are often modi ed by a biomarker variable. This leads to covariates-biomarker interactions. Here biomarker is an objective measurement of the patient characteristics at baseline. Liu et al. (2015) has built up a local partial likelihood bootstrap model to estimate and test this interaction e ffect of covariates and biomarker, but the R code developed by Liu et al. (2015) can only handle one variable and one interaction term and can not t the model with adjustment to nuisance variables. In this project, we expand the model to allow adjustment to nuisance variables, expand the R code to take any chosen interaction terms, and we set up many parameters for users to customize their research. We also build up an R package called "lplb" to integrate the complex computations into a simple interface. We conduct numerical simulation to show that the new method has excellent fi nite sample properties under both the null and alternative hypothesis. We also applied the method to analyze data from a prostate cancer clinical trial with acid phosphatase (AP) biomarker.

Compositional data analysis of Holocene sediments from the West Bengal Sundarbans, India: Geochemical proxies for grain-size variability in a delta environment

This paper is part of a special issue of Applied Geochemistry focusing on reliable applications of compositional multivariate statistical methods. This study outlines the application of compositional data analysis (CoDa) to calibration of geochemical data and multivariate statistical modelling of geochemistry and grain-size data from a set of Holocene sedimentary cores from the Ganges-Brahmaputra (G-B) delta. Over the last two decades, understanding near-continuous records of sedimentary sequences has required the use of core-scanning X-ray fluorescence (XRF) spectrometry, for both terrestrial and marine sedimentary sequences. Initial XRF data are generally unusable in ‘raw-format’, requiring data processing in order to remove instrument bias, as well as informed sequence interpretation. The applicability of these conventional calibration equations to core-scanning XRF data are further limited by the constraints posed by unknown measurement geometry and specimen homogeneity, as well as matrix effects. Log-ratio based calibration schemes have been developed and applied to clastic sedimentary sequences focusing mainly on energy dispersive-XRF (ED-XRF) core-scanning. This study has applied high resolution core-scanning XRF to Holocene sedimentary sequences from the tidal-dominated Indian Sundarbans, (Ganges-Brahmaputra delta plain). The Log-Ratio Calibration Equation (LRCE) was applied to a sub-set of core-scan and conventional ED-XRF data to quantify elemental composition. This provides a robust calibration scheme using reduced major axis regression of log-ratio transformed geochemical data. Through partial least squares (PLS) modelling of geochemical and grain-size data, it is possible to derive robust proxy information for the Sundarbans depositional environment. The application of these techniques to Holocene sedimentary data offers an improved methodological framework for unravelling Holocene sedimentation patterns.

Metabolic signatures of Huntington's disease (HD): 1H NMR analysis of the polar metabolome in post mortem human brain

Huntington’s disease (HD) is an autosomal neurodegenerative disorder affecting approximately 5-10 persons per 100,000 worldwide. The pathophysiology of HD is not fully understood but the age of onset is known to be highly dependent on the number of CAG triplet repeats in the huntingtin gene. Using 1H NMR spectroscopy this study biochemically profiled 39 brain metabolites in post-mortem striatum (n=14) and frontal lobe (n=14) from HD sufferers and controls (n=28). Striatum metabolites were more perturbed with 15 significantly affected in HD cases, compared with only 4 in frontal lobe (P<0.05; q<0.3). The metabolite which changed most overall was urea which decreased 3.25-fold in striatum (P<0.01). Four metabolites were consistently affected in both brain regions. These included the neurotransmitter precursors tyrosine and L-phenylalanine which were significantly depleted by 1.55-1.58-fold and 1.48-1.54-fold in striatum and frontal lobe, respectively (P=0.02-0.03). They also included L-leucine which was reduced 1.54-1.69-fold (P=0.04-0.09) and myo-inositol which was increased 1.26-1.37-fold (P<0.01). Logistic regression analyses performed with MetaboAnalyst demonstrated that data obtained from striatum produced models which were profoundly more sensitive and specific than those produced from frontal lobe. The brain metabolite changes uncovered in this first 1H NMR investigation of human HD offer new insights into the disease pathophysiology. Further investigations of striatal metabolite disturbances are clearly warranted.

Immunoglobulin gene rearrangements and the pathogenesis of multiple myeloma.

The ability to rearrange the germ-line DNA to generate antibody diversity is an essential prerequisite for the production of a functional repertoire. While this is essential to prevent infections, it also represents the "Achilles heel" of the B-cell lineage, occasionally leading to malignant transformation of these cells by translocation of protooncogenes into the immunoglobulin (Ig) loci. However, in evolutionary terms this is a small price to pay for a functional immune system. The study of the configuration and rearrangements of the Ig gene loci has contributed extensively to our understanding of the natural history of development of myeloma. In addition to this, the analysis of Ig gene rearrangements in B-cell neoplasms provides information about the clonal origin of the disease, prognosis, as well as providing a clinical useful tool for clonality detection and minimal residual disease monitoring. Herein, we review the data currently available on both Ig gene rearrangements and protein patterns seen in myeloma with the aim of illustrating how this knowledge has contributed to our understanding of the pathobiology of myeloma.

Integration of global SNP-based mapping and expression arrays reveals key regions, mechanisms, and genes important in the pathogenesis of multiple myeloma.

Multiple myeloma is characterized by genomic alterations frequently involving gains and losses of chromosomes. Single nucleotide polymorphism (SNP)-based mapping arrays allow the identification of copy number changes at the sub-megabase level and the identification of loss of heterozygosity (LOH) due to monosomy and uniparental disomy (UPD). We have found that SNP-based mapping array data and fluorescence in situ hybridization (FISH) copy number data correlated well, making the technique robust as a tool to investigate myeloma genomics. The most frequently identified alterations are located at 1p, 1q, 6q, 8p, 13, and 16q. LOH is found in these large regions and also in smaller regions throughout the genome with a median size of 1 Mb. We have identified that UPD is prevalent in myeloma and occurs through a number of mechanisms including mitotic nondisjunction and mitotic recombination. For the first time in myeloma, integration of mapping and expression data has allowed us to reduce the complexity of standard gene expression data and identify candidate genes important in both the transition from normal to monoclonal gammopathy of unknown significance (MGUS) to myeloma and in different subgroups within myeloma. We have documented these genes, providing a focus for further studies to identify and characterize those that are key in the pathogenesis of myeloma.

A global expression-based analysis of the consequences of the t(4;14) translocation in myeloma

Purpose: Our purpose in this report was to define genes and pathways dysregulated as a consequence of the t(4;14) in myeloma, and to gain insight into the downstream functional effects that may explain the different prognosis of this subgroup.Experimental Design: Fibroblast growth factor receptor 3 (FGFR3) overexpression, the presence of immunoglobulin heavy chain-multiple myeloma SET domain (IgH-MMSET) fusion products and the identification of t(4;14) breakpoints were determined in a series of myeloma cases. Differentially expressed genes were identified between cases with (n = 55) and without (n = 24) a t(4;14) by using global gene expression analysis.Results: Cases with a t(4;14) have a distinct expression pattern compared with other cases of myeloma. A total of 127 genes were identified as being differentially expressed including MMSET and cyclin D2, which have been previously reported as being associated with this translocation. Other important functional classes of genes include cell signaling, apoptosis and related genes, oncogenes, chromatin structure, and DNA repair genes. Interestingly, 25% of myeloma cases lacking evidence of this translocation had up-regulation of the MMSET transcript to the same level as cases with a translocation.Conclusions: t(4;14) cases form a distinct subgroup of myeloma cases with a unique gene signature that may account for their poor prognosis. A number of non-t(4;14) cases also express MMSET consistent with this gene playing a role in myeloma pathogenesis.

Gene scanning of VDJH-amplified segments is a clinically relevant technique to detect contaminating tumor cells in the apheresis products of multiple myeloma patients undergoing autologous peripheral blood stem cell transplantation.

Contaminating tumour cells in apheresis products have proved to influence the outcome of patients with multiple myeloma (MM) undergoing autologous stem cell transplantation (APBSCT). The gene scanning of clonally rearranged VDJ segments of the heavy chain immunoglobulin gene (VDJH) is a reproducible and easy to perform technique that can be optimised for clinical laboratories. We used it to analyse the aphereses of 27 MM patients undergoing APBSCT with clonally detectable VDJH segments, and 14 of them yielded monoclonal peaks in at least one apheresis product. The presence of positive results was not related to any pre-transplant characteristics, except the age at diagnosis (lower in patients with negative products, P = 0.04). Moreover, a better pre-transplant response trended to associate with a negative result (P = 0.069). Patients with clonally free products were more likely to obtain a better response to transplant (complete remission, 54% vs 28%; >90% reduction in the M-component, 93% vs 43% P = 0.028). In addition, patients transplanted with polyclonal products had longer progression-free survival, (39 vs 19 months, P = 0.037) and overall survival (81% vs 28% at 5 years, P = 0.045) than those transplanted with monoclonal apheresis. In summary, the gene scanning of apheresis products is a useful and clinically relevant technique in MM transplanted patients.

Energy efficiency analysis of rank-1 Ricean fading MIMO channels

This paper studies the energy efficiency (EE) of a point-to-point rank-1 Ricean fading multiple-input-multiple-output (MIMO) channel. In particular, a tight lower bound and an asymptotic approximation for the EE of the considered MIMO system are presented, under the assumption that the channel is unknown at the transmitter and perfectly known at the receiver. Moreover, the effects of different system parameters, namely, transmit power, spectral efficiency (SE), and number of transmit and receive antennas, on the EE are analytically investigated. An important observation is that, in the high signal-to-noise ratio regime and with the other system parameters fixed, the optimal transmit power that maximizes the EE increases as the Ricean-K factor increases. On the contrary, the optimal SE and the optimal number of transmit antennas decrease as K increases.

Identification of Microturbine Model for Long-term Dynamic Analysis of Distribution Networks

As one of the most successfully commercialized distributed energy resources, the long-term effects of microturbines (MTs) on the distribution network has not been fully investigated due to the complex thermo-fluid-mechanical energy conversion processes. This is further complicated by the fact that the parameter and internal data of MTs are not always available to the electric utility, due to different ownerships and confidentiality concerns. To address this issue, a general modeling approach for MTs is proposed in this paper, which allows for the long-term simulation of the distribution network with multiple MTs. First, the feasibility of deriving a simplified MT model for long-term dynamic analysis of the distribution network is discussed, based on the physical understanding of dynamic processes that occurred within MTs. Then a three-stage identification method is developed in order to obtain a piecewise MT model and predict electro-mechanical system behaviors with saturation. Next, assisted with the electric power flow calculation tool, a fast simulation methodology is proposed to evaluate the long-term impact of multiple MTs on the distribution network. Finally, the model is verified by using Capstone C30 microturbine experiments, and further applied to the dynamic simulation of a modified IEEE 37-node test feeder with promising results.

Conservation genetics of North Atlantic loggerhead sea turtles: analysis of nuclear and mitochondrial DNA

[EN] Complex population structure has been described for the loggerhead sea turtle (Caretta caretta), revealing lower levels of population genetic structure in nuclear compared to mitochondrial DNA assays. This may result from mating during spatially overlapping breeding migrations, or male-biased dispersal as previously found for the green turtle (Chelonia mydas). To further investigate these multiple possibilities, we carried out a comparative analysis from twelve newly developed microsatellite loci and the mitochondrial DNA control region (~804 bp) in adult females of the Cape Verde Islands (n=158), and Georgia, USA (n=17).

Graph analysis of verbal fluency test discriminate between patients with Alzheimer’s disease, Mild Cognitive Impairment and normal elderly controls

Verbal fluency is the ability to produce a satisfying sequence of spoken words during a given time interval. The core of verbal fluency lies in the capacity to manage the executive aspects of language. The standard scores of the semantic verbal fluency test are broadly used in the neuropsychological assessment of the elderly, and different analytical methods are likely to extract even more information from the data generated in this test. Graph theory, a mathematical approach to analyze relations between items, represents a promising tool to understand a variety of neuropsychological states. This study reports a graph analysis of data generated by the semantic verbal fluency test by cognitively healthy elderly (NC), patients with Mild Cognitive Impairment – subtypes amnestic(aMCI) and amnestic multiple domain (a+mdMCI) - and patients with Alzheimer’s disease (AD). Sequences of words were represented as a speech graph in which every word corresponded to a node and temporal links between words were represented by directed edges. To characterize the structure of the data we calculated 13 speech graph attributes (SGAs). The individuals were compared when divided in three (NC – MCI – AD) and four (NC – aMCI – a+mdMCI – AD) groups. When the three groups were compared, significant differences were found in the standard measure of correct words produced, and three SGA: diameter, average shortest path, and network density. SGA sorted the elderly groups with good specificity and sensitivity. When the four groups were compared, the groups differed significantly in network density, except between the two MCI subtypes and NC and aMCI. The diameter of the network and the average shortest path were significantly different between the NC and AD, and between aMCI and AD. SGA sorted the elderly in their groups with good specificity and sensitivity, performing better than the standard score of the task. These findings provide support for a new methodological frame to assess the strength of semantic memory through the verbal fluency task, with potential to amplify the predictive power of this test. Graph analysis is likely to become clinically relevant in neurology and psychiatry, and may be particularly useful for the differential diagnosis of the elderly.

Exploring associations between violent discipline and aggressive behavior in children ages 4-5 in The Republic of Nicaragua: A secondary data analysis of the 2011 Demographic and Health Survey

Thesis (Master's)--University of Washington, 2016-06

Utility Analysis for Multiple Selection Devices and Multiple Outcomes

Traditional utility analysis only calculates the value of a given selection procedure over random selection. This assumption is not only an inaccurate representation of staffing policy but also leads to overestimates of a device’s value. This paper presents a more accurate method for computing the validity of a selection battery for when there are multiple selection devices and multiple criteria. Application of the method is illustrated using previous utility analysis work and an actual case of administrative assistants with eight predictors and nine criteria. A final example also is provided that includes these advancements as well as other researchers’ advances in a combined utility model. Results reveal that accounting for multiple criteria and outcomes dramatically reduces the utility estimates of implementing new selection devices.

Computational Analysis of Intelligent Agents: Social and Strategic Settings

The central motif of this work is prediction and optimization in presence of multiple interacting intelligent agents. We use the phrase `intelligent agents' to imply in some sense, a `bounded rationality', the exact meaning of which varies depending on the setting. Our agents may not be `rational' in the classical game theoretic sense, in that they don't always optimize a global objective. Rather, they rely on heuristics, as is natural for human agents or even software agents operating in the real-world. Within this broad framework we study the problem of influence maximization in social networks where behavior of agents is myopic, but complication stems from the structure of interaction networks. In this setting, we generalize two well-known models and give new algorithms and hardness results for our models. Then we move on to models where the agents reason strategically but are faced with considerable uncertainty. For such games, we give a new solution concept and analyze a real-world game using out techniques. Finally, the richest model we consider is that of Network Cournot Competition which deals with strategic resource allocation in hypergraphs, where agents reason strategically and their interaction is specified indirectly via player's utility functions. For this model, we give the first equilibrium computability results. In all of the above problems, we assume that payoffs for the agents are known. However, for real-world games, getting the payoffs can be quite challenging. To this end, we also study the inverse problem of inferring payoffs, given game history. We propose and evaluate a data analytic framework and we show that it is fast and performant.