A Bayesian changepoint analysis on spatio-temporal point processes

Changepoint analysis is a well established area of statistical research, but in the context of spatio-temporal point processes it is as yet relatively unexplored. Some substantial differences with regard to standard changepoint analysis have to be taken into account: firstly, at every time point the datum is an irregular pattern of points; secondly, in real situations issues of spatial dependence between points and temporal dependence within time segments raise. Our motivating example consists of data concerning the monitoring and recovery of radioactive particles from Sandside beach, North of Scotland; there have been two major changes in the equipment used to detect the particles, representing known potential changepoints in the number of retrieved particles. In addition, offshore particle retrieval campaigns are believed may reduce the particle intensity onshore with an unknown temporal lag; in this latter case, the problem concerns multiple unknown changepoints. We therefore propose a Bayesian approach for detecting multiple changepoints in the intensity function of a spatio-temporal point process, allowing for spatial and temporal dependence within segments. We use Log-Gaussian Cox Processes, a very flexible class of models suitable for environmental applications that can be implemented using integrated nested Laplace approximation (INLA), a computationally efficient alternative to Monte Carlo Markov Chain methods for approximating the posterior distribution of the parameters. Once the posterior curve is obtained, we propose a few methods for detecting significant change points. We present a simulation study, which consists in generating spatio-temporal point pattern series under several scenarios; the performance of the methods is assessed in terms of type I and II errors, detected changepoint locations and accuracy of the segment intensity estimates. We finally apply the above methods to the motivating dataset and find good and sensible results about the presence and quality of changes in the process.

New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.

A sequential Cox approach for estimating the causal effect of treatment in the presence of time-dependent confounding applied to data from the Swiss HIV Cohort Study

When estimating the effect of treatment on HIV using data from observational studies, standard methods may produce biased estimates due to the presence of time-dependent confounders. Such confounding can be present when a covariate, affected by past exposure, is both a predictor of the future exposure and the outcome. One example is the CD4 cell count, being a marker for disease progression for HIV patients, but also a marker for treatment initiation and influenced by treatment. Fitting a marginal structural model (MSM) using inverse probability weights is one way to give appropriate adjustment for this type of confounding. In this paper we study a simple and intuitive approach to estimate similar treatment effects, using observational data to mimic several randomized controlled trials. Each 'trial' is constructed based on individuals starting treatment in a certain time interval. An overall effect estimate for all such trials is found using composite likelihood inference. The method offers an alternative to the use of inverse probability of treatment weights, which is unstable in certain situations. The estimated parameter is not identical to the one of an MSM, it is conditioned on covariate values at the start of each mimicked trial. This allows the study of questions that are not that easily addressed fitting an MSM. The analysis can be performed as a stratified weighted Cox analysis on the joint data set of all the constructed trials, where each trial is one stratum. The model is applied to data from the Swiss HIV cohort study.

Projected 21st century decrease in marine productivity: a multi-model analysis

Changes in marine net primary productivity (PP) and export of particulate organic carbon (EP) are projected over the 21st century with four global coupled carbon cycle-climate models. These include representations of marine ecosystems and the carbon cycle of different structure and complexity. All four models show a decrease in global mean PP and EP between 2 and 20% by 2100 relative to preindustrial conditions, for the SRES A2 emission scenario. Two different regimes for productivity changes are consistently identified in all models. The first chain of mechanisms is dominant in the low- and mid-latitude ocean and in the North Atlantic: reduced input of macro-nutrients into the euphotic zone related to enhanced stratification, reduced mixed layer depth, and slowed circulation causes a decrease in macro-nutrient concentrations and in PP and EP. The second regime is projected for parts of the Southern Ocean: an alleviation of light and/or temperature limitation leads to an increase in PP and EP as productivity is fueled by a sustained nutrient input. A region of disagreement among the models is the Arctic, where three models project an increase in PP while one model projects a decrease. Projected changes in seasonal and interannual variability are modest in most regions. Regional model skill metrics are proposed to generate multi-model mean fields that show an improved skill in representing observation-based estimates compared to a simple multi-model average. Model results are compared to recent productivity projections with three different algorithms, usually applied to infer net primary production from satellite observations.

A causal model of mathematics performance in early adolescence: the role of sex

Using path analysis, the present investigation was done to clarify possible causal linkages among general scholastic aptitude, academic achievement in mathematics, self-concept of ability, and performance on a mathematics examination. Subjects were 122 eighth-grade students who completed a mathematics examination as well as a measure of self-concept of ability. Aptitude and achievement measures were obtained from school records. Analysis showed sex differences in prediction of performance on the mathematics examination. For boys, this performance could be predicted from scholastic aptitude and previous achievement in mathematics. For girls, performance only could be predicted from previous achievement in mathematics. These results indicate that the direction, strength, and magnitude of relations among these variables differed for boys and girls, while mean levels of performance did not.

An empirical examination of the integration of causal attribution and social learning theories in achievement situations

Using path analysis, the present investigation sought to clarify possible operational linkages among constructs from social learning and attribution theories within the context of a self-esteem system. Subjects were 300 undergraduate university students who completed a measure of self-esteem and indicated expectancies for success and minimal goal levels for an experimental task. After completing the task and receiving feedback about their performance, subjects completed causal attribution and self-esteem questionnaires. Results revealed gender differences in the degree and strength of the proposed relations, but not in the mean levels of the variables studied. Results suggested that the integration of social learning and attribution theories within a single conceptual model provides a better understanding of students' behaviors and self-esteem in achievement situations.

Intravaginal practices, bacterial vaginosis, and HIV infection in women: individual participant data meta-analysis

Background Identifying modifiable factors that increase women's vulnerability to HIV is a critical step in developing effective female-initiated prevention interventions. The primary objective of this study was to pool individual participant data from prospective longitudinal studies to investigate the association between intravaginal practices and acquisition of HIV infection among women in sub-Saharan Africa. Secondary objectives were to investigate associations between intravaginal practices and disrupted vaginal flora; and between disrupted vaginal flora and HIV acquisition. Methods and Findings We conducted a meta-analysis of individual participant data from 13 prospective cohort studies involving 14,874 women, of whom 791 acquired HIV infection during 21,218 woman years of follow-up. Data were pooled using random-effects meta-analysis. The level of between-study heterogeneity was low in all analyses (I2 values 0.0%–16.1%). Intravaginal use of cloth or paper (pooled adjusted hazard ratio [aHR] 1.47, 95% confidence interval [CI] 1.18–1.83), insertion of products to dry or tighten the vagina (aHR 1.31, 95% CI 1.00–1.71), and intravaginal cleaning with soap (aHR 1.24, 95% CI 1.01–1.53) remained associated with HIV acquisition after controlling for age, marital status, and number of sex partners in the past 3 months. Intravaginal cleaning with soap was also associated with the development of intermediate vaginal flora and bacterial vaginosis in women with normal vaginal flora at baseline (pooled adjusted odds ratio [OR] 1.24, 95% CI 1.04–1.47). Use of cloth or paper was not associated with the development of disrupted vaginal flora. Intermediate vaginal flora and bacterial vaginosis were each associated with HIV acquisition in multivariable models when measured at baseline (aHR 1.54 and 1.69, p<0.001) or at the visit before the estimated date of HIV infection (aHR 1.41 and 1.53, p<0.001), respectively. Conclusions This study provides evidence to suggest that some intravaginal practices increase the risk of HIV acquisition but a direct causal pathway linking intravaginal cleaning with soap, disruption of vaginal flora, and HIV acquisition has not yet been demonstrated. More consistency in the definition and measurement of specific intravaginal practices is warranted so that the effects of specific intravaginal practices and products can be further elucidated.

Fic domain-catalyzed adenylylation: insight provided by the structural analysis of the type IV secretion system effector BepA

Numerous bacterial pathogens subvert cellular functions of eukaryotic host cells by the injection of effector proteins via dedicated secretion systems. The type IV secretion system (T4SS) effector protein BepA from Bartonella henselae is composed of an N-terminal Fic domain and a C-terminal Bartonella intracellular delivery domain, the latter being responsible for T4SS-mediated translocation into host cells. A proteolysis resistant fragment (residues 10-302) that includes the Fic domain shows autoadenylylation activity and adenylyl transfer onto Hela cell extract proteins as demonstrated by autoradiography on incubation with α-[(32)P]-ATP. Its crystal structure, determined to 2.9-Å resolution by the SeMet-SAD method, exhibits the canonical Fic fold including the HPFxxGNGRxxR signature motif with several elaborations in loop regions and an additional β-rich domain at the C-terminus. On crystal soaking with ATP/Mg(2+), additional electron density indicated the presence of a PP(i) /Mg(2+) moiety, the side product of the adenylylation reaction, in the anion binding nest of the signature motif. On the basis of this information and that of the recent structure of IbpA(Fic2) in complex with the eukaryotic target protein Cdc42, we present a detailed model for the ternary complex of Fic with the two substrates, ATP/Mg(2+) and target tyrosine. The model is consistent with an in-line nucleophilic attack of the deprotonated side-chain hydroxyl group onto the α-phosphorus of the nucleotide to accomplish AMP transfer. Furthermore, a general, sequence-independent mechanism of target positioning through antiparallel β-strand interactions between enzyme and target is suggested.

Detecting and resolving position-dependent temperature effects in real-time quantitative polymerase chain reaction

Real-time quantitative polymerase chain reaction (qPCR) depends on precise temperature control of the sample during cycling. In the current study, we investigated how temperature variation in plate-based qPCR instruments influences qPCR results. Temperature variation was measured by amplicon melting analysis as a convenient means to assess well-to-well differences. Multiple technical replicates of several SYBR Green I-based qPCR assays allowed correlation of relative well temperature to quantification cycle. We found that inadequate template denaturation results in an inverse correlation and requires increasing the denaturation temperature, adding a DNA destabilizing agent, or pretreating with a restriction enzyme. In contrast, inadequate primer annealing results in a direct correlation and requires lowering the annealing temperature. Significant correlations were found in 18 of 25 assays. The critical nature of temperature-dependent effects was shown in a blinded study of 29 patients for the diagnosis of Prader-Willy and Angelman syndromes, where eight diagnoses were incorrect unless temperature-dependent effects were controlled. A method to detect temperature-dependent effects by pairwise comparisons of replicates in routine experiments is presented and applied. Systematic temperature errors in qPCR instruments can be recognized and their effects eliminated when high precision is required in quantitative genetic diagnostics and critical complementary DNA analyses.

Varying spin state composition by the choice of capping ligand in a family of molecular chains: detailed analysis of magnetic properties of chromium(III) horseshoes

We report a detailed physical analysis on a family of isolated, antiferro-magnetically (AF) coupled, chromium(III) finite chains, of general formula (Cr(RCO(2))(2)F)(n) where the chain length n = 6 or 7. Additionally, the chains are capped with a selection of possible terminating ligands, including hfac (= 1,1,1,5,5,5-hexafluoropentane-2,4-dionate(1-)), acac (= pentane-2,4-dionate(1-)) or (F)(3). Measurements by inelastic neutron scattering (INS), magnetometery and electron paramagnetic resonance (EPR) spectroscopy have been used to study how the electronic properties are affected by n and capping ligand type. These comparisons allowed the subtle electronic effects the choice of capping ligand makes for odd member spin 3/2 ground state and even membered spin 0 ground state chains to be investigated. For this investigation full characterisation of physical properties have been performed with spin Hamiltonian parameterisation, including the determination of Heisenberg exchange coupling constants and single ion axial and rhombic anisotropy. We reveal how the quantum spin energy levels of odd or even membered chains can be modified by the type of capping ligand terminating the chain. Choice of capping ligands enables Cr-Cr exchange coupling to be adjusted by 0, 4 or 24%, relative to Cr-Cr exchange coupling within the body of the chain, by the substitution of hfac, acac or (F)(3) capping ligands to the ends of the chain, respectively. The manipulation of quantum spin levels via ligands which play no role in super-exchange, is of general interest to the practise of spin Hamilton modelling, where such second order effects are generally not considered of relevance to magnetic properties.

A missense mutation in the skeletal muscle chloride channel 1 (CLCN1) as candidate causal mutation for congenital myotonia in a New Forest pony

A 7-month-old New Forest foal presented for episodes of recumbency and stiffness with myotonic discharges on electromyography. The observed phenotype resembled congenital myotonia caused by CLCN1 mutations in goats and humans. Mutation of the CLCN1 gene was considered as possible cause and mutation analysis was performed. The affected foal was homozygous for a missense mutation (c.1775A>C, p.D592A) located in a well conserved domain of the CLCN1 gene. The mutation showed a recessive mode of inheritance within the reported pony family. Therefore, this CLCN1 polymorphism is considered to be a possible cause of congenital myotonia.

Is the allegiance effect an epiphenomenon of true efficacy differences between treatments? a meta-analysis

Many meta-analyses of comparative outcome studies found a substantial association of researcher allegiance (RA) and relative treatment effects. Therefore, RA is regarded as a biasing factor in comparative outcome research (RA bias hypothesis). However, the RA bias hypothesis has been criticized as causality might be reversed. That is, RA might be a reflection of true efficacy differences between treatments (true efficacy hypothesis). Consequently, the RA-outcome association would not be indicative of bias but an epiphenomenon of true efficacy differences. This meta-analysis tested the validity of the true efficacy hypothesis. This was done by controlling the RA-outcome association for true efficacy differences by restricting analysis to direct comparisons of treatments with equivalent efficacy. We included direct comparisons of different versions of trauma-focused therapy (TFT) in the treatment of posttraumatic stress disorder (PTSD). RA was measured from the research reports. Relative effect sizes for symptoms of PTSD were calculated. Random effects meta-regression was conducted. Twenty-nine comparisons of TFTs from 20 studies were identified. Initial heterogeneity among relative effect sizes was low. RA was a significant predictor of outcome and explained 12% of the variance in outcomes. The true efficacy hypothesis predicted the RA-outcome association to be zero; however, a substantial association was found. Thus, this study does not support the true efficacy hypothesis. Given findings from psychotherapy research and other fields that support a biasing influence of researcher preferences, RA should be regarded as a causal factor and conceptualized as a threat to the validity of conclusions from comparative outcome studies.

Assessment of estrogenic exposure in brown trout (Salmo trutta) in a Swiss midland river: integrated analysis of passive samplers, wild and caged fish, and vitellogenin mRNA and protein

This field study examined the vitellogenin (VTG) biomarker response under conditions of low and fluctuating activities of environmental estrogenicity. The present study was performed on immature brown trout (Salmo trutta) exposed to the small river Luetzelmurg, which is located in the prealpine Swiss midland region and receives effluents from a single sewage treatment plant (STP). To understand better factors influencing the relationship between estrogenic exposure and VTG induction, we compared VTG levels in caged (stationary) and feral (free-ranging) fish, VTG levels in fish from up- and downstream of the STP, and two different methods for quantifying VTG (enzyme-linked immunosorbent assay vs real-time reverse transcription-polymerase chain reaction), and we used passive samplers (polar organic chemical integrative sampler [POCIS]) to integrate the variable, bioaccumulative estrogenic load in the river water over time. The POCIS from the downstream site contained approximately 20-fold higher levels of bioassay-derived estrogen equivalents than the POCIS from the upstream site. In feral fish, this site difference in estrogenic exposure was reflected in VTG protein levels but not in VTG mRNA. In contrast, in caged fish, the site difference was evident only for VTG mRNA but not for VTG protein. Thus, the outcome of VTG biomarker measurements varied with the analytical detection method (protein vs mRNA) and with the exposure modus (caged vs feral). Our findings suggest that for environmental situations with low and variable estrogenic contamination, a multiple-assessment approach may be necessary for the assessment of estrogenic exposure in fish.

THE ROLE OF AN EXPLICIT CAUSAL FRAMEWORK IN AFFECTED SIB PAIR DESIGNS WITH COVARIATES

The affected sib/relative pair (ASP/ARP) design is often used with covariates to find genes that can cause a disease in pathways other than through those covariates. However, such "covariates" can themselves have genetic determinants, and the validity of existing methods has so far only been argued under implicit assumptions. We propose an explicit causal formulation of the problem using potential outcomes and principal stratification. The general role of this formulation is to identify and separate the meaning of the different assumptions that can provide valid causal inference in linkage analysis. This separation helps to (a) develop better methods under explicit assumptions, and (b) show the different ways in which these assumptions can fail, which is necessary for developing further specific designs to test these assumptions and confirm or improve the inference. Using this formulation in the specific problem above, we show that, when the "covariate" (e.g., addiction to smoking) also has genetic determinants, then existing methods, including those previously thought as valid, can declare linkage between the disease and marker loci even when no such linkage exists. We also introduce design strategies to address the problem.

A unifying approach for haplotype analysis of quantitative traits in family-based association studies: Testing and estimating gene-environment interactions with complex exposure variables

We propose robust and e±cient tests and estimators for gene-environment/gene-drug interactions in family-based association studies. The methodology is designed for studies in which haplotypes, quantitative pheno- types and complex exposure/treatment variables are analyzed. Using causal inference methodology, we derive family-based association tests and estimators for the genetic main effects and the interactions. The tests and estimators are robust against population admixture and strati¯cation without requiring adjustment for confounding variables. We illustrate the practical relevance of our approach by an application to a COPD study. The data analysis suggests a gene-environment interaction between a SNP in the Serpine gene and smok- ing status/pack years of smoking that reduces the FEV1 volume by about 0.02 liter per pack year of smoking. Simulation studies show that the pro- posed methodology is su±ciently powered for realistic sample sizes and that it provides valid tests and effect size estimators in the presence of admixture and stratification.